How to use

get

method

in

org.lenskit.LenskitRecommender

@Override
public ItemRecommender getItemRecommender() {
  return get(ItemRecommender.class);
}

  @Test
  public void testConfigSeparation() {
    try (LenskitRecommender rec1 = engine.createRecommender(dao);
       LenskitRecommender rec2 = engine.createRecommender(dao)) {

      assertThat(rec1.getItemScorer(),
            not(sameInstance(rec2.getItemScorer())));
      assertThat(rec1.get(SlopeOneModel.class),
            allOf(not(nullValue()),
               sameInstance(rec2.get(SlopeOneModel.class))));
    }
  }
}

@SuppressWarnings("deprecation")
@Test
public void testItemItemRecommenderEngineCreate() {
  try (LenskitRecommender rec = engine.createRecommender()) {
    assertThat(rec.getItemScorer(),
          instanceOf(ItemItemScorer.class));
    assertThat(rec.getRatingPredictor(),
          instanceOf(SimpleRatingPredictor.class));
    assertThat(rec.getItemRecommender(),
          instanceOf(TopNItemRecommender.class));
    assertThat(rec.getItemBasedItemRecommender(),
          instanceOf(TopNItemBasedItemRecommender.class));
    assertThat(rec.get(ItemBasedItemScorer.class),
          instanceOf(ItemItemItemBasedItemScorer.class));
  }
}

@SuppressWarnings("deprecation")
@Test
public void testItemItemRecommenderEngineCreate() {
  try (LenskitRecommender rec = engine.createRecommender(dao)) {
    assertThat(rec.getItemScorer(),
          instanceOf(ItemItemScorer.class));
    assertThat(rec.getRatingPredictor(),
          instanceOf(SimpleRatingPredictor.class));
    assertThat(rec.getItemRecommender(),
          instanceOf(TopNItemRecommender.class));
    assertThat(rec.getItemBasedItemRecommender(),
          instanceOf(TopNItemBasedItemRecommender.class));
    assertThat(rec.get(ItemBasedItemScorer.class),
          instanceOf(ItemItemItemBasedItemScorer.class));
  }
}

  @Test
  public void testConfigSeparation() {
    try(LenskitRecommender rec1 = engine.createRecommender(dao);
      LenskitRecommender rec2 = engine.createRecommender(dao)){

      assertThat(rec1.getItemScorer(),
            not(sameInstance(rec2.getItemScorer())));
      assertThat(rec1.get(ItemItemModel.class),
            allOf(not(nullValue()),
               sameInstance(rec2.get(ItemItemModel.class))));
    }
  }
}

@Override
public ItemScorer getItemScorer() {
  return get(ItemScorer.class);
}

  @Test
  public void testConfigSeparation() {
    try (LenskitRecommender rec1 = engine.createRecommender();
       LenskitRecommender rec2 = engine.createRecommender()) {

      assertThat(rec1.getItemScorer(),
            not(sameInstance(rec2.getItemScorer())));
      assertThat(rec1.get(ItemItemModel.class),
            allOf(not(nullValue()),
               sameInstance(rec2.get(ItemItemModel.class))));
    }
  }
}

@Override
public RatingPredictor getRatingPredictor() {
  return get(RatingPredictor.class);
}

@Test
public void testConfigSeparation() throws RecommenderBuildException {
  LenskitRecommenderEngine engine = makeEngine();
  try (LenskitRecommender rec1 = engine.createRecommender(dao);
     LenskitRecommender rec2 = engine.createRecommender(dao)) {
    assertThat(rec1.getItemScorer(),
        not(sameInstance(rec2.getItemScorer())));
    assertThat(rec1.get(HPFModel.class), sameInstance(rec2.get(HPFModel.class)));
  }
}

@Nullable
@Override
public ItemBasedItemRecommender getItemBasedItemRecommender() {
  return get(ItemBasedItemRecommender.class);
}

  @Test
  public void testConfigSeparation() throws RecommenderBuildException {
    LenskitRecommenderEngine engine = makeEngine();
    try (LenskitRecommender rec1 = engine.createRecommender(dao);
       LenskitRecommender rec2 = engine.createRecommender(dao)) {
      assertThat(rec1.getItemScorer(),
            not(sameInstance(rec2.getItemScorer())));
      assertThat(rec1.get(FunkSVDModel.class),
            sameInstance(rec2.get(FunkSVDModel.class)));
    }
  }
}

@Nullable
@Override
public ItemBasedItemScorer getItemBasedItemScorer() {
  return get(ItemBasedItemScorer.class);
}

/**
 * Get the data access object from this recommender.
 * @return The data access object.
 */
@Nonnull
public DataAccessObject getDataAccessObject() {
  DataAccessObject dao =  get(DataAccessObject.class);
  if (dao == null) {
    throw new IllegalStateException("recommender has no DAO");
  }
  return dao;
}

@Nonnull
@Override
public MetricResult measureUserRecList(Recommender rec, TestUser user, int targetLength, List<Long> recs, Context context) {
  RatingSummary summary = null;
  if (rec instanceof LenskitRecommender) {
    summary = ((LenskitRecommender) rec).get(RatingSummary.class);
  }
  if (recs == null || recs.isEmpty() || summary == null) {
    return MetricResult.empty();
  }
  double pop = 0;
  for (long item: recs) {
    pop += summary.getItemRatingCount(item);
  }
  pop = pop / recs.size();
  context.addUser(pop);
  return new PopResult(pop);
}

@Test
public void testContextRemoved() {
  try (LenskitRecommender rec = engine.createRecommender()) {
    assertThat(rec.get(ItemItemBuildContext.class),
          nullValue());
  }
}

/**
 * Check that we score items but do not provide scores for items
 * the user has previously rated.  User 5 has rated only item 8
 * previously.
 */
@Test
public void testItemScorerNoRating() {
  long[] items = {7, 8};
  ItemItemScorer scorer = session.get(ItemItemScorer.class);
  assertThat(scorer, notNullValue());
  Map<Long, Double> scores = scorer.score(5, LongArrayList.wrap(items));
  assertThat(scores, notNullValue());
  assertThat(scores.size(), equalTo(1));
  assertThat(scores.get(7L), not(notANumber()));
  assertThat(scores.containsKey(8L), equalTo(false));
}

@Test
public void testFeatureInfo() throws RecommenderBuildException {
  LenskitRecommenderEngine engine = makeEngine();
  try (LenskitRecommender rec = engine.createRecommender(dao)) {
    FunkSVDModel model = rec.get(FunkSVDModel.class);
    assertThat(model, notNullValue());
    assertThat(model.getFeatureInfo().size(),
          equalTo(20));
    for (FeatureInfo feat : model.getFeatureInfo()) {
      assertThat(feat.getIterCount(), equalTo(10));
      assertThat(feat.getLastDeltaRMSE(),
            greaterThan(0.0));
    }
  }
}

  @Test
  public void testComputeMeans() {
    EntityFactory efac = new EntityFactory();
    EntityCollectionDAOBuilder daoBuilder = new EntityCollectionDAOBuilder();
    daoBuilder.addEntities(efac.rating(100, 200, 3.0),
                efac.rating(101, 200, 4.0),
                efac.rating(101, 201, 2.5),
                efac.rating(102, 203, 4.5),
                efac.rating(103, 203, 3.5));
    LenskitConfiguration config = new LenskitConfiguration();
    config.addRoot(BiasModel.class);
    config.bind(BiasModel.class).toProvider(ItemAverageRatingBiasModelProvider.class);

    LenskitRecommender rec = LenskitRecommender.build(config, daoBuilder.build());
    BiasModel model = rec.get(BiasModel.class);

    assertThat(model.getIntercept(), closeTo(3.5, 1.0e-3));
    assertThat(model.getItemBias(200), closeTo(0.0, 1.0e-3));
    assertThat(model.getItemBias(201), closeTo(-1.0, 1.0e-3));
    assertThat(model.getItemBias(203), closeTo(0.5, 1.0e-3));
  }
}

@Test
public void testComputeItemMeans() {
  EntityFactory efac = new EntityFactory();
  EntityCollectionDAOBuilder daoBuilder = new EntityCollectionDAOBuilder();
  daoBuilder.addEntities(efac.rating(100, 200, 3.0),
              efac.rating(101, 200, 4.0),
              efac.rating(101, 201, 2.5),
              efac.rating(102, 203, 4.5),
              efac.rating(103, 203, 3.5));
  LenskitConfiguration config = new LenskitConfiguration();
  config.addRoot(BiasModel.class);
  config.bind(BiasModel.class).toProvider(ItemAverageRatingBiasModelProvider.class);
  LenskitRecommender rec = LenskitRecommender.build(config, daoBuilder.build());
  BiasModel model = rec.get(BiasModel.class);
  assertThat(model.getIntercept(), closeTo(3.5, 1.0e-3));
  assertThat(model.getItemBias(200), closeTo(0.0, 1.0e-3));
  assertThat(model.getItemBias(201), closeTo(-1.0, 1.0e-3));
  assertThat(model.getItemBias(203), closeTo(0.5, 1.0e-3));
}

@Test
public void testComputeUserMeans() {
  EntityFactory efac = new EntityFactory();
  EntityCollectionDAOBuilder daoBuilder = new EntityCollectionDAOBuilder();
  daoBuilder.addEntities(efac.rating(100, 200, 3.0),
              efac.rating(101, 200, 4.0),
              efac.rating(102, 201, 2.5),
              efac.rating(102, 203, 4.5),
              efac.rating(101, 203, 3.5));
  LenskitConfiguration config = new LenskitConfiguration();
  config.addRoot(BiasModel.class);
  config.bind(BiasModel.class).toProvider(UserAverageRatingBiasModelProvider.class);
  LenskitRecommender rec = LenskitRecommender.build(config, daoBuilder.build());
  BiasModel model = rec.get(BiasModel.class);
  assertThat(model.getIntercept(), closeTo(3.5, 1.0e-3));
  assertThat(model.getUserBias(100), closeTo(-0.5, 1.0e-3));
  assertThat(model.getUserBias(101), closeTo(0.25, 1.0e-3));
  assertThat(model.getUserBias(102), closeTo(0.0, 1.0e-3));
}