lenskit.flexmf.FlexMFImplicitConfig

class lenskit.flexmf.FlexMFImplicitConfig

Bases: lenskit.flexmf._base.FlexMFConfigBase

Configuration for FlexMFImplicitScorer. It inherits base model options from FlexMFConfigBase.

Stability:

Experimental

preset: Literal['bpr', 'warp', 'lightgcn'] | None = None

Select preset defaults to mimic a particular model’s original presentation.

loss: ImplicitLoss = 'logistic'

The loss to use for model training.

negative_strategy: NegativeStrategy | None = None

The negative sampling strategy. The default is "misranked" for WARP loss and "uniform" for other losses.

negative_count: pydantic.PositiveInt = 1

The number of negative items to sample for each positive item in the training data. With BPR loss, the positive item is compared to each negative item; with logistic loss, the positive item is treated once per learning round, so this setting effectively makes the model learn on _n_ negatives per positive, rather than giving positive and negative examples equal weight.

positive_weight: pydantic.PositiveFloat = 1.0

A weighting multiplier to apply to the positive item’s loss, to adjust the relative importance of positive and negative classifications. Only applies to logistic loss.

user_bias: bool | None = None

Whether to learn a user bias term. If unspecified, the default depends on the loss function (False for pairwise and True for logistic).

item_bias: bool = True

Whether to learn an item bias term.

convolution_layers: pydantic.NonNegativeInt = 0

The number of LightGCN convolution layers to use. 0 (the default) configures for standard matrix factorization.

user_embeddings: bool | Literal['prefer'] = True

Whether to use trained user embeddings for scoring. If True, trained embeddings are used when the query does not provide usable query items. If False, trained user embeddings are not used for scoring. If set to "prefer", the trained embedding is used for known users even when the query provides items.

selected_negative_strategy()

Return type:

NegativeStrategy

classmethod apply_preset(data)

check_strategies()