RankBasedEvaluator

class RankBasedEvaluator(filtered=True, metrics=None, metrics_kwargs=None, add_defaults=True, **kwargs)[source]

Bases: pykeen.evaluation.evaluator.Evaluator

A rank-based evaluator for KGE models.

Initialize rank-based evaluator.

Parameters

  • filtered (bool) – Whether to use the filtered evaluation protocol. If enabled, ranking another true triple higher than the currently considered one will not decrease the score.

  • metrics (Optional[Sequence[Union[str, RankBasedMetric, Type[RankBasedMetric], None]]]) – the rank-based metrics to compute

  • metrics_kwargs (Optional[Mapping[str, Any]]) – additional keyword parameter

  • add_defaults (bool) – whether to add all default metrics besides the ones specified by metrics / metrics_kwargs.

  • kwargs – Additional keyword arguments that are passed to the base class.

Methods Summary

finalize()

Compute the final results, and clear buffers.

process_scores_(hrt_batch, target, scores[, ...])

Process a batch of triples with their computed scores for all entities.

Methods Documentation

finalize()[source]

Compute the final results, and clear buffers.

Return type

RankBasedMetricResults

process_scores_(hrt_batch, target, scores, true_scores=None, dense_positive_mask=None)[source]

Process a batch of triples with their computed scores for all entities.

Parameters

  • hrt_batch (LongTensor) – shape: (batch_size, 3)

  • target (Literal[‘head’, ‘relation’, ‘tail’]) – the prediction target

  • scores (FloatTensor) – shape: (batch_size, num_entities)

  • true_scores (Optional[FloatTensor]) – shape: (batch_size, 1)

  • dense_positive_mask (Optional[FloatTensor]) – shape: (batch_size, num_entities) An optional binary (0/1) tensor indicating other true entities.

Return type

None