SimplE¶
-
class
SimplE
(triples_factory, embedding_dim=200, loss=None, preferred_device=None, random_seed=None, regularizer=None, clamp_score=None)[source]¶ Bases:
pykeen.models.base.EntityRelationEmbeddingModel
An implementation of SimplE [kazemi2018].
SimplE is an extension of canonical polyadic (CP), an early tensor factorization approach in which each entity \(e \in \mathcal{E}\) is represented by two vectors \(\textbf{h}_e, \textbf{t}_e \in \mathbb{R}^d\) and each relation by a single vector \(\textbf{r}_r \in \mathbb{R}^d\). Depending whether an entity participates in a triple as the head or tail entity, either \(\textbf{h}\) or \(\textbf{t}\) is used. Both entity representations are learned independently, i.e. observing a triple \((h,r,t)\), the method only updates \(\textbf{h}_h\) and \(\textbf{t}_t\). In contrast to CP, SimplE introduces for each relation \(\textbf{r}_r\) the inverse relation \(\textbf{r'}_r\), and formulates its the interaction model based on both:
\[f(h,r,t) = \frac{1}{2}\left(\left\langle\textbf{h}_h, \textbf{r}_r, \textbf{t}_t\right\rangle + \left\langle\textbf{h}_t, \textbf{r'}_r, \textbf{t}_h\right\rangle\right)\]Therefore, for each triple \((h,r,t) \in \mathbb{K}\), both \(\textbf{h}_h\) and \(\textbf{h}_t\) as well as \(\textbf{t}_h\) and \(\textbf{t}_t\) are updated.
See also
Official implementation: https://github.com/Mehran-k/SimplE
Improved implementation in pytorch: https://github.com/baharefatemi/SimplE
Initialize the entity embedding model.
- Parameters
relation_dim – The relation embedding dimensionality. If not given, defaults to same size as entity embedding dimension.
See also
Constructor of the base class
pykeen.models.Model
See also
Constructor of the base class
pykeen.models.EntityEmbeddingModel
Attributes Summary
The default strategy for optimizing the model’s hyper-parameters
The default parameters for the default loss function class
The power sum settings used by [trouillon2016] for SimplE
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
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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
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loss_default_kwargs
: ClassVar[Mapping[str, Any]] = {}¶ The default parameters for the default loss function class
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regularizer_default_kwargs
: ClassVar[Mapping[str, Any]] = {'normalize': True, 'p': 2.0, 'weight': 20}¶ The power sum settings used by [trouillon2016] for SimplE
Methods Documentation
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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.
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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.
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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.