RotatE¶

class RotatE(triples_factory, embedding_dim=200, loss=None, preferred_device=None, random_seed=None, regularizer=None, entity_initializer=<function xavier_uniform_>, relation_initializer=<function init_phases>, relation_constrainer=<function complex_normalize>)[source]¶

Bases: pykeen.models.base.EntityRelationEmbeddingModel

An implementation of RotatE from [sun2019].

RotatE models relations as rotations from head to tail entities in complex space:

\[\textbf{e}_t= \textbf{e}_h \odot \textbf{r}_r\]

where \(\textbf{e}, \textbf{r} \in \mathbb{C}^{d}\) and the complex elements of \(\textbf{r}_r\) are restricted to have a modulus of one (\(\|\textbf{r}_r\| = 1\)). The interaction model is then defined as:

\[f(h,r,t) = -\|\textbf{e}_h \odot \textbf{r}_r - \textbf{e}_t\|\]

which allows to model symmetry, antisymmetry, inversion, and composition.

See also

Constructor of the base class pykeen.models.Model

Attributes Summary

hpo_default

The default strategy for optimizing the model’s hyper-parameters

Methods Summary

`interaction_function`(h, r, t)	Evaluate the interaction function of ComplEx for given embeddings.
`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': 1024, 'low': 32, 'q': 16, 'type': <class 'int'>}}¶: The default strategy for optimizing the model’s hyper-parameters

Methods Documentation

static interaction_function(h, r, t)[source]¶

Evaluate the interaction function of ComplEx for given embeddings.

The embeddings have to be in a broadcastable shape.

WARNING: No forward constraints are applied.

Parameters

h (FloatTensor) – shape: (…, e, 2) Head embeddings. Last dimension corresponds to (real, imag).
r (FloatTensor) – shape: (…, e, 2) Relation embeddings. Last dimension corresponds to (real, imag).
t (FloatTensor) – shape: (…, e, 2) Tail embeddings. Last dimension corresponds to (real, imag).

Return type

FloatTensor

Returns

shape: (…) The scores.

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.