Models¶
An interaction model \(f:\mathcal{E} \times \mathcal{R} \times \mathcal{E} \rightarrow \mathbb{R}\) computes a real-valued score representing the plausibility of a triple \((h,r,t) \in \mathbb{K}\) given the embeddings for the entities and relations. In general, a larger score indicates a higher plausibility. The interpretation of the score value is model-dependent, and usually it cannot be directly interpreted as a probability.
Functions¶
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Look up a model class by name (case/punctuation insensitive) in |
Classes¶
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An implementation of ComplEx [trouillon2016]. |
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An implementation of ComplexLiteral from [agustinus2018] based on the LCWA training approach. |
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An implementation of ConvE from [dettmers2018]. |
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An implementation of ConvKB from [nguyen2018]. |
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An implementation of DistMult from [yang2014]. |
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An implementation of DistMultLiteral from [agustinus2018]. |
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An implementation of ERMLP from [dong2014]. |
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An extension of ERMLP proposed by [sharifzadeh2019]. |
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An implementation of HolE [nickel2016]. |
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An implementation of KG2E from [he2015]. |
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An implementation of NTN from [socher2013]. |
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An implementation of ProjE from [shi2017]. |
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An implementation of RESCAL from [nickel2011]. |
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An implementation of R-GCN from [schlichtkrull2018]. |
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An implementation of RotatE from [sun2019]. |
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An implementation of SimplE [kazemi2018]. |
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An implementation of the Structured Embedding (SE) published by [bordes2011]. |
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An implementation of TransD from [ji2015]. |
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TransE models relations as a translation from head to tail entities in \(\textbf{e}\) [bordes2013]. |
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An implementation of TransH [wang2014]. |
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An implementation of TransR from [lin2015]. |
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An implementation of TuckEr from [balazevic2019]. |
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An implementation of the Unstructured Model (UM) published by [bordes2014]. |
Class Inheritance Diagram¶
Base Classes¶
Base module for all KGE models.
Classes¶
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A base module for all of the KGE models. |
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A base module for most KGE models that have one embedding for entities. |
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A base module for KGE models that have different embeddings for entities and relations. |
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A multimodal KGE model. |
Initialization¶
Embedding weight initialization routines.
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xavier_normal_(tensor, gain=1.0)[source]¶ Initialize weights of the tensor similarly to Glorot/Xavier initialization.
Proceed as if it was a linear layer with fan_in of zero and Xavier normal initialization is used. Fill the weight of input embedding with values values sampled from \(\mathcal{N}(0, a^2)\) where
\[a = \text{gain} \times \sqrt{\frac{2}{\text{embedding_dim}}}\]
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xavier_uniform_(tensor, gain=1.0)[source]¶ Initialize weights of the tensor similarly to Glorot/Xavier initialization.
Proceed as if it was a linear layer with fan_in of zero and Xavier uniform initialization is used, i.e. fill the weight of input embedding with values values sampled from \(\mathcal{U}(-a, a)\) where
\[a = \text{gain} \times \sqrt{\frac{6}{\text{embedding_dim}}}\]- Parameters
tensor – A tensor
gain (
float) – An optional scaling factor, defaults to 1.0.
- Returns
Embedding with weights by the Xavier uniform initializer.
Extra Modules¶
PyKEEN internal “nn” module.
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class
Embedding(num_embeddings, embedding_dim, initializer=None, initializer_kwargs=None, normalizer=None, normalizer_kwargs=None, constrainer=None, constrainer_kwargs=None, trainable=True)[source]¶ Trainable embeddings.
This class provides the same interface as
torch.nn.Embeddingand can be used throughout PyKEEN as a more fully featured drop-in replacement.Instantiate an embedding with extended functionality.
- Parameters
num_embeddings (
int) – >0 The number of embeddings.embedding_dim (
int) – >0 The embedding dimensionality.initializer (
Optional[Callable[[~TensorType], ~TensorType]]) – 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)initializer_kwargs (
Optional[Mapping[str,Any]]) – Additional keyword arguments passed to the initializernormalizer (
Optional[Callable[[~TensorType], ~TensorType]]) – A normalization function, which is applied in every forward pass.normalizer_kwargs (
Optional[Mapping[str,Any]]) – Additional keyword arguments passed to the normalizerconstrainer (
Optional[Callable[[~TensorType], ~TensorType]]) – 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.constrainer_kwargs (
Optional[Mapping[str,Any]]) – Additional keyword arguments passed to the constrainer
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forward(indices=None)[source]¶ Get representations for indices.
- Parameters
indices (
Optional[LongTensor]) – shape: (m,) The indices, or None. If None, return all representations.- Return type
FloatTensor- Returns
shape: (m, d) The representations.
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get_in_canonical_shape(indices=None)[source]¶ Get embedding in canonical shape.
- Parameters
indices (
Optional[LongTensor]) – The indices. If None, return all embeddings.- Return type
FloatTensor- Returns
shape: (batch_size, num_embeddings, d)
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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
Embedding- Returns
The embedding.
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class
RepresentationModule[source]¶ A base class for obtaining representations for entities/relations.
Initializes internal Module state, shared by both nn.Module and ScriptModule.