# MarginPairwiseLoss

class MarginPairwiseLoss(margin, margin_activation, reduction='mean')[source]

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 $$f(h,r,t)=\mathbf{e}_h+\mathbf{r}_r-\mathbf{e}_t$$), $$g(x)$$ 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 (Union[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’}.

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.

Methods Documentation

forward(pos_scores, neg_scores)[source]

Compute the margin loss.

The scores have to be in broadcastable shape.

Parameters
• pos_scores (FloatTensor) – The positive scores.

• neg_scores (FloatTensor) – The negative scores.

Return type

FloatTensor

Returns

A scalar loss term.

process_lcwa_scores(predictions, labels, label_smoothing=None, num_entities=None)[source]

Process scores from LCWA training loop.

Parameters
Return type

FloatTensor

Returns

A scalar loss value.

process_slcwa_scores(positive_scores, negative_scores, label_smoothing=None, batch_filter=None, num_entities=None)[source]

Process scores from sLCWA training loop.

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

• negative_scores (FloatTensor) – 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 (Optional[float]) – An optional label smoothing parameter.

• batch_filter (Optional[BoolTensor]) – 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 (Optional[int]) – The number of entities. Only required if label smoothing is enabled.

Return type

FloatTensor

Returns

A scalar loss term.