PyKEEN¶
PyKEEN is a Python package for reproducible, facile knowledge graph embeddings.
The fastest way to get up and running is to use the pykeen.pipeline.pipeline()
function.
It provides a high-level entry into the extensible functionality of
this package. The following example shows how to train and evaluate the
TransE model (pykeen.models.TransE) on the Nations dataset (pykeen.datasets.Nations)
by referring to them by name. By default, the training loop uses the stochastic closed world assumption training
approach
(pykeen.training.SLCWATrainingLoop) and evaluates with rank-based evaluation
(pykeen.evaluation.RankBasedEvaluator).
>>> from pykeen.pipeline import pipeline
>>> result = pipeline(
... model='TransE',
... dataset='Nations',
... )
The results are returned in a pykeen.pipeline.PipelineResult instance, which has
attributes for the trained model, the training loop, and the evaluation.
PyKEEN has a function pykeen.env() that magically prints relevant version information
about PyTorch, CUDA, and your operating system that can be used for debugging.
If you’re in a Jupyter notebook, it will be pretty printed as an HTML table.
>>> import pykeen
>>> pykeen.env()
Getting Started
Bring Your Own
- Bring Your Own Data
- Bring Your Own Interaction
- Implementing your first Interaction Module
- Interactions with Hyper-Parameters
- Interactions with Trainable Parameters
- Interactions with Different Shaped Vectors
- Interactions with Multiple Representations
- Interactions with Different Dimension Vectors
- Differences between
pykeen.nn.modules.Interactionandpykeen.models.Model - Ad hoc Models from Interactions
- Interaction Pipeline
Extending PyKEEN
Reference
Appendix