LightFMWrapperModel

class rectools.models.lightfm.LightFMWrapperModel(model: LightFM, epochs: int = 1, num_threads: int = 1, verbose: int = 0)[source]

Bases: FixedColdRecoModelMixin, VectorModel

Wrapper for lightfm.LightFM.

See https://making.lyst.com/lightfm/docs/home.html for details of base model.

SparseFeatures are used for this model, if you use DenseFeatures, it’ll be converted to sparse. Also it’s usually better to use categorical features. If you have real features (age, price, etc.), you can binarize it.

Parameters

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

  • epochs (int, default 1) – Will be used as epochs parameter for LightFM.fit.

  • num_threads (int, default 1) – Will be used as num_threads parameter for LightFM.fit.

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

Inherited-members

Methods

fit(dataset, *args, **kwargs)

Fit model.

get_vectors(dataset[, add_biases])

Return user and item vector representations from fitted model.

recommend(users, dataset, k, filter_viewed)

Recommend items for users.

recommend_to_items(target_items, dataset, k)

Recommend items for target items.

Attributes

i2i_dist

n_threads

recommends_for_cold

recommends_for_warm

u2i_dist

get_vectors(dataset: Dataset, add_biases: bool = True) → Tuple[ndarray, ndarray][source]

Return user and item vector representations from fitted model.

Parameters

  • dataset (Dataset) – Dataset with input data. Usually it’s the same dataset that was used to fit model.

  • add_biases (bool, default True) – LightFM model stores separately embeddings and biases for users and items. If False, only embeddings will be returned. If True, biases will be added as 2 first columns (see Returns section for details).

Returns

User and item embeddings.

If add_biases is False, shapes are (n_users, no_components) and (n_items, no_components).

If add_biases is True, shapes are (n_users, no_components + 2) and (n_items, no_components + 2). In that case (user_biases_column, ones_column) will be added to user embeddings, and (ones_column, item_biases_column) - to item embeddings. So, if you calculate user_embeddings @ item_embeddings.T, for each user-item pair you will get value user_embedding @ item_embedding + user_bias + item_bias.

Return type

(np.ndarray, np.ndarray)