predict_triples
- predict_triples(model: Model, *, triples: None | Tensor | ndarray | tuple[str, str, str] | Sequence[tuple[str, str, str]], triples_factory: CoreTriplesFactory | None = None, batch_size: int | None = None, mode: Literal['training', 'validation', 'testing'] | None = None) ScorePack[source]
Predict on labeled or mapped triples.
- Parameters:
model (Model) – The model.
triples (None | Tensor | ndarray | tuple[str, str, str] | Sequence[tuple[str, str, str]]) –
shape: (num_triples, 3) The triples in one of the following formats:
A single label-based triple.
A list of label-based triples.
An array of label-based triples
An array of ID-based triples.
None. In this case, a triples factory has to be provided, and its triples will be used.
triples_factory (CoreTriplesFactory | None) – The triples factory. Must be given if triples are label-based. If provided and triples are ID-based, add labels to result.
batch_size (int | None) – The batch size to use. Use None to use the largest possible.
mode (Literal['training', 'validation', 'testing'] | None) – The pass mode, which is None in the transductive setting and one of “training”, “validation”, or “testing” in the inductive setting.
- Returns:
a score pack of the triples with the predicted scores.
- Return type: