Beispiel #1
0
  @Test
  public void test_perfectPrediction_NMAEequals0() {

    // Phase 1: Preparation
    RatingsDataset<? extends Rating> testDataset = new RatingsDatasetMock();
    RelevanceCriteria relevanceCriteria = new RelevanceCriteria(4);
    RecommendationResults recommendationResults = new RecommendationResults();
    for (int idUser : testDataset.allUsers()) {
      try {
        ArrayList<Recommendation> recommendations = new ArrayList<>();
        for (Map.Entry<Integer, ? extends Rating> entry :
            testDataset.getUserRatingsRated(idUser).entrySet()) {
          final int idItem = entry.getKey();
          final Rating rating = entry.getValue();

          final double prediction = rating.getRatingValue().doubleValue();
          recommendations.add(new Recommendation(idItem, prediction));
        }
        Collections.sort(recommendations);
        recommendationResults.add(idUser, recommendations);
      } catch (UserNotFound ex) {
        // Never happens if the dataset is properly implemented
      }
    }
    NRMSE instance = new NRMSE();

    // Phase 2: Execution
    MeasureResult result =
        instance.getMeasureResult(recommendationResults, testDataset, relevanceCriteria);

    // Phase 3: Result checking
    double expResult = 0;
    double delta = 0.001f;
    assertEquals(expResult, result.getValue(), delta);
  }
Beispiel #2
0
  @Test
  public void test_perfectPredictionButOneMovedBy3_NMAEequals0dot433() {

    // Phase 1: Preparation
    RatingsDataset<? extends Rating> testDataset = new RatingsDatasetMock();
    RelevanceCriteria relevanceCriteria = new RelevanceCriteria(4);
    RecommendationResults recommendationResults = new RecommendationResults();

    int idUser = 1;

    ArrayList<Recommendation> recommendations = new ArrayList<>();
    recommendations.add(new Recommendation(10, 4));
    recommendations.add(new Recommendation(12, 5));
    recommendations.add(new Recommendation(13, 2));

    Collections.sort(recommendations);
    recommendationResults.add(idUser, recommendations);

    NRMSE instance = new NRMSE();

    // Phase 2: Execution
    MeasureResult nRMSEResult =
        instance.getMeasureResult(recommendationResults, testDataset, relevanceCriteria);

    // Phase 3: Result checking
    double expResult = 0.4330127;
    double result = nRMSEResult.getValue();
    double delta = 0.001f;
    assertEquals(expResult, result, delta);
  }
Beispiel #3
0
  @Test
  public void test_allMovedTwo_NRMSEequals0point5() {

    // Phase 1: Preparation
    RatingsDataset<? extends Rating> testDataset = new RatingsDatasetMock();

    RelevanceCriteria relevanceCriteria = new RelevanceCriteria(4);
    RecommendationResults recommendationResults = new RecommendationResults();
    int i = 0;
    for (int idUser : testDataset.allUsers()) {
      try {
        ArrayList<Recommendation> recommendations = new ArrayList<>();
        for (Map.Entry<Integer, ? extends Rating> entry :
            testDataset.getUserRatingsRated(idUser).entrySet()) {
          final int idItem = entry.getKey();
          final double ratingValue = entry.getValue().getRatingValue().doubleValue();

          double prediction;
          if (i % 2 == 0) {
            prediction = ratingValue + 2;
            if (prediction > testDataset.getRatingsDomain().max().doubleValue()) {
              prediction = ratingValue - 2;
            }
          } else {
            prediction = ratingValue - 2;
            if (prediction < testDataset.getRatingsDomain().min().doubleValue()) {
              prediction = ratingValue + 2;
            }
          }

          recommendations.add(new Recommendation(idItem, prediction));
          i++;
        }
        Collections.sort(recommendations);
        recommendationResults.add(idUser, recommendations);
      } catch (UserNotFound ex) {
        // Never happens if the dataset is properly implemented
        ERROR_CODES.UNDEFINED_ERROR.exit(ex);
      }
    }
    NRMSE instance = new NRMSE();

    // Phase 2: Execution
    MeasureResult result =
        instance.getMeasureResult(recommendationResults, testDataset, relevanceCriteria);

    // Phase 3: Result checking
    double expResult = 0.5f;
    double delta = 0.001f;
    assertEquals(expResult, result.getValue(), delta);
  }
Beispiel #4
0
  /** Test of getMeasureResult method, of class NRMSE. */
  @Test
  public void test_noRecommendations_NRMSEequalsNaN() {

    // Phase 1: Preparation
    int idUser = 1;
    RatingsDataset<? extends Rating> testDataset = new RatingsDatasetMock();
    RelevanceCriteria relevanceCriteria = new RelevanceCriteria(4);
    RecommendationResults recommendationResults = new RecommendationResults();
    recommendationResults.add(idUser, new ArrayList<>());
    NRMSE instance = new NRMSE();

    // Phase 2: Execution
    MeasureResult result =
        instance.getMeasureResult(recommendationResults, testDataset, relevanceCriteria);

    // Phase 3: Result checking
    double expResult = Double.NaN;
    double delta = 0.001f;
    assertEquals(expResult, result.getValue(), delta);
  }