Representation

Embedding modules.

class CombinedCompGCNRepresentations(*, triples_factory, embedding_specification, num_layers=1, dims=None, layer_kwargs=None)[source]

A sequence of CompGCN layers.

Initialize the combined entity and relation representation module.

Parameters

triples_factory (CoreTriplesFactory) – The triples factory containing the training triples.
embedding_specification (EmbeddingSpecification) – An embedding specification for the base entity and relation representations.
num_layers (Optional[int]) – The number of message passing layers to use. If None, will be inferred by len(dims), i.e., requires dims to be a sequence / list.
dims (Union[None, int, Sequence[int]]) – The hidden dimensions to use. If None, defaults to the embedding dimension of the base representations. If an integer, is the same for all layers. The last dimension is equal to the output dimension.
layer_kwargs (Optional[Mapping[str, Any]]) – Additional key-word based parameters passed to the individual layers; cf. CompGCNLayer.

forward()[source]

Compute enriched representations.

Return type: Tuple[FloatTensor, FloatTensor]

split()[source]

Return the separated representations.

Return type: Tuple[ForwardRef, ForwardRef]

train(mode=True)[source]

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Args:

mode (bool): whether to set training mode (True) or evaluation: mode (False). Default: True.

Returns:

Module: self

class CompGCNLayer(input_dim, output_dim=None, dropout=0.0, use_bias=True, use_relation_bias=False, composition=None, activation=<class 'torch.nn.modules.linear.Identity'>, activation_kwargs=None, edge_weighting=<class 'pykeen.nn.weighting.SymmetricEdgeWeighting'>)[source]

A single layer of the CompGCN model.

Initialize the module.

Parameters

input_dim (int) – The input dimension.
output_dim (Optional[int]) – The output dimension. If None, equals the input dimension.
dropout (float) – The dropout to use for forward and backward edges.
use_bias (bool) – # TODO: do we really need this? it comes before a mandatory batch norm layer Whether to use bias.
use_relation_bias (bool) – Whether to use a bias for the relation transformation.
composition (Union[str, CompositionModule, None]) – The composition function.
activation (Union[str, Module, None]) – The activation to use.
activation_kwargs (Optional[Mapping[str, Any]]) – Additional key-word based arguments passed to the activation.

forward(x_e, x_r, edge_index, edge_type)[source]

Update entity and relation representations.

\[X_E'[e] = \frac{1}{3} \left( X_E W_s + \left( \sum_{h,r,e \in T} \alpha(h, e) \phi(X_E[h], X_R[r]) W_f \right) + \left( \sum_{e,r,t \in T} \alpha(e, t) \phi(X_E[t], X_R[r^{-1}]) W_b \right) \right)\]

Parameters

x_e (FloatTensor) – shape: (num_entities, input_dim) The entity representations.
x_r (FloatTensor) – shape: (2 * num_relations, input_dim) The relation representations (including inverse relations).
edge_index (LongTensor) – shape: (2, num_edges) The edge index, pairs of source and target entity for each triple.
edge_type (LongTensor) – shape (num_edges,) The edge type, i.e., relation ID, for each triple.

Return type

Tuple[FloatTensor, FloatTensor]

Returns

shape: (num_entities, output_dim) / (2 * num_relations, output_dim) The updated entity and relation representations.

message(x_e, x_r, edge_index, edge_type, weight)[source]

Perform message passing.

Parameters

x_e (FloatTensor) – shape: (num_entities, input_dim) The entity representations.
x_r (FloatTensor) – shape: (2 * num_relations, input_dim) The relation representations (including inverse relations).
edge_index (LongTensor) – shape: (2, num_edges) The edge index, pairs of source and target entity for each triple.
edge_type (LongTensor) – shape (num_edges,) The edge type, i.e., relation ID, for each triple.
weight (Parameter) – The transformation weight.

Return type

FloatTensor

Returns

The updated entity representations.

reset_parameters()[source]: Reset the model’s parameters.

class Embedding(num_embeddings, embedding_dim=None, shape=None, initializer=None, initializer_kwargs=None, normalizer=None, normalizer_kwargs=None, constrainer=None, constrainer_kwargs=None, regularizer=None, regularizer_kwargs=None, trainable=True, dtype=None, dropout=None)[source]

Trainable embeddings.

This class provides the same interface as torch.nn.Embedding and can be used throughout PyKEEN as a more fully featured drop-in replacement.

It extends it by adding additional options for normalizing, constraining, or applying dropout.

When a normalizer is selected, it is applied in every forward pass. It can be used, e.g., to ensure that the embedding vectors are of unit length. A constrainer can be used similarly, but it is applied after each parameter update (using the post_parameter_update hook), i.e., outside of the automatic gradient computation.

The optional dropout can also be used as a regularization technique. Moreover, it enables to obtain uncertainty estimates via techniques such as Monte-Carlo dropout. The following simple example shows how to obtain different scores for a single triple from an (untrained) model. These scores can be considered as samples from a distribution over the scores.

>>> from pykeen.datasets import Nations
>>> dataset = Nations()
>>> from pykeen.nn.emb import EmbeddingSpecification
>>> spec = EmbeddingSpecification(embedding_dim=3, dropout=0.1)
>>> from pykeen.models import ERModel
>>> model = ERModel(
...     triples_factory=dataset.training,
...     interaction='distmult',
...     entity_representations=spec,
...     relation_representations=spec,
... )
>>> import torch
>>> batch = torch.as_tensor(data=[[0, 1, 0]]).repeat(10, 1)
>>> scores = model.score_hrt(batch)

Instantiate an embedding with extended functionality.

Parameters

num_embeddings (int) – >0 The number of embeddings.
embedding_dim (Optional[int]) – >0 The embedding dimensionality.
initializer (Union[str, Callable[[FloatTensor], FloatTensor], None]) –
An optional initializer, which takes an uninitialized (num_embeddings, embedding_dim) tensor as input, and returns an initialized tensor of same shape and dtype (which may be the same, i.e. the initialization may be in-place). Can be passed as a function, or as string corresponding to a key in pykeen.nn.emb.initializers such as:
- "xavier_uniform"
- "xavier_uniform_norm"
- "xavier_normal"
- "xavier_normal_norm"
- "normal"
- "normal_norm"
- "uniform"
- "uniform_norm"
- "init_phases"
initializer_kwargs (Optional[Mapping[str, Any]]) – Additional keyword arguments passed to the initializer
normalizer (Union[str, Callable[[FloatTensor], FloatTensor], None]) – A normalization function, which is applied in every forward pass.
normalizer_kwargs (Optional[Mapping[str, Any]]) – Additional keyword arguments passed to the normalizer
constrainer (Union[str, Callable[[FloatTensor], FloatTensor], None]) –
A function which is applied to the weights after each parameter update, without tracking gradients. It may be used to enforce model constraints outside of gradient-based training. The function does not need to be in-place, but the weight tensor is modified in-place. Can be passed as a function, or as a string corresponding to a key in pykeen.nn.emb.constrainers such as:
- 'normalize'
- 'complex_normalize'
- 'clamp'
- 'clamp_norm'
constrainer_kwargs (Optional[Mapping[str, Any]]) – Additional keyword arguments passed to the constrainer
regularizer (Union[str, Regularizer, None]) – A regularizer, which is applied to the selected embeddings in forward pass
regularizer_kwargs (Optional[Mapping[str, Any]]) – Additional keyword arguments passed to the regularizer
dropout (Optional[float]) – A dropout value for the embeddings.

property embedding_dim: int

The representation dimension.

Return type: int

forward(indices=None)[source]

Get representations for indices.

Parameters: indices (Optional[LongTensor]) – shape: s The indices, or None. If None, this is interpreted as torch.arange(self.max_id) (although implemented more efficiently).
Return type: FloatTensor
Returns: shape: (*s, *self.shape) The representations.

classmethod init_with_device(num_embeddings, embedding_dim, device, initializer=None, initializer_kwargs=None, normalizer=None, normalizer_kwargs=None, constrainer=None, constrainer_kwargs=None)[source]

Create an embedding object on the given device by wrapping __init__().

This method is a hotfix for not being able to pass a device during initialization of torch.nn.Embedding. Instead the weight is always initialized on CPU and has to be moved to GPU afterwards.

Return type: ForwardRef
Returns: The embedding.

property num_embeddings: int

The total number of representations (i.e. the maximum ID).

Return type: int

post_parameter_update()[source]: Apply constraints which should not be included in gradients.

reset_parameters()[source]

Reset the module’s parameters.

Return type: None

class EmbeddingSpecification(embedding_dim=None, shape=None, initializer=None, initializer_kwargs=None, normalizer=None, normalizer_kwargs=None, constrainer=None, constrainer_kwargs=None, regularizer=None, regularizer_kwargs=None, dtype=None, dropout=None)[source]

An embedding specification.

make(*, num_embeddings, device=None)[source]

Create an embedding with this specification.

Return type: Embedding

class LiteralRepresentation(numeric_literals)[source]

Literal representations.

Instantiate an embedding with extended functionality.

Parameters

num_embeddings – >0 The number of embeddings.
embedding_dim – >0 The embedding dimensionality.
initializer –
An optional initializer, which takes an uninitialized (num_embeddings, embedding_dim) tensor as input, and returns an initialized tensor of same shape and dtype (which may be the same, i.e. the initialization may be in-place). Can be passed as a function, or as string corresponding to a key in pykeen.nn.emb.initializers such as:
- "xavier_uniform"
- "xavier_uniform_norm"
- "xavier_normal"
- "xavier_normal_norm"
- "normal"
- "normal_norm"
- "uniform"
- "uniform_norm"
- "init_phases"
initializer_kwargs – Additional keyword arguments passed to the initializer
normalizer – A normalization function, which is applied in every forward pass.
normalizer_kwargs – Additional keyword arguments passed to the normalizer
constrainer –
A function which is applied to the weights after each parameter update, without tracking gradients. It may be used to enforce model constraints outside of gradient-based training. The function does not need to be in-place, but the weight tensor is modified in-place. Can be passed as a function, or as a string corresponding to a key in pykeen.nn.emb.constrainers such as:
- 'normalize'
- 'complex_normalize'
- 'clamp'
- 'clamp_norm'
constrainer_kwargs – Additional keyword arguments passed to the constrainer
regularizer – A regularizer, which is applied to the selected embeddings in forward pass
regularizer_kwargs – Additional keyword arguments passed to the regularizer
dropout – A dropout value for the embeddings.

class RGCNRepresentations(triples_factory, embedding_specification, num_layers=2, use_bias=True, use_batch_norm=False, activation=None, activation_kwargs=None, edge_dropout=0.4, self_loop_dropout=0.2, edge_weighting=None, decomposition=None, decomposition_kwargs=None)[source]

Entity representations enriched by R-GCN.

The GCN employed by the entity encoder is adapted to include typed edges. The forward pass of the GCN is defined by:

\[\textbf{e}_{i}^{l+1} = \sigma \left( \sum_{r \in \mathcal{R}}\sum_{j\in \mathcal{N}_{i}^{r}} \frac{1}{c_{i,r}} \textbf{W}_{r}^{l} \textbf{e}_{j}^{l} + \textbf{W}_{0}^{l} \textbf{e}_{i}^{l}\right)\]

where \(\mathcal{N}_{i}^{r}\) is the set of neighbors of node \(i\) that are connected to \(i\) by relation \(r\), \(c_{i,r}\) is a fixed normalization constant (but it can also be introduced as an additional parameter), and \(\textbf{W}_{r}^{l} \in \mathbb{R}^{d^{(l)} \times d^{(l)}}\) and \(\textbf{W}_{0}^{l} \in \mathbb{R}^{d^{(l)} \times d^{(l)}}\) are weight matrices of the l-th layer of the R-GCN.

The encoder aggregates for each node \(e_i\) the latent representations of its neighbors and its own latent representation \(e_{i}^{l}\) into a new latent representation \(e_{i}^{l+1}\). In contrast to standard GCN, R-GCN defines relation specific transformations \(\textbf{W}_{r}^{l}\) which depend on the type and direction of an edge.

Since having one matrix for each relation introduces a large number of additional parameters, the authors instead propose to use a decomposition, cf. pykeen.nn.message_passing.Decomposition.

Instantiate the R-GCN encoder.

Parameters

triples_factory (CoreTriplesFactory) – The triples factory holding the training triples used for message passing.
embedding_specification (EmbeddingSpecification) – The base embedding specification.
num_layers (int) – The number of layers.
use_bias (bool) – Whether to use a bias.
use_batch_norm (bool) – Whether to use batch normalization.
activation (Union[str, Module, None]) – The activation.
activation_kwargs (Optional[Mapping[str, Any]]) – Additional keyword based arguments passed if the activation is not pre-instantiated. Ignored otherwise.
edge_dropout (float) – The edge dropout to use. Does not apply to self-loops.
self_loop_dropout (float) – The self-loop dropout to use.
edge_weighting (Union[str, EdgeWeighting, None]) – The edge weighting mechanism.
decomposition (Union[str, Decomposition, None]) – The decomposition, cf. pykeen.nn.message_passing.Decomposition.
decomposition_kwargs (Optional[Mapping[str, Any]]) – Additional keyword based arguments passed to the decomposition upon instantiation.

forward(indices=None)[source]

Enrich the entity embeddings of the decoder using R-GCN message propagation.

Return type: FloatTensor

post_parameter_update()[source]

Apply constraints which should not be included in gradients.

Return type: None

reset_parameters()[source]: Reset the module’s parameters.

class RepresentationModule(max_id, shape)[source]

A base class for obtaining representations for entities/relations.

A representation module maps integer IDs to representations, which are tensors of floats.

max_id defines the upper bound of indices we are allowed to request (exclusively). For simple embeddings this is equivalent to num_embeddings, but more a more appropriate word for general non-embedding representations, where the representations could come from somewhere else, e.g. a GNN encoder.

shape describes the shape of a single representation. In case of a vector embedding, this is just a single dimension. For others, e.g. pykeen.models.RESCAL, we have 2-d representations, and in general it can be any fixed shape.

We can look at all representations as a tensor of shape (max_id, *shape), and this is exactly the result of passing indices=None to the forward method.

We can also pass multi-dimensional indices to the forward method, in which case the indices’ shape becomes the prefix of the result shape: (*indices.shape, *self.shape).

Initialize the representation module.

Parameters

max_id (int) – The maximum ID (exclusively). Valid Ids reach from 0, …, max_id-1
shape (Sequence[int]) – The shape of an individual representation.

property embedding_dim: int

Return the “embedding dimension”. Kept for backward compatibility.

Return type: int

abstract forward(indices=None)[source]

Get representations for indices.

Parameters: indices (Optional[LongTensor]) – shape: s The indices, or None. If None, this is interpreted as torch.arange(self.max_id) (although implemented more efficiently).
Return type: FloatTensor
Returns: shape: (*s, *self.shape) The representations.

get_in_canonical_shape(indices=None)[source]

Get representations in canonical shape.

Parameters: indices (Optional[LongTensor]) – None, shape: (b,) or (b, n) The indices. If None, return all representations.
Return type: FloatTensor
Returns: shape: (b?, n?, d) If indices is None, b=1, n=max_id. If indices is 1-dimensional, b=indices.shape[0] and n=1. If indices is 2-dimensional, b, n = indices.shape

get_in_more_canonical_shape(dim, indices=None)[source]

Get representations in canonical shape.

The canonical shape is given as

(batch_size, d_1, d_2, d_3, *)

fulfilling the following properties:

Let i = dim. If indices is None, the return shape is (1, d_1, d_2, d_3) with d_i = num_representations, d_i = 1 else. If indices is not None, then batch_size = indices.shape[0], and d_i = 1 if indices.ndimension() = 1 else d_i = indices.shape[1]

The canonical shape is given by (batch_size, 1, *) if indices is not None, where batch_size=len(indices), or (1, num, *) if indices is None with num equal to the total number of embeddings.

Examples: >>> emb = EmbeddingSpecification(shape=(20,)).make(num_embeddings=10) >>> # Get head representations for given batch indices >>> emb.get_in_more_canonical_shape(dim=”h”, indices=torch.arange(5)).shape (5, 1, 1, 1, 20) >>> # Get head representations for given 2D batch indices, as e.g. used by fast slcwa scoring >>> emb.get_in_more_canonical_shape(dim=”h”, indices=torch.arange(6).view(2, 3)).shape (2, 3, 1, 1, 20) >>> # Get head representations for 1:n scoring >>> emb.get_in_more_canonical_shape(dim=”h”, indices=None).shape (1, 10, 1, 1, 20)

Parameters

dim (Union[int, str]) – The dimension along which to expand for indices=None, or indices.ndimension() == 2.
indices (Optional[LongTensor]) – The indices. Either None, in which care all embeddings are returned, or a 1 or 2 dimensional index tensor.

Return type

FloatTensor

Returns

shape: (batch_size, d1, d2, d3, *self.shape)

max_id: int: the maximum ID (exclusively)

post_parameter_update()[source]: Apply constraints which should not be included in gradients.

reset_parameters()[source]

Reset the module’s parameters.

Return type: None

shape: Tuple[int, ...]: the shape of an individual representation

class SingleCompGCNRepresentation(combined, position=0)[source]

A wrapper around the combined representation module.

Initialize the module.

Parameters

combined (CombinedCompGCNRepresentations) – The combined representations.
position (int) – The position, either 0 for entities, or 1 for relations.

forward(indices=None)[source]

Get representations for indices.

Parameters: indices (Optional[LongTensor]) – shape: s The indices, or None. If None, this is interpreted as torch.arange(self.max_id) (although implemented more efficiently).
Return type: FloatTensor
Returns: shape: (*s, *self.shape) The representations.