ImplicitALSWrapperModel

class rectools.models.implicit_als.ImplicitALSWrapperModel(model: Union[AlternatingLeastSquares, AlternatingLeastSquares], verbose: int = 0, fit_features_together: bool = False)

Bases: VectorModel[ImplicitALSWrapperModelConfig]

Wrapper for implicit.als.AlternatingLeastSquares with possibility to use explicit features and GPU support.

See https://implicit.readthedocs.io/en/latest/als.html for details of base model.

Parameters

• model (AnyAlternatingLeastSquares) – Base model that will be used.

• verbose (int, default 0) – Degree of verbose output. If 0, no output will be provided.

• fit_features_together (bool, default False) – Whether fit explicit features together with latent features or not. Used only if explicit features are present in dataset. See documentations linked above for details.

Inherited-members

Methods

dumps()	Serialize model to bytes.
fit(dataset, *args, **kwargs)	Fit model.
fit_partial(dataset, *args, **kwargs)	Fit model.
from_config(config)	Create model from config.
get_config([mode, simple_types])	Return model config.
get_params([simple_types, sep])	Return model parameters.
get_vectors()	Return user and item vector representations from fitted model.
load(f)	Load model from file.
loads(data)	Load model from bytes.
recommend(users, dataset, k, filter_viewed)	Recommend items for users.
recommend_to_items(target_items, dataset, k)	Recommend items for target items.
save(f)	Save model to file.

Attributes

i2i_dist
n_threads
recommends_for_cold
recommends_for_warm
u2i_dist

config_class

alias of ImplicitALSWrapperModelConfig

get_vectors() → Tuple[ndarray, ndarray]

Return user and item vector representations from fitted model.

Returns

User and item embeddings. Shapes are (n_users, n_factors) and (n_items, n_factors).

Return type

(np.ndarray, np.ndarray)