dicee.evaluation.link_prediction

Link prediction evaluation functions.

This module provides various functions for evaluating link prediction performance of knowledge graph embedding models.

Functions

evaluate_link_prediction_performance(→ Dict[str, float])	Evaluate link prediction performance with head and tail prediction.
evaluate_link_prediction_performance_with_reciprocals(...)	Evaluate link prediction with reciprocal relations.
evaluate_link_prediction_performance_with_bpe_reciprocals(...)	Evaluate link prediction with BPE encoding and reciprocals.
evaluate_link_prediction_performance_with_bpe(...)	Evaluate link prediction with BPE encoding (head and tail).
evaluate_lp(→ Dict[str, float])	Evaluate link prediction with batched processing.
evaluate_bpe_lp(→ Dict[str, float])	Evaluate link prediction with BPE-encoded entities.
evaluate_lp_bpe_k_vs_all(→ Dict[str, float])	Evaluate BPE link prediction with KvsAll scoring.

Module Contents

dicee.evaluation.link_prediction.evaluate_link_prediction_performance(model, triples, er_vocab: Dict[Tuple, List], re_vocab: Dict[Tuple, List]) → Dict[str, float]

Evaluate link prediction performance with head and tail prediction.

Performs filtered evaluation where known correct answers are filtered out before computing ranks.

Parameters:

• model – KGE model wrapper with entity/relation mappings.

• triples – Test triples as list of (head, relation, tail) strings.

• er_vocab – Mapping (entity, relation) -> list of valid tail entities.

• re_vocab – Mapping (relation, entity) -> list of valid head entities.

Returns:

Dictionary with H@1, H@3, H@10, and MRR metrics.

dicee.evaluation.link_prediction.evaluate_link_prediction_performance_with_reciprocals(model, triples, er_vocab: Dict[Tuple, List]) → Dict[str, float]

Evaluate link prediction with reciprocal relations.

Optimized for models trained with reciprocal triples where only tail prediction is needed.

Parameters:

• model – KGE model wrapper.

• triples – Test triples as list of (head, relation, tail) strings.

• er_vocab – Mapping (entity, relation) -> list of valid tail entities.

Returns:

Dictionary with H@1, H@3, H@10, and MRR metrics.

dicee.evaluation.link_prediction.evaluate_link_prediction_performance_with_bpe_reciprocals(model, within_entities: List[str], triples: List[List[str]], er_vocab: Dict[Tuple, List]) → Dict[str, float]

Evaluate link prediction with BPE encoding and reciprocals.

Parameters:

• model – KGE model wrapper with BPE support.

• within_entities – List of entities to evaluate within.

• triples – Test triples as list of [head, relation, tail] strings.

• er_vocab – Mapping (entity, relation) -> list of valid tail entities.

Returns:

Dictionary with H@1, H@3, H@10, and MRR metrics.

dicee.evaluation.link_prediction.evaluate_link_prediction_performance_with_bpe(model, within_entities: List[str], triples: List[Tuple[str]], er_vocab: Dict[Tuple, List], re_vocab: Dict[Tuple, List]) → Dict[str, float]

Evaluate link prediction with BPE encoding (head and tail).

Parameters:

• model – KGE model wrapper with BPE support.

• within_entities – List of entities to evaluate within.

• triples – Test triples as list of (head, relation, tail) tuples.

• er_vocab – Mapping (entity, relation) -> list of valid tail entities.

• re_vocab – Mapping (relation, entity) -> list of valid head entities.

Returns:

Dictionary with H@1, H@3, H@10, and MRR metrics.

dicee.evaluation.link_prediction.evaluate_lp(model, triple_idx, num_entities: int, er_vocab: Dict[Tuple, List], re_vocab: Dict[Tuple, List], info: str = 'Eval Starts', batch_size: int = 128, chunk_size: int = 1000) → Dict[str, float]

Evaluate link prediction with batched processing.

Memory-efficient evaluation using chunked entity scoring.

Parameters:

• model – The KGE model to evaluate.

• triple_idx – Integer-indexed triples as numpy array.

• num_entities – Total number of entities.

• er_vocab – Mapping (head_idx, rel_idx) -> list of tail indices.

• re_vocab – Mapping (rel_idx, tail_idx) -> list of head indices.

• info – Description to print.

• batch_size – Batch size for triple processing.

• chunk_size – Chunk size for entity scoring.

Returns:

Dictionary with H@1, H@3, H@10, and MRR metrics.

dicee.evaluation.link_prediction.evaluate_bpe_lp(model, triple_idx: List[Tuple], all_bpe_shaped_entities, er_vocab: Dict[Tuple, List], re_vocab: Dict[Tuple, List], info: str = 'Eval Starts') → Dict[str, float]

Evaluate link prediction with BPE-encoded entities.

Parameters:

• model – The KGE model to evaluate.

• triple_idx – List of BPE-encoded triple tuples.

• all_bpe_shaped_entities – All entities with BPE representations.

• er_vocab – Mapping for tail filtering.

• re_vocab – Mapping for head filtering.

• info – Description to print.

Returns:

Dictionary with H@1, H@3, H@10, and MRR metrics.

dicee.evaluation.link_prediction.evaluate_lp_bpe_k_vs_all(model, triples: List[List[str]], er_vocab: Dict = None, batch_size: int = None, func_triple_to_bpe_representation: Callable = None, str_to_bpe_entity_to_idx: Dict = None) → Dict[str, float]

Evaluate BPE link prediction with KvsAll scoring.

Parameters:

• model – The KGE model wrapper.

• triples – List of string triples.

• er_vocab – Entity-relation vocabulary for filtering.

• batch_size – Batch size for processing.

• func_triple_to_bpe_representation – Function to convert triples to BPE.

• str_to_bpe_entity_to_idx – Mapping from string entities to BPE indices.

Returns:

Dictionary with H@1, H@3, H@10, and MRR metrics.