predict_h_uncertain(model, rt_batch, num_samples=5, slice_size=None, *, mode=None)[source]

Forward pass using left side (head) prediction for obtaining scores of all possible heads.

This method calculates the score for all possible heads for each (relation, tail) pair, as well as an uncertainty quantification.


If the model has been trained with inverse relations, the task of predicting the head entities becomes the task of predicting the tail entities of the inverse triples, i.e., \(f(*,r,t)\) is predicted by means of \(f(t,r_{inv},*)\).

  • model (Model) – the model used for predicting scores

  • rt_batch (LongTensor) – shape: (batch_size, 2) The indices of (relation, tail) pairs.

  • slice_size (Optional[int]) – >0 The divisor for the scoring function when using slicing.

  • num_samples (int) – >1 the number of samples to draw

  • mode (Optional[Literal[‘training’, ‘validation’, ‘testing’]]) – The pass mode, which is None in the transductive setting and one of “training”, “validation”, or “testing” in the inductive setting.

Return type



shape: (batch_size, num_entities) For each r-t pair, the scores for all possible heads.

This function delegates to predict_uncertain_helper() by using pykeen.models.Model.score_h() (or pykeen.models.Model.score_h_inverse() if the model uses inverse triples) as the score_method.


This function sets the model to evaluation mode and all dropout layers to training mode.