Pipeline

pykeen.pipeline.pipeline(*, dataset=None, dataset_kwargs=None, training_triples_factory=None, testing_triples_factory=None, validation_triples_factory=None, model, model_kwargs=None, loss=None, loss_kwargs=None, regularizer=None, regularizer_kwargs=None, optimizer=None, optimizer_kwargs=None, clear_optimizer=True, training_loop=None, negative_sampler=None, negative_sampler_kwargs=None, training_kwargs=None, stopper=None, stopper_kwargs=None, evaluator=None, evaluator_kwargs=None, evaluation_kwargs=None, mlflow_tracking_uri=None, metadata=None, device=None, random_seed=None, use_testing_data=True)[source]

Train and evaluate a model.

Parameters

  • dataset (Union[None, str, Type[DataSet]]) – The name of the dataset (a key from pykeen.datasets.datasets) or the pykeen.datasets.DataSet instance. Alternatively, the training_triples_factory and testing_triples_factory can be specified.

  • dataset_kwargs (Optional[Mapping[str, Any]]) – The keyword arguments passed to the dataset upon instantiation

  • training_triples_factory (Optional[TriplesFactory]) – A triples factory with training instances if a a dataset was not specified

  • testing_triples_factory (Optional[TriplesFactory]) – A triples factory with training instances if a dataset was not specified

  • validation_triples_factory (Optional[TriplesFactory]) – A triples factory with validation instances if a dataset was not specified

  • model (Union[str, Type[Model]]) – The name of the model or the model class

  • model_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to pass to the model class on instantiation

  • loss (Union[None, str, Type[_Loss]]) – The name of the loss or the loss class.

  • loss_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to pass to the loss on instantiation

  • regularizer (Union[None, str, Type[Regularizer]]) – The name of the regularizer or the regularizer class.

  • regularizer_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to pass to the regularizer on instantiation

  • optimizer (Union[None, str, Type[Optimizer]]) – The name of the optimizer or the optimizer class. Defaults to torch.optim.Adagrad.

  • optimizer_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to pass to the optimizer on instantiation

  • clear_optimizer (bool) – Whether to delete the optimizer instance after training. As the optimizer might have additional memory consumption due to e.g. moments in Adam, this is the default option. If you want to continue training, you should set it to False, as the optimizer’s internal parameter will get lost otherwise.

  • training_loop (Union[None, str, Type[TrainingLoop]]) – The name of the training loop’s training approach ('slcwa' or 'lcwa') or the training loop class. Defaults to pykeen.training.SLCWATrainingLoop.

  • negative_sampler (Union[None, str, Type[NegativeSampler]]) – The name of the negative sampler ('basic' or 'bernoulli') or the negative sampler class. Only allowed when training with sLCWA. Defaults to pykeen.sampling.BasicNegativeSampler.

  • negative_sampler_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to pass to the negative sampler class on instantiation

  • training_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to pass to the training loop’s train function on call

  • stopper (Union[None, str, Type[Stopper]]) – What kind of stopping to use. Default to no stopping, can be set to ‘early’.

  • stopper_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to pass to the stopper upon instantiation.

  • evaluator (Union[None, str, Type[Evaluator]]) – The name of the evaluator or an evaluator class. Defaults to pykeen.evaluation.RankBasedEvaluator.

  • evaluator_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to pass to the evaluator on instantiation

  • evaluation_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to pass to the evaluator’s evaluate function on call

  • mlflow_tracking_uri (Optional[str]) – The MLFlow tracking URL. If None is given, MLFlow is not used to track results.

  • metadata (Optional[Dict[str, Any]]) – A JSON dictionary to store with the experiment

  • use_testing_data (bool) – If true, use the testing triples. Otherwise, use the validation triples. Defaults to true - use testing triples.

Return type

PipelineResult

class pykeen.pipeline.PipelineResult(random_seed, model, training_loop, losses, metric_results, train_seconds, evaluate_seconds, stopper=None, metadata=<factory>, version=<factory>, git_hash=<factory>)[source]

A dataclass containing the results of running pykeen.pipeline.pipeline().

evaluate_seconds: float

How long in seconds did evaluation take?

git_hash: str

The git hash of PyKEEN used to create these results

losses: List[float]

The losses during training

metadata: Optional[Mapping[str, Any]]

Any additional metadata as a dictionary

metric_results: pykeen.evaluation.evaluator.MetricResults

The results evaluated by the pipeline

model: pykeen.models.base.Model

The model trained by the pipeline

plot_losses()[source]

Plot the losses per epoch.

random_seed: int

The random seed used at the beginning of the pipeline

save_model(path)[source]

Save the trained model to the given path using torch.save().

The model contains within it the triples factory that was used for training.

Return type

None

save_to_directory(directory, save_metadata=True, save_replicates=True)[source]

Save all artifacts in the given directory.

Return type

None

stopper: Optional[pykeen.stoppers.stopper.Stopper] = None

An early stopper

property title

The title of the experiment.

Return type

Optional[str]

train_seconds: float

How long in seconds did training take?

training_loop: pykeen.training.training_loop.TrainingLoop

The training loop used by the pipeline

version: str

The version of PyKEEN used to create these results