pykeen
v1.3.0
Getting Started
Installation
First Steps
Understanding the Evaluation
Toy Example with Translational Distance Models
Using Checkpoints
Optimizing a Model
Running an Ablation Study
Bring Your Own Data
Trackers
Using MLflow
Using Neptune.ai
Using Weights and Biases
Using File-Based Tracking
Novel Link Prediction
Performance Tricks
Extending PyKEEN
Reference
Pipeline
Models
Datasets
Triples
Training
Stoppers
Loss Functions
Regularizers
Result Trackers
Evaluation
Negative Sampling
Hyper-parameter Optimization
Ablation
Lookup
Prediction
Sealant
Constants
pykeen.nn
Utilities
Appendix
References
pykeen
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Trackers
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Trackers
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Trackers
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Using MLflow
Pipeline Example
HPO Example
Reusing Experiments
Adding Tags
Using Neptune.ai
Preparation
Pipeline Example
Reusing Experiments
Adding Tags
Using Weights and Biases
Pipeline Example
HPO Example
Using File-Based Tracking
Minimal Pipeline Example with CSV
Specifying a Name
Combining with
tail
Pipeline Example with JSON
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v: v1.3.0
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