ERModel

class ERModel(*, triples_factory, interaction, interaction_kwargs=None, entity_representations=None, entity_representations_kwargs=None, relation_representations=None, relation_representations_kwargs=None, skip_checks=False, **kwargs)[source]

Bases: Generic[pykeen.typing.HeadRepresentation, pykeen.typing.RelationRepresentation, pykeen.typing.TailRepresentation], pykeen.models.nbase._NewAbstractModel

A commonly useful base for KGEMs using embeddings and interaction modules.

This model does not use post-init hooks to automatically initialize all of its parameters. Rather, the call to Model.reset_parameters_() happens at the end of ERModel.__init__. This is possible because all trainable parameters should necessarily be passed through the super().__init__() in subclasses of ERModel.

Other code can still be put after the call to super().__init__() in subclasses, such as registering regularizers (as done in pykeen.models.ConvKB and pykeen.models.TransH).

Initialize the module.

Parameters

triples_factory (KGInfo) – The triples factory facilitates access to the dataset.
interaction (Union[str, Interaction[~HeadRepresentation, ~RelationRepresentation, ~TailRepresentation], Type[Interaction[~HeadRepresentation, ~RelationRepresentation, ~TailRepresentation]]]) – The interaction module (e.g., TransE)
interaction_kwargs (Optional[Mapping[str, Any]]) – Additional key-word based parameters given to the interaction module’s constructor, if not already instantiated.
entity_representations (Union[str, Representation, Type[Representation], None, Sequence[Union[str, Representation, Type[Representation], None]]]) – The entity representation or sequence of representations
entity_representations_kwargs (Union[Mapping[str, Any], None, Sequence[Optional[Mapping[str, Any]]]]) – additional keyword-based parameters for instantiation of entity representations
relation_representations (Union[str, Representation, Type[Representation], None, Sequence[Union[str, Representation, Type[Representation], None]]]) – The relation representation or sequence of representations
relation_representations_kwargs (Union[Mapping[str, Any], None, Sequence[Optional[Mapping[str, Any]]]]) – additional keyword-based parameters for instantiation of relation representations
skip_checks (bool) – whether to skip entity representation checks.
kwargs – Keyword arguments to pass to the base model

Methods Summary

`append_weight_regularizer`(parameter, regularizer)	Add a model weight to a regularizer's weight list, and register the regularizer with the model.
`forward`(h_indices, r_indices, t_indices[, ...])	Forward pass.
`score_h`(rt_batch, *[, slice_size, mode])	Forward pass using left side (head) prediction.
`score_hrt`(hrt_batch, *[, mode])	Forward pass.
`score_r`(ht_batch, *[, slice_size, mode])	Forward pass using middle (relation) prediction.
`score_t`(hr_batch, *[, slice_size, mode])	Forward pass using right side (tail) prediction.

Methods Documentation

append_weight_regularizer(parameter, regularizer)[source]

Add a model weight to a regularizer’s weight list, and register the regularizer with the model.

Parameters

parameter (Union[str, Parameter, Iterable[Union[str, Parameter]]]) –

The parameter, either as name, or as nn.Parameter object. A list of available parameter names is shown by
sorted(dict(self.named_parameters()).keys()).
regularizer (Regularizer) – The regularizer instance which will regularize the weights.

Raises

KeyError – If an invalid parameter name was given

Return type

None

forward(h_indices, r_indices, t_indices, slice_size=None, slice_dim=0, *, mode)[source]

Forward pass.

This method takes head, relation and tail indices and calculates the corresponding scores. It supports broadcasting.

Parameters

h_indices (LongTensor) – The head indices.
r_indices (LongTensor) – The relation indices.
t_indices (LongTensor) – The tail indices.
slice_size (Optional[int]) – The slice size.
slice_dim (int) – The dimension along which to slice
mode (Optional[Literal[‘training’, ‘validation’, ‘testing’]]) – The pass mode, which is None in the transductive setting and one of “training”, “validation”, or “testing” in the inductive setting.

Return type

FloatTensor

Returns

The scores

Raises

NotImplementedError – if score repetition becomes necessary

score_h(rt_batch, *, slice_size=None, mode=None)[source]

Forward pass using left side (head) prediction.

This method calculates the score for all possible heads for each (relation, tail) pair.

Parameters

rt_batch (LongTensor) – shape: (batch_size, 2), dtype: long The indices of (relation, tail) pairs.
slice_size (Optional[int]) – The slice size.
mode (Optional[Literal[‘training’, ‘validation’, ‘testing’]]) – The pass mode, which is None in the transductive setting and one of “training”, “validation”, or “testing” in the inductive setting.

Return type

FloatTensor

Returns

shape: (batch_size, num_entities), dtype: float For each r-t pair, the scores for all possible heads.

score_hrt(hrt_batch, *, mode=None)[source]

Forward pass.

This method takes head, relation and tail of each triple and calculates the corresponding score.

Parameters

hrt_batch (LongTensor) – shape: (batch_size, 3), dtype: long The indices of (head, relation, tail) triples.
mode (Optional[Literal[‘training’, ‘validation’, ‘testing’]]) – The pass mode, which is None in the transductive setting and one of “training”, “validation”, or “testing” in the inductive setting.

Return type

FloatTensor

Returns

shape: (batch_size, 1), dtype: float The score for each triple.

score_r(ht_batch, *, slice_size=None, mode=None)[source]

Forward pass using middle (relation) prediction.

This method calculates the score for all possible relations for each (head, tail) pair.

Parameters

ht_batch (LongTensor) – shape: (batch_size, 2), dtype: long The indices of (head, tail) pairs.
slice_size (Optional[int]) – The slice size.
mode (Optional[Literal[‘training’, ‘validation’, ‘testing’]]) – The pass mode, which is None in the transductive setting and one of “training”, “validation”, or “testing” in the inductive setting.

Return type

FloatTensor

Returns

shape: (batch_size, num_relations), dtype: float For each h-t pair, the scores for all possible relations.

score_t(hr_batch, *, slice_size=None, mode=None)[source]

Forward pass using right side (tail) prediction.

This method calculates the score for all possible tails for each (head, relation) pair.

Parameters

hr_batch (LongTensor) – shape: (batch_size, 2), dtype: long The indices of (head, relation) pairs.
slice_size (Optional[int]) – The slice size.
mode (Optional[Literal[‘training’, ‘validation’, ‘testing’]]) – The pass mode, which is None in the transductive setting and one of “training”, “validation”, or “testing” in the inductive setting.

Return type

FloatTensor

Returns

shape: (batch_size, num_entities), dtype: float For each h-r pair, the scores for all possible tails.