MuRE

class MuRE(*, embedding_dim=200, p=2, power_norm=True, entity_initializer=<function normal_>, entity_initializer_kwargs=None, entity_bias_initializer=<function zeros_>, relation_initializer=<function normal_>, relation_initializer_kwargs=None, relation_matrix_initializer=<function uniform_>, relation_matrix_initializer_kwargs=None, **kwargs)

Bases: ERModel

An implementation of MuRE from [balazevic2019b].

Initialize MuRE via the pykeen.nn.modules.MuREInteraction interaction.

Parameters:

• embedding_dim (int) – The entity embedding dimension \(d\). Defaults to 200. Is usually \(d \in [50, 300]\).

• p (int) – The \(l_p\) norm. Defaults to 2.

• power_norm (bool) – Should the power norm be used? Defaults to true.

• entity_initializer (Union[str, Callable[[FloatTensor], FloatTensor], None]) – Entity initializer function. Defaults to torch.nn.init.normal_()

• entity_initializer_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to be used when calling the entity initializer

• entity_bias_initializer (Union[str, Callable[[FloatTensor], FloatTensor], None]) – Entity bias initializer function. Defaults to torch.nn.init.zeros_()

• relation_initializer (Union[str, Callable[[FloatTensor], FloatTensor], None]) – Relation initializer function. Defaults to torch.nn.init.normal_()

• relation_initializer_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to be used when calling the relation initializer

• relation_matrix_initializer (Union[str, Callable[[FloatTensor], FloatTensor], None]) – Relation matrix initializer function. Defaults to torch.nn.init.uniform_()

• relation_matrix_initializer_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to be used when calling the relation matrix initializer

• kwargs – Remaining keyword arguments passed through 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': 256, 'low': 16, 'q': 16, 'type': <class 'int'>}, 'p': {'high': 2, 'low': 1, 'type': <class 'int'>}}

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