ProjE¶

class ProjE(triples_factory, embedding_dim=50, loss=None, preferred_device=None, random_seed=None, inner_non_linearity=None, regularizer=None)[source]¶

Bases: pykeen.models.base.EntityRelationEmbeddingModel

An implementation of ProjE from [shi2017].

ProjE is a neural network-based approach with a combination and a projection layer. The interaction model first combines \(h\) and \(r\) by following combination operator:

\[\textbf{h} \otimes \textbf{r} = \textbf{D}_e \textbf{h} + \textbf{D}_r \textbf{r} + \textbf{b}_c\]

where \(\textbf{D}_e, \textbf{D}_r \in \mathbb{R}^{k \times k}\) are diagonal matrices which are used as shared parameters among all entities and relations, and \(\textbf{b}_c \in \mathbb{R}^{k}\) represents the candidate bias vector shared across all entities. Next, the score for the triple \((h,r,t) \in \mathbb{K}\) is computed:

\[f(h, r, t) = g(\textbf{t} \ z(\textbf{h} \otimes \textbf{r}) + \textbf{b}_p)\]

where \(g\) and \(z\) are activation functions, and \(\textbf{b}_p\) represents the shared projection bias vector.

See also

Constructor of the base class pykeen.models.EntityEmbeddingModel

Attributes Summary

`hpo_default`	The default strategy for optimizing the model’s hyper-parameters
`loss_default_kwargs`	The default parameters for the default loss function class

Methods Summary

`score_h`(rt_batch)	Forward pass using left side (head) prediction.
`score_hrt`(hrt_batch)	Forward pass.
`score_t`(hr_batch)	Forward pass using right side (tail) prediction.

Attributes Documentation

hpo_default: ClassVar[Mapping[str, Any]] = {'embedding_dim': {'high': 256, 'low': 16, 'q': 16, 'type': <class 'int'>}}¶: The default strategy for optimizing the model’s hyper-parameters

loss_default_kwargs: ClassVar[Optional[Mapping[str, Any]]] = {'reduction': 'mean'}¶: The default parameters for the default loss function class

Methods Documentation

score_h(rt_batch)[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.
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)[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.
Raises: NotImplementedError – If the method was not implemented for this class.
Return type: FloatTensor
Returns: shape: (batch_size, 1), dtype: float The score for each triple.

score_t(hr_batch)[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.
Return type: FloatTensor
Returns: shape: (batch_size, num_entities), dtype: float For each h-r pair, the scores for all possible tails.