mixle.models.knowledge_graph module¶

Knowledge-graph embedding helpers.

class KnowledgeGraphFitResult(model, history, validation_history=None)[source]

Bases: FitResult[TransEKnowledgeGraphModel]

Result from TransE margin fitting.

Parameters:

model (ModelT)
history (list[float])
validation_history (list[float] | None)

class TransEKnowledgeGraphModel(entity_embeddings, relation_embeddings, entity_names=None, relation_names=None, name=None)[source]

Bases: object

Dependency-free TransE model with a NumPy margin objective.

Parameters:

entity_embeddings (Any)
relation_embeddings (Any)
entity_names (Sequence[Any] | None)
relation_names (Sequence[Any] | None)
name (str | None)

classmethod random(num_entities, num_relations, embedding_dim=16, seed=None, scale=0.01, entity_names=None, relation_names=None, name=None)[source]

Create a randomly initialized model.

Parameters:

num_entities (int)
num_relations (int)
embedding_dim (int)
seed (int | None)
scale (float)
entity_names (Sequence[Any] | None)
relation_names (Sequence[Any] | None)
name (str | None)

Return type:

TransEKnowledgeGraphModel

distance_triples(triples)[source]

Return squared TransE distances ||h + r - t||^2.

Parameters:: triples (Sequence[tuple[Any, Any, Any]])
Return type:: ndarray

score_triples(triples)[source]

Return TransE scores; higher is more plausible.

Parameters:: triples (Sequence[tuple[Any, Any, Any]])
Return type:: ndarray

margin_loss(positive_triples, negative_triples, margin=1.0)[source]

Return the pairwise TransE ranking loss.

Parameters:

positive_triples (Sequence[tuple[Any, Any, Any]])
negative_triples (Sequence[tuple[Any, Any, Any]])
margin (float)

Return type:

float

negative_sample(triples, seed=None, corrupt='tail')[source]

Corrupt heads or tails to produce negative triples.

Parameters:

triples (Sequence[tuple[Any, Any, Any]])
seed (int | None)
corrupt (str)

Return type:

list[tuple[Any, Any, Any]]

fit_margin(positive_triples, negative_triples=None, margin=1.0, lr=0.01, max_its=100, seed=None, normalize_entities=True)[source]

Fit embeddings with simple stochastic subgradient descent.

Parameters:

positive_triples (Sequence[tuple[Any, Any, Any]])
negative_triples (Sequence[tuple[Any, Any, Any]] | None)
margin (float)
lr (float)
max_its (int)
seed (int | None)
normalize_entities (bool)

Return type:

KnowledgeGraphFitResult

normalize_entity_embeddings(max_norm=1.0)[source]

Project entity embeddings into an L2 ball.

Parameters:: max_norm (float)
Return type:: None