ClassificationEvaluator

class ClassificationEvaluator(**kwargs)

Bases: pykeen.evaluation.evaluator.Evaluator

An evaluator that uses a classification metrics.

Initialize the evaluator.

Parameters

• filtered – Should filtered evaluation be performed?

• requires_positive_mask – Does the evaluator need access to the masks?

• batch_size – >0. Evaluation batch size.

• slice_size – >0. The divisor for the scoring function when using slicing

• automatic_memory_optimization – Whether to automatically optimize the sub-batch size during evaluation with regards to the hardware at hand.

Methods Summary

finalize()	Compute the final results, and clear buffers.
process_head_scores_(hrt_batch, true_scores, ...)	Process a batch of triples with their computed head scores for all entities.
process_tail_scores_(hrt_batch, true_scores, ...)	Process a batch of triples with their computed tail scores for all entities.

Methods Documentation

finalize()

Compute the final results, and clear buffers.

Return type

ClassificationMetricResults

process_head_scores_(hrt_batch, true_scores, scores, dense_positive_mask=None)

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

Parameters

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

• true_scores (FloatTensor) – shape: (batch_size)

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

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

Return type

None

process_tail_scores_(hrt_batch, true_scores, scores, dense_positive_mask=None)

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

Parameters

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

• true_scores (FloatTensor) – shape: (batch_size)

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

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

Return type

None