CompGCN

class CompGCN(*, triples_factory, embedding_dim=64, encoder_kwargs=None, interaction=None, interaction_kwargs=None, **kwargs)

Bases: ERModel[FloatTensor, RelationRepresentation, FloatTensor]

An implementation of CompGCN from [vashishth2020].

This model uses graph convolutions, and composition functions.

Initialize the model.

Parameters:

• triples_factory (CoreTriplesFactory) – The triples factory.

• embedding_dim (int) – The embedding dimension to be used if embedding_specification is not given explicitly in encoder_kwargs.

• encoder_kwargs (Optional[Mapping[str, Any]]) – Additional keyword arguments for the encoder, cf. pykeen.nn.representation.CombinedCompGCNRepresentations.

• interaction (Union[str, Interaction[FloatTensor, ~RelationRepresentation, FloatTensor], None]) – The interaction function to use as decoder.

• interaction_kwargs (Optional[Mapping[str, Any]]) – Additional keyword based arguments for the interaction function.

• kwargs – Additional keyword based arguments passed to pykeen.models.ERModel.

Attributes Summary

hpo_default

The default strategy for optimizing the model's hyper-parameters

Attributes Documentation

hpo_default: ClassVar[Mapping[str, Any]] = {'embedding_dim': {'high': 512, 'low': 32, 'q': 32, 'type': <class 'int'>}}

The default strategy for optimizing the model’s hyper-parameters