After training, the interaction model (e.g., TransE, ConvE, RotatE) can assign a score to an arbitrary triple, whether it appeared during training, testing, or not. In PyKEEN, each is implemented such that the higher the score (or less negative the score), the more likely a triple is to be true.
However, for most models, these scores do not have obvious statistical interpretations. This has two main consequences:
- The score for a triple from one model can not be compared to the score for that triple from another model
- There is no a priori minimum score for a triple to be labeled as true, so predictions must be given as a prioritization by sorting a set of triples by their respective scores.
After training a model, there are four high-level interfaces for making predictions:
pykeen.models.predict.get_tail_prediction_df
for a given head/relation pairpykeen.models.predict.get_relation_prediction_df
for a given head/tail pairpykeen.models.predict.get_head_prediction_df
for a given relation/tail pairpykeen.models.predict.get_all_prediction_df
for prioritizing linkspykeen.models.predict.predict_triples
for computing scores for explicitly provided triples
Scientifically, pykeen.models.predict.get_all_prediction_df
is the most interesting in a scenario where predictions could be tested and validated experimentally.
This example shows using the pykeen.pipeline.pipeline
to train a model which will already be in memory. Each of the high-level interfaces are exposed through the model:
>>> from pykeen.pipeline import pipeline >>> from pykeen.models import predict >>> # Run the pipeline >>> result = pipeline(dataset='Nations', model='RotatE') >>> # save the model >>> result.save_to_directory('doctests/nations_rotate') >>> model = result.model >>> # Predict tails >>> predicted_tails_df = predict.get_tail_prediction_df( ... model, 'brazil', 'intergovorgs', triples_factory=result.training, ... ) >>> # Predict relations >>> predicted_relations_df = predict.get_relation_prediction_df( ... model, 'brazil', 'uk', triples_factory=result.training, ... ) >>> # Predict heads >>> predicted_heads_df = predict.get_head_prediction_df(model, 'conferences', 'brazil', triples_factory=result.training) >>> # Score all triples (memory intensive) >>> predictions_df = predict.get_all_prediction_df(model, triples_factory=result.training) >>> # Score top K triples >>> top_k_predictions_df = predict.get_all_prediction_df(model, k=150, triples_factory=result.training) >>> # Score a given list of triples >>> score_df = predict.predict_triples_df( ... model=model, ... triples=[('brazil', 'conferences', 'uk'), ('brazil', 'intergovorgs', 'uk')], ... triples_factory=result.training, ... )
This example shows how to reload a previously trained model. The pykeen.pipeline.PipelineResult.save_to_directory
function makes a file named trained_model.pkl
, so we will use the one from the previous example.
>>> import torch >>> from pykeen.datasets import get_dataset >>> model = torch.load('doctests/nations_rotate/trained_model.pkl') >>> training = get_dataset(dataset="nations").training >>> # Predict tails >>> predicted_tails_df = model.get_tail_prediction_df('brazil', 'intergovorgs', triples_factory=training) >>> # everything else is the same as above
There's an example model available at https://github.com/pykeen/pykeen/blob/master/notebooks/hello_world/nations_transe/trained_model.pkl from the "Hello World" notebook for you to try.
The model is trained on its ability to predict the appropriate tail for a given head/relation pair as well as its ability to predict the appropriate head for a given relation/tail pair. This means that while the model can technically predict relations between a given head/tail pair, it must be done with the caveat that it was not trained for this task.