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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

RatingRGNN architecture

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.

RatingRGNN architecture

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.

RatingRGNN architecture

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)

Source distribution for torch-rating 0.0.2
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torch_rating-0.0.2.tar.gz 623.4 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for torch-rating 0.0.2
File Interpreter ABI Platform
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
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Release files / torch_rating-0.0.2-py3-none-any.whl

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