lenskit.graphs.lightgcn.LightGCNConfig#

class lenskit.graphs.lightgcn.LightGCNConfig#

Bases: lenskit.config.common.EmbeddingSizeMixin, pydantic.BaseModel

Configuration for LightGCNScorer.

Stability:: Experimental

embedding_size: pydantic.PositiveInt = 16#: The dimension of the embedding space (number of latent features). Seems to work best as a power of 2.

layer_count: pydantic.PositiveInt = 2#: The number of layers to use.

layer_blend: pydantic.PositiveFloat | list[pydantic.PositiveFloat] | None = None#: The blending coefficient(s) for layer blending. This is equivalent to alpha in LightGCN.

batch_size: pydantic.PositiveInt = 4096#: The training batch size.

learning_rate: pydantic.PositiveFloat = 0.01#: The learning rate for training.

epochs: pydantic.PositiveInt = 10#: The number of training epochs.

regularization: pydantic.PositiveFloat | None = 0.01#: The regularization strength.

loss: Literal['logistic', 'pairwise'] = 'pairwise'#

The loss to use for model training.

pairwise: BPR pairwise ranking loss, using LightGCN.recommend_loss().
logistic: Logistic link prediction loss, using LightGCN.link_pred_loss().

check_layer_blending()#

Return type:: Self