Interaction

class Interaction[source]

Bases: Module, Generic[HeadRepresentation, RelationRepresentation, TailRepresentation], ABC

Base class for interaction functions.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Attributes Summary

entity_shape

The symbolic shapes for entity representations

relation_shape

The symbolic shapes for relation representations

tail_entity_shape

Return the symbolic shape for tail entity representations.

value_range

the interaction's value range (for unrestricted input)

Methods Summary

forward(h, r, t)

Compute broadcasted triple scores given broadcasted representations for head, relation and tails.

full_entity_shapes()

Return all entity shapes (head & tail).

get_dimensions()

Get all of the relevant dimension keys.

head_indices()

Return the entity representation indices used for the head representations.

reset_parameters()

Reset parameters the interaction function may have.

score(h, r, t[, slice_size, slice_dim])

Compute broadcasted triple scores with optional slicing.

score_h(all_entities, r, t[, slice_size])

Score all head entities.

score_hrt(h, r, t)

Score a batch of triples.

score_r(h, all_relations, t[, slice_size])

Score all relations.

score_t(h, r, all_entities[, slice_size])

Score all tail entities.

tail_indices()

Return the entity representation indices used for the tail representations.

Attributes Documentation

entity_shape: Sequence[str] = ('d',)

The symbolic shapes for entity representations

relation_shape: Sequence[str] = ('d',)

The symbolic shapes for relation representations

tail_entity_shape

Return the symbolic shape for tail entity representations.

Return type

Sequence[str]

value_range: ClassVar[ValueRange] = ValueRange(lower=None, lower_inclusive=False, upper=None, upper_inclusive=False)

the interaction’s value range (for unrestricted input)

Methods Documentation

abstract forward(h, r, t)[source]

Compute broadcasted triple scores given broadcasted representations for head, relation and tails.

Parameters
  • h (~HeadRepresentation) – shape: (*batch_dims, *dims) The head representations.

  • r (~RelationRepresentation) – shape: (*batch_dims, *dims) The relation representations.

  • t (~TailRepresentation) – shape: (*batch_dims, *dims) The tail representations.

Return type

FloatTensor

Returns

shape: batch_dims The scores.

full_entity_shapes()[source]

Return all entity shapes (head & tail).

Return type

Sequence[str]

classmethod get_dimensions()[source]

Get all of the relevant dimension keys.

This draws from Interaction.entity_shape, Interaction.relation_shape, and in the case of ConvEInteraction, the Interaction.tail_entity_shape.

Return type

Set[str]

Returns

a set of strings representting the dimension keys.

head_indices()[source]

Return the entity representation indices used for the head representations.

Return type

Sequence[int]

reset_parameters()[source]

Reset parameters the interaction function may have.

score(h, r, t, slice_size=None, slice_dim=1)[source]

Compute broadcasted triple scores with optional slicing.

Note

At most one of the slice sizes may be not None.

# TODO: we could change that to slicing along multiple dimensions, if necessary

Parameters
  • h (~HeadRepresentation) – shape: (*batch_dims, *dims) The head representations.

  • r (~RelationRepresentation) – shape: (*batch_dims, *dims) The relation representations.

  • t (~TailRepresentation) – shape: (*batch_dims, *dims) The tail representations.

  • slice_size (Optional[int]) – The slice size.

  • slice_dim (int) – The dimension along which to slice. From {0, …, len(batch_dims)}

Return type

FloatTensor

Returns

shape: batch_dims The scores.

score_h(all_entities, r, t, slice_size=None)[source]

Score all head entities.

Parameters
  • all_entities (~HeadRepresentation) – shape: (num_entities, d_e) The head representations.

  • r (~RelationRepresentation) – shape: (batch_size, d_r) The relation representations.

  • t (~TailRepresentation) – shape: (batch_size, d_e) The tail representations.

  • slice_size (Optional[int]) – The slice size.

Return type

FloatTensor

Returns

shape: (batch_size, num_entities) The scores.

score_hrt(h, r, t)[source]

Score a batch of triples.

Parameters
  • h (~HeadRepresentation) – shape: (batch_size, d_e) The head representations.

  • r (~RelationRepresentation) – shape: (batch_size, d_r) The relation representations.

  • t (~TailRepresentation) – shape: (batch_size, d_e) The tail representations.

Return type

FloatTensor

Returns

shape: (batch_size, 1) The scores.

score_r(h, all_relations, t, slice_size=None)[source]

Score all relations.

Parameters
  • h (~HeadRepresentation) – shape: (batch_size, d_e) The head representations.

  • all_relations (~RelationRepresentation) – shape: (num_relations, d_r) The relation representations.

  • t (~TailRepresentation) – shape: (batch_size, d_e) The tail representations.

  • slice_size (Optional[int]) – The slice size.

Return type

FloatTensor

Returns

shape: (batch_size, num_entities) The scores.

score_t(h, r, all_entities, slice_size=None)[source]

Score all tail entities.

Parameters
  • h (~HeadRepresentation) – shape: (batch_size, d_e) The head representations.

  • r (~RelationRepresentation) – shape: (batch_size, d_r) The relation representations.

  • all_entities (~TailRepresentation) – shape: (num_entities, d_e) The tail representations.

  • slice_size (Optional[int]) – The slice size.

Return type

FloatTensor

Returns

shape: (batch_size, num_entities) The scores.

tail_indices()[source]

Return the entity representation indices used for the tail representations.

Return type

Sequence[int]