As an alternative to using a pre-packaged dataset, the training and testing can be set explicitly by file path or with instances of pykeen.triples.TriplesFactory.

## Pre-stratified Dataset¶

You’ve got a training and testing file as 3-column TSV files, all ready to go. You’re sure that there aren’t any entities or relations appearing in the testing set that don’t appear in the training set. Load them in the pipeline like this:

from pykeen.triples import TriplesFactory
from pykeen.pipeline import pipeline

training_path: str = ...
testing_path: str = ...

result = pipeline(
training_triples_factory=training_path,
testing_triples_factory=testing_path,
model='TransE',
)
result.save_to_directory('test_pre_stratified_transe')


PyKEEN will take care of making sure that the entities are mapped from their labels to appropriate integer (technically, 0-dimensional torch.LongTensor) indexes and that the different sets of triples share the same mapping.

This is equally applicable for the pykeen.hpo.hpo_pipeline(), which has a similar interface to the pykeen.pipeline.pipeline() as in:

from pykeen.triples import TriplesFactory
from pykeen.hpo import hpo_pipeline

training_path: str = ...
testing_path: str = ...

result = hpo_pipeline(
n_trials=30,
training_triples_factory=training_path,
testing_triples_factory=testing_path,
model='TransE',
)
result.save_to_directory('test_hpo_pre_stratified_transe')


The remainder of the examples will be for pykeen.pipeline.pipeline(), but all work exactly the same for pykeen.hpo.hpo_pipeline().

If you want to add dataset-wide arguments, you can use the dataset_kwargs argument to the pykeen.pipeline.pipeline to enable options like create_inverse_triples=True.

from pykeen.triples import TriplesFactory
from pykeen.pipeline import pipeline

training_path: str = ...
testing_path: str = ...

result = pipeline(
training_triples_factory=training_path,
testing_triples_factory=testing_path,
dataset_kwargs={'create_inverse_triples': True},
model='TransE',
)
result.save_to_directory('test_pre_stratified_transe')


If you want finer control over how the triples are created, for example, if they are not all coming from TSV files, you can use the pykeen.triples.TriplesFactory interface.

from pykeen.triples import TriplesFactory
from pykeen.pipeline import pipeline

training_path: str = ...
testing_path: str = ...

training = TriplesFactory(path=training_path)
testing = TriplesFactory(
path=testing_path,
entity_to_id=training.entity_to_id,
relation_to_id=training.relation_to_id,
)

result = pipeline(
training_triples_factory=training,
testing_triples_factory=testing,
model='TransE',
)
pipeline_result.save_to_directory('test_pre_stratified_transe')


Warning

The instantiation of the testing factory, we used the entity_to_id and relation_to_id keyword arguments. This is because PyKEEN automatically assigns numeric identifiers to all entities and relations for each triples factory. However, we want the identifiers to be exactly the same for the testing set as the training set, so we just reuse it. If we didn’t have the same identifiers, then the testing set would get mixed up with the wrong identifiers in the training set during evaluation, and we’d get nonsense results.

The dataset_kwargs argument is ignored when passing your own pykeen.triples.TriplesFactory, so be sure to include the create_inverse_triples=True in the instantiation of those classes if that’s your desired behavior as in:

from pykeen.triples import TriplesFactory
from pykeen.pipeline import pipeline

training_path: str = ...
testing_path: str = ...

training = TriplesFactory(
path=training_path,
create_inverse_triples=True,
)
testing = TriplesFactory(
path=testing_path,
entity_to_id=training.entity_to_id,
relation_to_id=training.relation_to_id,
create_inverse_triples=True,
)

result = pipeline(
training_triples_factory=training,
testing_triples_factory=testing,
model='TransE',
)
result.save_to_directory('test_pre_stratified_transe')


Triples factories can also be instantiated using the triples keyword argument instead of the path argument if you already have triples loaded in a numpy.ndarray.

## Unstratified Dataset¶

It’s more realistic your real-world dataset is not already stratified into training and testing sets. PyKEEN has you covered with pykeen.triples.TriplesFactory.split(), which will allow you to create a stratified dataset.

from pykeen.triples import TriplesFactory
from pykeen.pipeline import pipeline

tf = TriplesFactory(path=...)
training, testing = tf.split()

result = pipeline(
training_triples_factory=training,
testing_triples_factory=testing,
model='TransE',
)
pipeline_result.save_to_directory('test_unstratified_transe')


By default, this is an 80/20 split. If you want to use early stopping, you’ll also need a validation set, so you should specify the splits:

from pykeen.triples import TriplesFactory
from pykeen.pipeline import pipeline

tf = TriplesFactory(path=...)
training, testing, validation = tf.split([.8, .1, .1])

result = pipeline(
training_triples_factory=training,
testing_triples_factory=testing,
validation_triples_factory=validation,
model='TransE',
stopper='early',
)
pipeline_result.save_to_directory('test_unstratified_stopped_transe')