OGBEvaluator¶
- class OGBEvaluator(filtered=False, **kwargs)[source]¶
Bases:
SampledRankBasedEvaluator
A sampled, rank-based evaluator that applies a custom OGB evaluation.
Initialize the evaluator.
- Parameters:
evaluation_factory – the factory with evaluation triples
additional_filter_triples – additional true triples to use for filtering; only relevant if not explicit negatives are given. cf.
pykeen.evaluation.rank_based_evaluator.sample_negatives()
num_negatives – the number of negatives to sample; only relevant if not explicit negatives are given. cf.
pykeen.evaluation.rank_based_evaluator.sample_negatives()
head_negatives – shape: (num_triples, num_negatives) the entity IDs of negative samples for head prediction for each evaluation triple
tail_negatives – shape: (num_triples, num_negatives) the entity IDs of negative samples for tail prediction for each evaluation triple
kwargs – additional keyword-based arguments passed to
pykeen.evaluation.rank_based_evaluator.RankBasedEvaluator.__init__()
- Raises:
ValueError – if only a single side’s negatives are given, or the negatives are in wrong shape
Methods Summary
evaluate
(model, mapped_triples[, ...])Run
evaluate_ogb()
with this evaluator.Methods Documentation
- evaluate(model, mapped_triples, batch_size=None, slice_size=None, device=None, use_tqdm=True, tqdm_kwargs=None, restrict_entities_to=None, restrict_relations_to=None, do_time_consuming_checks=True, additional_filter_triples=None, pre_filtered_triples=True, targets=('head', 'tail'))[source]¶
Run
evaluate_ogb()
with this evaluator.- Return type:
- Parameters:
model (Model) –
mapped_triples (LongTensor) –
batch_size (int | None) –
slice_size (int | None) –
device (device | None) –
use_tqdm (bool) –
restrict_entities_to (Collection[int] | None) –
restrict_relations_to (Collection[int] | None) –
do_time_consuming_checks (bool) –
additional_filter_triples (None | LongTensor | List[LongTensor]) –
pre_filtered_triples (bool) –
targets (Collection[Literal['head', 'relation', 'tail']]) –