ImplicitRanker

class rectools.models.rank.rank_implicit.ImplicitRanker(distance: Distance, subjects_factors: Union[ndarray, csr_matrix], objects_factors: ndarray, num_threads: int = 0, use_gpu: bool = False)[source]

Bases: object

Ranker model which uses implicit library matrix factorization topk method.

This ranker is suitable for the following cases of scores calculation: 1. subject_embeddings.dot(objects_embeddings) 2. subject_interactions.dot(item-item-similarities)

Parameters

  • distance (Distance) – Distance metric.

  • subjects_factors (np.ndarray | sparse.csr_matrix) – Array of subjects embeddings, shape (n_subjects, n_factors). For item-item similarity models subjects vectors from ui_csr are viewed as factors.

  • objects_factors (np.ndarray) – Array with embeddings of all objects, shape (n_objects, n_factors). For item-item similarity models item similarity vectors are viewed as factors.

  • num_threads (int, default 0) – Will be used as num_threads parameter for implicit.cpu.topk.topk. Omitted if use_gpu is True

  • use_gpu (bool, default False) – If True implicit.gpu.KnnQuery().topk will be used instead of classic cpu version.

Inherited-members

Methods

rank(subject_ids[, k, filter_pairs_csr, ...])

Rank objects to proceed inference using implicit library topk cpu method.
rank(subject_ids: Union[Sequence[int], ndarray], k: Optional[int] = None, filter_pairs_csr: Optional[csr_matrix] = None, sorted_object_whitelist: Optional[ndarray] = None) → Tuple[Union[Sequence[int], ndarray], Union[Sequence[int], ndarray], Union[Sequence[float], ndarray]][source]

Rank objects to proceed inference using implicit library topk cpu method.

Parameters

  • subject_ids (csr_matrix) – Array of ids to recommend for.

  • k (int, optional, default None) – Derived number of recommendations for every subject id.

  • filter_pairs_csr (sparse.csr_matrix, optional, default None) – Subject-object interactions that should be filtered from recommendations. This is relevant for u2i case.

  • sorted_object_whitelist (sparse.csr_matrix, optional, default None) – Whitelist of object ids. If given, only these items will be used for recommendations. Otherwise all items from dataset will be used.

Returns

Array of subject ids, array of recommended items, sorted by score descending and array of scores.

Return type

(InternalIds, InternalIds, Scores)