| Name | torch-rating JSON |
| Version |
0.0.1rc0
JSON |
| download |
| home_page | None |
| Summary | Seamless integration of sports rating systems as layers into pytorch environment |
| upload_time | 2024-12-03 16:12:33 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License Copyright (c) 2023 Jakub Sukdol Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
gnn
sport prediction
rating
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://badge.fury.io/py/torch-rating)
PyTorch based package for incorporating rating systems to neural networks. This package provides model rating layers. The resulting RatingRGNN can be found [here](https://github.com/kubosis/rating_gnn)
### Prerequisities
```
Python >= 3.10
```
### Installation
```commandline
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.
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"description": "\n[](https://badge.fury.io/py/torch-rating)\n\n\n\n\nPyTorch based package for incorporating rating systems to neural networks. This package provides model rating layers. The resulting RatingRGNN can be found [here](https://github.com/kubosis/rating_gnn)\n\n### Prerequisities\n\n```\nPython >= 3.10\n```\n\n### Installation\n\n```commandline\npip install --upgrade pip\npip install torch-rating\n```\n\n### Nera - Neural rating\n\nThis package implements seamless integration of statistical rating systems into graph neural network in the PyTorch environment.\nThis project was developed as my Bachelor's thesis.\n\n### Implemented rating layers and recurrent graph neural network architectures\n\n- Elo rating\n- Berrar rating\n- Pi rating\n\n\n\n\n### Showcases of predictive validation accuracy on collected datasets:\n\nNote: the RatingRGNN was fine-tuned only on the NBL dataset and then applied across the other.\n\n\n\nNote: 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.\n\n\n",
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