- get_prediction_df(model, triples_factory, *, head_label=None, relation_label=None, tail_label=None, targets=None, add_novelties=True, remove_known=False, testing=None, mode=None)
Get predictions for the head, relation, and/or tail combination.
Exactly one of head_label, relation_label and tail_label should be None. This is the position which will be predicted.
Model) – A PyKEEN model
TriplesFactory) – the training triples factory
bool) – should the dataframe include a column denoting if the ranked head entities correspond to novel triples?
bool) – should non-novel triples (those appearing in the training set) be shown with the results? On one hand, this allows you to better assess the goodness of the predictions - you want to see that the non-novel triples generally have higher scores. On the other hand, if you’re doing hypothesis generation, they may pose as a distraction. If this is set to True, then non-novel triples will be removed and the column denoting novelty will be excluded, since all remaining triples will be novel. Defaults to false.
LongTensor]) – the mapped_triples from the testing triples factory (TriplesFactory.mapped_triples)
- Return type
shape: (k, 3) A dataframe with columns based on the settings or a tensor. Contains either the k highest scoring triples, or all possible triples if k is None