MultimodalModel

class MultimodalModel(triples_factory, loss=None, predict_with_sigmoid=False, preferred_device=None, random_seed=None, regularizer=None)

Bases: pykeen.models.base.Model

A multimodal KGE model.

Initialize the module.

Parameters

• triples_factory (TriplesFactory) – The triples factory facilitates access to the dataset.

• loss (Optional[Loss]) – The loss to use. If None is given, use the loss default specific to the model subclass.

• predict_with_sigmoid (bool) – Whether to apply sigmoid onto the scores when predicting scores. Applying sigmoid at prediction time may lead to exactly equal scores for certain triples with very high, or very low score. When not trained with applying sigmoid (or using BCEWithLogitsLoss), the scores are not calibrated to perform well with sigmoid.

• preferred_device (Union[None, str, device]) – The preferred device for model training and inference.

• random_seed (Optional[int]) – A random seed to use for initialising the model’s weights. Should be set when aiming at reproducibility.

• regularizer (Optional[Regularizer]) – A regularizer to use for training.

Methods Summary

score_h(rt_batch)	Forward pass using left side (head) prediction.
score_hrt(hrt_batch)	Forward pass.
score_r(ht_batch)	Forward pass using middle (relation) prediction.
score_t(hr_batch)	Forward pass using right side (tail) prediction.

Methods Documentation

score_h(rt_batch)

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)

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_r(ht_batch)

Forward pass using middle (relation) prediction.

This method calculates the score for all possible relations for each (head, tail) pair.

Parameters

ht_batch (LongTensor) – shape: (batch_size, 2), dtype: long The indices of (head, tail) pairs.

Return type

FloatTensor

Returns

shape: (batch_size, num_relations), dtype: float For each h-t pair, the scores for all possible relations.

score_t(hr_batch)

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.