How to use

org.grouplens.grapht.annotation.DefaultProvider

constructor

/**
 * Abstraction of strategies for iterating over potential neighboring items.  This is used by the
 * item-item model builder to iterate over the potential neighbors of an item.  It is abstracted
 * so that different strategies can be used depending on the properties of the similarity function
 * and data set.
 *
 * @since 2.1
 * @author <a href="http://www.grouplens.org">GroupLens Research</a>
 */
@DefaultProvider(DefaultNeighborIterationStrategyProvider.class)
public interface NeighborIterationStrategy {
  /**
   * Get an iterator over possible neighbors of an item.
   * @param context The build context (to get item &amp; neighbor information).
   * @param item The item ID.  The item may or may not be included in the returned items.
   * @param onlyAfter If {@code true}, only consider item IDs after {@code item}, because
   *                  the caller only needs unique unordered pairs.
   * @return An iterator over possible neighbors of {@code item}.
   */
  LongIterator neighborIterator(ItemItemBuildContext context, long item, boolean onlyAfter);
}

@DefaultProvider(PreferenceDomainQuantizer.AutoProvider.class)
public interface Quantizer {

/**
 * The Poisson Factorization Model. This extends the SVD model.
 *
 * Poisson Factorization models each item as a vector of K latent attributes,
 * and each user as a vector of K latent preferences.
 * Then each rating is modeled as a Poisson distribution,
 * and the rate (mean) of the Poisson distribution is the inner product of
 * corresponding item latent vector and user latent vector.
 */
@DefaultProvider(HPFModelParallelProvider.class)
@Shareable
public final class HPFModel extends MFModel {
  private static final long serialVersionUID = 4L;

  /**
   * Construct a Poisson Factorization Model.
   * @param umat The user feature matrix (users x features).
   * @param imat The item feature matrix (items x features).
   * @param uidx The user index mapping
   * @param iidx The item index mapping
   */
  public HPFModel(RealMatrix umat, RealMatrix imat,
          KeyIndex uidx, KeyIndex iidx) {
    super(umat, imat, uidx, iidx);
  }
}

@DefaultProvider(RandomDataSplitStrategyProvider.class)
@Shareable
public class RandomDataSplitStrategy implements DataSplitStrategy, Serializable {

@DefaultProvider(FunkSVDModelProvider.class)
@Shareable
public final class FunkSVDModel extends MFModel {

/**
 * Bias model that provides global and item biases. The item biases are precomputed and are *not* updated based on
 * new data added since the model builds
 */
@Shareable
@Immutable
@DefaultProvider(ItemAverageRatingBiasModelProvider.class)
public class ItemBiasModel extends UserItemBiasModel {
  private static final long serialVersionUID = 1L;

  /**
   * Construct a new item bias model.
   * @param global The global bias.
   * @param items The item biases.
   */
  public ItemBiasModel(double global, Long2DoubleMap items) {
    super(global, Long2DoubleMaps.EMPTY_MAP, items);
  }
}

/**
 * Bias model that provides global and user biases.  The user biases are precomputed and are *not* refreshed based
 * on user data added since the model build.
 */
@Shareable
@Immutable
@DefaultProvider(UserAverageRatingBiasModelProvider.class)
public class UserBiasModel extends UserItemBiasModel {
  private static final long serialVersionUID = 1L;

  /**
   * Construct a new user bias model.
   * @param global The global bias.
   * @param users The user biases.
   */
  public UserBiasModel(double global, Long2DoubleMap users) {
    super(global, users, Long2DoubleMaps.EMPTY_MAP);
  }
}

@DefaultProvider(RatingSummaryProvider.class)
public class RatingSummary implements Serializable {
  private static final long serialVersionUID = 1L;

@DefaultProvider(SlopeOneModelProvider.class)
@Shareable
public class SlopeOneModel implements Serializable {

@DefaultProvider(ItemItemModelProvider.class)
@Shareable
public class SimilarityMatrixModel implements Serializable, ItemItemModel {

@DefaultProvider(PackedRatingMatrixProvider.class)
@Shareable
public class PackedRatingMatrix implements RatingMatrix {

@DefaultProvider(LeastSquaresItemScorer.Builder.class)
@Shareable
public class LeastSquaresItemScorer extends AbstractItemScorer implements Serializable {

@DefaultProvider(ItemItemBuildContextProvider.class)
@Shareable
@Immutable

@DefaultProvider(GlobalAverageRatingBiasModelProvider.class)
public class GlobalBiasModel implements BiasModel, Serializable {
  private static final long serialVersionUID = 1L;

@DefaultProvider(UserItemAverageRatingBiasModelProvider.class)
public class UserItemBiasModel implements BiasModel, Serializable {
  private static final long serialVersionUID = 1L;

@DefaultProvider(UserSnapshot.Builder.class)
public class UserSnapshot implements Serializable {
  private static final long serialVersionUID = 1L;

@DefaultProvider(InteractionStatistics.ISProvider.class)
public class InteractionStatistics implements Serializable {
  private static final long serialVersionUID = 1L;

/**
 * Abstraction of strategies for iterating over potential neighboring items.  This is used by the
 * item-item model builder to iterate over the potential neighbors of an item.  It is abstracted
 * so that different strategies can be used depending on the properties of the similarity function
 * and data set.
 *
 * @since 2.1
 * @author <a href="http://www.grouplens.org">GroupLens Research</a>
 */
@DefaultProvider(DefaultNeighborIterationStrategyProvider.class)
public interface NeighborIterationStrategy {
  /**
   * Get an iterator over possible neighbors of an item.
   * @param context The build context (to get item &amp; neighbor information).
   * @param item The item ID.  The item may or may not be included in the returned items.
   * @param onlyAfter If {@code true}, only consider item IDs after {@code item}, because
   *                  the caller only needs unique unordered pairs.
   * @return An iterator over possible neighbors of {@code item}.
   */
  LongIterator neighborIterator(ItemItemBuildContext context, long item, boolean onlyAfter);
}

/**
 * Bias model that provides global and user biases.  The user biases are precomputed and are *not* refreshed based
 * on user data added since the model build.
 */
@Shareable
@Immutable
@DefaultProvider(UserAverageRatingBiasModelProvider.class)
public class UserBiasModel extends UserItemBiasModel {
  private static final long serialVersionUID = 1L;

  /**
   * Construct a new user bias model.
   * @param global The global bias.
   * @param users The user biases.
   */
  public UserBiasModel(double global, Long2DoubleMap users) {
    super(global, users, Long2DoubleMaps.EMPTY_MAP);
  }
}

/**
 * Bias model that provides global and item biases. The item biases are precomputed and are *not* updated based on
 * new data added since the model builds
 */
@Shareable
@Immutable
@DefaultProvider(ItemAverageRatingBiasModelProvider.class)
public class ItemBiasModel extends UserItemBiasModel {
  private static final long serialVersionUID = 1L;

  /**
   * Construct a new item bias model.
   * @param global The global bias.
   * @param items The item biases.
   */
  public ItemBiasModel(double global, Long2DoubleMap items) {
    super(global, Long2DoubleMaps.EMPTY_MAP, items);
  }
}