MarginPairwiseLoss

class MarginPairwiseLoss(margin: float = 1.0, margin_activation: str | Module | None = None, reduction: str = 'mean')

Bases: PairwiseLoss

The generalized margin ranking loss.

\[L(k, \bar{k}) = g(f(\bar{k}) - f(k) + \lambda)\]

Where \(k\) are the positive triples, \(\bar{k}\) are the negative triples, \(f\) is the interaction function (e.g., pykeen.models.TransE has \(f(h,r,t)=-||\mathbf{e}_h+\mathbf{e}_r-\mathbf{e}_t||_p\)), \(g(x)\) is an activation function like the ReLU or softmax, and \(\lambda\) is the margin.

Initialize the margin loss instance.

Parameters:

• margin (float) – The margin by which positive and negative scores should be apart.

• margin_activation (str | Module | None) – A margin activation. Defaults to 'relu', i.e. \(h(\Delta) = max(0, \Delta + \lambda)\), which is the default “margin loss”. Using 'softplus' leads to a “soft-margin” formulation as discussed in https://arxiv.org/abs/1703.07737.

• reduction (str) – The name of the reduction operation to aggregate the individual loss values from a batch to a scalar loss value. From {‘mean’, ‘sum’}.

Attributes Summary

hpo_default

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

Methods Summary

forward(pos_scores, neg_scores)	Compute the margin loss.
process_lcwa_scores(predictions, labels[, ...])	Process scores from LCWA training loop.
process_slcwa_scores(positive_scores, ...[, ...])	Process scores from sLCWA training loop.

Attributes Documentation

hpo_default: ClassVar[Mapping[str, Any]] = {'margin': {'high': 3, 'low': 0, 'type': <class 'float'>}, 'margin_activation': {'choices': {'hard', 'relu', 'soft', 'softplus'}, 'type': 'categorical'}}

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

Methods Documentation

forward(pos_scores: Tensor, neg_scores: Tensor) → Tensor

Compute the margin loss.

The scores have to be in broadcastable shape.

Parameters:

• pos_scores (Tensor) – The positive scores.

• neg_scores (Tensor) – The negative scores.

Returns:

A scalar loss term.

Return type:

Tensor

process_lcwa_scores(predictions: Tensor, labels: Tensor, label_smoothing: float | None = None, num_entities: int | None = None) → Tensor

Process scores from LCWA training loop.

Parameters:

• predictions (Tensor) – shape: (batch_size, num_entities) The scores.

• labels (Tensor) – shape: (batch_size, num_entities) The labels.

• label_smoothing (float | None) – An optional label smoothing parameter.

• num_entities (int | None) – The number of entities (required for label-smoothing).

Returns:

A scalar loss value.

Return type:

Tensor

process_slcwa_scores(positive_scores: Tensor, negative_scores: Tensor, label_smoothing: float | None = None, batch_filter: Tensor | None = None, num_entities: int | None = None) → Tensor

Process scores from sLCWA training loop.

Parameters:

• positive_scores (Tensor) – shape: (batch_size, 1) The scores for positive triples.

• negative_scores (Tensor) – shape: (batch_size, num_neg_per_pos) or (num_unfiltered_negatives,) The scores for the negative triples, either in dense 2D shape, or in case they are already filtered, in sparse shape. If they are given in sparse shape, batch_filter needs to be provided, too.

• label_smoothing (float | None) – An optional label smoothing parameter.

• batch_filter (Tensor | None) – shape: (batch_size, num_neg_per_pos) An optional filter of negative scores which were kept. Given if and only if negative_scores have been pre-filtered.

• num_entities (int | None) – The number of entities. Only required if label smoothing is enabled.

Returns:

A scalar loss term.

Return type:

Tensor