consume_scores
- consume_scores(model: Model, dataset: PredictionDataset, *consumers: ScoreConsumer, batch_size: int = 1, mode: Literal['training', 'validation', 'testing'] | None = None) None [source]
Batch-wise calculation of all triple scores and consumption.
From a high-level perspective, this method does the following:
for batch in dataset: scores = model.predict(batch) for consumer in consumers: consumer(batch, scores)
By bringing custom prediction datasets and/or score consumers, this method is highly configurable.
- Parameters:
model (Model) – the model used to calculate scores
dataset (PredictionDataset) – the dataset defining the prediction tasks, i.e., inputs to model.predict to loop over.
consumers (ScoreConsumer) – the consumers of score batches
batch_size (int) – The batch size to use. Will automatically be lowered, if the hardware cannot handle this large batch sizes.
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.
- Raises:
ValueError – if no score consumers are given
- Return type:
None