FixedModel

class FixedModel(*, triples_factory, **_kwargs)

Bases: pykeen.models.base.Model

A mock model returning fixed scores.

\[score(h, r, t) = h \cdot |\mathcal{E}| \cdot |\mathcal{R}| + r \cdot |\mathcal{E}| + t\]

Initialize the module.

Parameters

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

• loss – The loss to use. If None is given, use the loss default specific to the model subclass.

• predict_with_sigmoid – 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.

• random_seed – A random seed to use for initialising the model’s weights. Should be set when aiming at reproducibility.

Attributes Summary

hpo_default

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

Methods Summary

collect_regularization_term()	Get the regularization term for the loss function.
score_h(rt_batch, **kwargs)	Forward pass using left side (head) prediction.
score_hrt(hrt_batch, **kwargs)	Forward pass.
score_r(ht_batch, **kwargs)	Forward pass using middle (relation) prediction.
score_t(hr_batch, **kwargs)	Forward pass using right side (tail) prediction.

Attributes Documentation

hpo_default: ClassVar[Mapping[str, Any]] = {}

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

Methods Documentation

collect_regularization_term()

Get the regularization term for the loss function.

score_h(rt_batch, **kwargs)

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.

• slice_size – >0 The divisor for the scoring function when using slicing.

• mode – The pass mode, which is None in the transductive setting and one of “training”, “validation”, or “testing” in the inductive setting.

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, **kwargs)

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.

• mode – The pass mode, which is None in the transductive setting and one of “training”, “validation”, or “testing” in the inductive setting.

Return type

FloatTensor

Returns

shape: (batch_size, 1), dtype: float The score for each triple.

score_r(ht_batch, **kwargs)

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.

• slice_size – >0 The divisor for the scoring function when using slicing.

• mode – The pass mode, which is None in the transductive setting and one of “training”, “validation”, or “testing” in the inductive setting.

Return type

FloatTensor

Returns

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

score_t(hr_batch, **kwargs)

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.

• slice_size – >0 The divisor for the scoring function when using slicing.

• mode – The pass mode, which is None in the transductive setting and one of “training”, “validation”, or “testing” in the inductive setting.

Return type

FloatTensor

Returns

shape: (batch_size, num_entities), dtype: float For each h-r pair, the scores for all possible tails.