# GPyConform
[](https://www.python.org/downloads/)
[](https://pypi.org/project/gpyconform)
[](https://anaconda.org/conda-forge/gpyconform)
[](https://github.com/harrisp/gpyconform/blob/master/CHANGELOG.md)
[](https://gpyconform.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/harrisp/gpyconform/blob/master/LICENSE.txt)
[](https://pepy.tech/project/gpyconform)
**GPyConform** extends the [GPyTorch](https://gpytorch.ai) library by implementing (Full) Conformal Prediction for Gaussian Process Regression based on the approach described in [1]. Designed to work seamlessly with Exact Gaussian Process (GP) models, GPyConform enhances GPyTorch by introducing the capability to generate and evaluate both 'symmetric' and 'asymmetric' Conformal Prediction Intervals.
## Key Features
- **Provides Provably Valid Prediction Intervals**: Provides Prediction Intervals with guaranteed coverage under minimal assumptions (data exchangeability).
- **Full Utilization of GPyTorch**: Leverages the robust and efficient GP modeling capabilities of GPyTorch.
- **Supports Both Symmetric and Asymmetric Prediction Intervals**: Implements both the symmetric and asymmetric Full Conformal Prediction approaches for constructing Prediction Intervals.
### Note
Currently, GPyConform is tailored specifically for Exact GP models combined with any covariance function that employs an exact prediction strategy.
## Documentation
For detailed documentation and usage examples, see [GPyConform Documentation](https://gpyconform.readthedocs.io).
## Installation
From [PyPI](https://pypi.org/project/gpyconform/)
```bash
pip install gpyconform
```
From [conda-forge](https://anaconda.org/conda-forge/gpyconform)
```bash
conda install conda-forge::gpyconform
```
## Citing GPyConform
If you use `GPyConform` for a scientific publication, you are kindly requested to cite the following paper:
Harris Papadopoulos. "Guaranteed Coverage Prediction Intervals with Gaussian Process Regression", in *IEEE Transactions on Pattern Analysis and Machine Intelligence*, vol. 46, no. 12, pp. 9072-9083, Dec. 2024. DOI: [10.1109/TPAMI.2024.3418214](https://doi.org/10.1109/TPAMI.2024.3418214).
([arXiv version](https://arxiv.org/abs/2310.15641))
Bibtex entry:
```bibtex
@ARTICLE{gprcp,
author={Papadopoulos, Harris},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Guaranteed Coverage Prediction Intervals with Gaussian Process Regression},
year={2024},
volume={46},
number={12},
pages={9072-9083},
doi={10.1109/TPAMI.2024.3418214}
}
```
## References
<a id="1">[1]</a> Harris Papadopoulos. "Guaranteed Coverage Prediction Intervals with Gaussian Process Regression", in *IEEE Transactions on Pattern Analysis and Machine Intelligence*, vol. 46, no. 12, pp. 9072-9083, Dec. 2024. DOI: [10.1109/TPAMI.2024.3418214](https://doi.org/10.1109/TPAMI.2024.3418214).
([arXiv version](https://arxiv.org/abs/2310.15641))
<a id="2">[2]</a> Vladimir Vovk, Alexander Gammerman, and Glenn Shafer. *Algorithmic Learning in a Random World*, 2nd Ed. Springer, 2023. DOI: [10.1007/978-3-031-06649-8](https://doi.org/10.1007/978-3-031-06649-8).
- - -
Author: Harris Papadopoulos (h.papadopoulos@frederick.ac.cy) /
Copyright 2024 Harris Papadopoulos /
License: BSD 3 clause
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"description": "# GPyConform\r\n\r\n[](https://www.python.org/downloads/)\r\n[](https://pypi.org/project/gpyconform)\r\n[](https://anaconda.org/conda-forge/gpyconform)\r\n[](https://github.com/harrisp/gpyconform/blob/master/CHANGELOG.md)\r\n[](https://gpyconform.readthedocs.io/en/latest/?badge=latest)\r\n[](https://github.com/harrisp/gpyconform/blob/master/LICENSE.txt)\r\n[](https://pepy.tech/project/gpyconform)\r\n\r\n\r\n**GPyConform** extends the [GPyTorch](https://gpytorch.ai) library by implementing (Full) Conformal Prediction for Gaussian Process Regression based on the approach described in [1]. Designed to work seamlessly with Exact Gaussian Process (GP) models, GPyConform enhances GPyTorch by introducing the capability to generate and evaluate both 'symmetric' and 'asymmetric' Conformal Prediction Intervals.\r\n\r\n## Key Features\r\n- **Provides Provably Valid Prediction Intervals**: Provides Prediction Intervals with guaranteed coverage under minimal assumptions (data exchangeability).\r\n- **Full Utilization of GPyTorch**: Leverages the robust and efficient GP modeling capabilities of GPyTorch.\r\n- **Supports Both Symmetric and Asymmetric Prediction Intervals**: Implements both the symmetric and asymmetric Full Conformal Prediction approaches for constructing Prediction Intervals.\r\n\r\n### Note\r\nCurrently, GPyConform is tailored specifically for Exact GP models combined with any covariance function that employs an exact prediction strategy.\r\n\r\n## Documentation\r\n\r\nFor detailed documentation and usage examples, see [GPyConform Documentation](https://gpyconform.readthedocs.io).\r\n\r\n## Installation\r\n\r\nFrom [PyPI](https://pypi.org/project/gpyconform/)\r\n\r\n```bash\r\npip install gpyconform\r\n```\r\n\r\nFrom [conda-forge](https://anaconda.org/conda-forge/gpyconform)\r\n\r\n```bash\r\nconda install conda-forge::gpyconform\r\n```\r\n\r\n## Citing GPyConform\r\n\r\nIf you use `GPyConform` for a scientific publication, you are kindly requested to cite the following paper:\r\n\r\nHarris Papadopoulos. \"Guaranteed Coverage Prediction Intervals with Gaussian Process Regression\", in *IEEE Transactions on Pattern Analysis and Machine Intelligence*, vol. 46, no. 12, pp. 9072-9083, Dec. 2024. DOI: [10.1109/TPAMI.2024.3418214](https://doi.org/10.1109/TPAMI.2024.3418214).\r\n([arXiv version](https://arxiv.org/abs/2310.15641))\r\n\r\nBibtex entry:\r\n\r\n```bibtex\r\n@ARTICLE{gprcp,\r\n author={Papadopoulos, Harris},\r\n journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, \r\n title={Guaranteed Coverage Prediction Intervals with Gaussian Process Regression}, \r\n year={2024},\r\n volume={46},\r\n number={12},\r\n pages={9072-9083},\r\n doi={10.1109/TPAMI.2024.3418214}\r\n}\r\n```\r\n\r\n## References\r\n\r\n<a id=\"1\">[1]</a> Harris Papadopoulos. \"Guaranteed Coverage Prediction Intervals with Gaussian Process Regression\", in *IEEE Transactions on Pattern Analysis and Machine Intelligence*, vol. 46, no. 12, pp. 9072-9083, Dec. 2024. DOI: [10.1109/TPAMI.2024.3418214](https://doi.org/10.1109/TPAMI.2024.3418214). \r\n([arXiv version](https://arxiv.org/abs/2310.15641))\r\n\r\n<a id=\"2\">[2]</a> Vladimir Vovk, Alexander Gammerman, and Glenn Shafer. *Algorithmic Learning in a Random World*, 2nd Ed. Springer, 2023. DOI: [10.1007/978-3-031-06649-8](https://doi.org/10.1007/978-3-031-06649-8).\r\n\r\n\r\n- - -\r\n\r\nAuthor: Harris Papadopoulos (h.papadopoulos@frederick.ac.cy) / \r\nCopyright 2024 Harris Papadopoulos / \r\nLicense: BSD 3 clause\r\n",
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