How to use

addRoot

method

in

org.lenskit.LenskitConfiguration

/**
 * Add a root type.
 * @param type The type to add.
 * @see LenskitConfiguration#addRoot(Class)
 */
public void root(Class<?> type) {
  config.addRoot(type);
}

for (EvalTask task: tasks) {
  for (Class<?> cls: task.getRequiredRoots()) {
    config.addRoot(cls);

/**
 * Add a root type.
 * @param type The type to add.
 * @see LenskitConfiguration#addRoot(Class)
 */
public void root(Class<?> type) {
  config.addRoot(type);
}

                     efac.rating(101, 203, 3.5));
LenskitConfiguration config = new LenskitConfiguration();
config.addRoot(BiasModel.class);
config.bind(BiasModel.class).to(LiveUserItemBiasModel.class);

@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));
}

@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 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(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).to(UserBiasModel.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));
  }
}

  @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));
  }
}

@Before
public void createNormalizer() {
  EntityFactory ef = new EntityFactory();
  EntityCollectionDAOBuilder db = new EntityCollectionDAOBuilder();
  /* Set up so that b=3.0, b(u=42) = 0.5, b(u=37) = -0.2, b(i=1) = 0.2, b(i=2) = -0.1 */
  db.addEntities(ef.rating(42, 7, 3.0),
          ef.rating(42, 8, 4.0),
          ef.rating(37, 9, 3.0),
          ef.rating(37, 10, 2.6),
          ef.rating(99, 1, 3.2),
          ef.rating(99, 2, 2.9),
          ef.rating(99, 7, 2),
          ef.rating(99, 8, 3),
          ef.rating(99, 9, 3.4),
          ef.rating(99, 10, 3.0),
          ef.rating(99, 99, 2.9));
  db.deriveEntities(CommonTypes.ITEM,
           CommonTypes.RATING,
           CommonAttributes.ITEM_ID);
  db.deriveEntities(CommonTypes.USER,
           CommonTypes.RATING,
           CommonAttributes.USER_ID);
  LenskitConfiguration config = new LenskitConfiguration();
  config.bind(BaselineScorer.class, ItemScorer.class).to(UserMeanItemScorer.class);
  config.bind(UserMeanBaseline.class, ItemScorer.class).to(ItemMeanRatingItemScorer.class);
  config.bind(UserVectorNormalizer.class).to(BaselineSubtractingUserVectorNormalizer.class);
  config.addRoot(UserVectorNormalizer.class);
  recommender = LenskitRecommender.build(config, db.build());
  normalizer = recommender.get(UserVectorNormalizer.class);
}

  @Test
  public void testComputeAllMeans() {
    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(UserItemAverageRatingBiasModelProvider.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));
    assertThat(model.getUserBias(100), closeTo(-0.5, 1.0e-3));
    assertThat(model.getUserBias(101), closeTo(0, 1.0e-3));
    assertThat(model.getUserBias(102), closeTo(0.25, 1.0e-3));
  }
}

for (EvalTask task: tasks) {
  for (Class<?> cls: task.getRequiredRoots()) {
    config.addRoot(cls);