NSSALoss

class NSSALoss(margin=9.0, adversarial_temperature=1.0, reduction='mean')[source]

Bases: pykeen.losses.SetwiseLoss

An implementation of the self-adversarial negative sampling loss function proposed by [sun2019].

Initialize the NSSA loss.

Parameters

  • margin (float) – The loss’s margin (also written as gamma in the reference paper)

  • adversarial_temperature (float) –

    The negative sampling temperature (also written as alpha in the reference paper)

    Note

    The adversarial temperature is the inverse of the softmax temperature used when computing the weights! Its name is only kept for consistency with the nomenclature of [sun2019].

  • 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’}.

Note

The default hyperparameters are based on the experiments for FB15k-237 in [sun2019].

Attributes Summary

hpo_default

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

synonyms

Methods Summary

forward(pos_scores, neg_scores, neg_weights)

Calculate the loss for the given scores.

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]] = {'adversarial_temperature': {'high': 1.0, 'low': 0.5, 'type': <class 'float'>}, 'margin': {'high': 30, 'low': 3, 'q': 3, 'type': <class 'int'>}}

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

synonyms: ClassVar[Optional[Set[str]]] = {'Negative Sampling Self-Adversarial Loss', 'Self-Adversarial Negative Sampling Loss'}

Methods Documentation

forward(pos_scores, neg_scores, neg_weights)[source]

Calculate the loss for the given scores.

Parameters

  • pos_scores (FloatTensor) – shape: s_p Positive score tensor

  • neg_scores (FloatTensor) – shape: s_n Negative score tensor

  • neg_weights (FloatTensor) – shape: s_n

Return type

FloatTensor

Returns

A loss value

See also

https://github.com/DeepGraphLearning/KnowledgeGraphEmbedding/blob/master/codes/model.py

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

Process scores from LCWA training loop.

Parameters

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

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

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

  • num_entities (Optional[int]) – The number of entities.

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.