PyTorch based package for incorporating rating systems to neural networks. This package provides model rating layers. The resulting RatingRGNN can be found here
Prerequisities
Python >= 3.10
Installation
pip install --upgrade pip
pip install torch-rating
Nera - Neural rating
This package implements seamless integration of statistical rating systems into graph neural network in the PyTorch environment. This project was developed as my Bachelor's thesis.
Implemented rating layers and recurrent graph neural network architectures
- Elo rating
- Berrar rating
- Pi rating
Showcases of predictive validation accuracy on collected datasets:
Note: the RatingRGNN was fine-tuned only on the NBL dataset and then applied across the other.
Note: the accuracy is across time snapshots. These snapshots represent seasons. They do not represents epochs of iterating the whole dataset. The training was done only for one epoch.
Metadata
Release files for torch-rating 0.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| torch_rating-0.0.2.tar.gz | 623.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| torch_rating-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.2 MB
Release files / torch_rating-0.0.2.tar.gz
| Download URL | torch_rating-0.0.2.tar.gz |
|---|---|
| Size | 623.4 kB |
| Tags | Source |
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Release files / torch_rating-0.0.2-py3-none-any.whl
| Download URL | torch_rating-0.0.2-py3-none-any.whl |
|---|---|
| Size | 623.8 kB |
| Tags | Python 3 |
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twine/6.0.1 CPython/3.11.10
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