FixedModel

class FixedModel(*, triples_factory: KGInfo, **_kwargs)

Bases: 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 model.

Parameters:

• triples_factory (KGInfo) – the (training) triples factory

• _kwargs – ignored keyword-based parameters

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[, heads])	Forward pass using left side (head) prediction.
score_hrt(hrt_batch, **kwargs)	Forward pass.
score_r(ht_batch[, relations])	Forward pass using middle (relation) prediction.
score_t(hr_batch[, tails])	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: Tensor, heads: Tensor | None = None, **kwargs) → Tensor

Forward pass using left side (head) prediction.

This method calculates the score for all possible heads for each (relation, tail) pair.

Parameters:

• rt_batch (Tensor) – 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.

• heads (Tensor | None) – shape: (num_heads,) | (batch_size, num_heads) head entity indices to score against. If None, scores against all entities (from the given mode).

Returns:

shape: (batch_size, num_heads), dtype: float For each r-t pair, the scores for all possible heads.

Return type:

Tensor

score_hrt(hrt_batch: Tensor, **kwargs) → Tensor

Forward pass.

This method takes head, relation and tail of each triple and calculates the corresponding score.

Parameters:

• hrt_batch (Tensor) – 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.

Returns:

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

Return type:

Tensor

score_r(ht_batch: Tensor, relations: Tensor | None = None, **kwargs) → Tensor

Forward pass using middle (relation) prediction.

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

Parameters:

• ht_batch (Tensor) – 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.

• relations (Tensor | None) – shape: (num_relations,) | (batch_size, num_relations) relation indices to score against. If None, scores against all relations (from the given mode).

Returns:

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

Return type:

Tensor

score_t(hr_batch: Tensor, tails: Tensor | None = None, **kwargs) → Tensor

Forward pass using right side (tail) prediction.

This method calculates the score for all possible tails for each (head, relation) pair.

Parameters:

• hr_batch (Tensor) – 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.

• tails (Tensor | None) – shape: (num_tails,) | (batch_size, num_tails) tail entity indices to score against. If None, scores against all entities (from the given mode).

Returns:

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

Return type:

Tensor