pykeen

Getting Started

Installation
First Steps
Knowledge Graph Embedding Models
Representations
Interaction Functions
Tracking Results during Training
Saving Checkpoints during Training
A Toy Example with Translational Distance Models
Understanding the Evaluation
Optimizing a Model’s Hyper-parameters
Running an Ablation Study
Performance Tricks
Getting Started with NodePiece
Inductive Link Prediction
Splitting
PyTorch Lightning Integration
Using Resolvers
Normalizer, Constrainer & Regularizer
Troubleshooting

Bring Your Own

Bring Your Own Data
Bring Your Own Interaction

Extending PyKEEN

Extending the Datasets
Extending the Models

Reference

Pipeline
Models
Datasets
Inductive Datasets
Entity Alignment
Triples
Triples Workflows
Training
Stoppers
Loss Functions
Loss Weighting
Regularizers
Result Trackers
Negative Sampling
Filtering
Optimizers
Evaluation
Metrics
Hyper-parameter Optimization
Ablation
Prediction
Uncertainty
Sealant
Constants
Flexible Weight Checkpoints
pykeen.nn
Utilities

Appendix

Analysis
References

pykeen

Loss Weighting
loss_weighter_resolver
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loss_weighter_resolver

loss_weighter_resolver: ClassResolver[LossWeighter] = <class_resolver.api.ClassResolver object>: A resolver for loss weighters

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