| Name | kernel-quantile-regression JSON |
| Version |
0.0.15
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| home_page | None |
| Summary | Kernel quantile regression |
| upload_time | 2024-08-10 15:10:23 |
| maintainer | None |
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| author | None |
| requires_python | >=3.10 |
| license | None |
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# Kernel quantile regression
The kernel_quantile_regression package is an open source implementation of the quantile regressor technique introduced in [[1]](#1).
This repo contains the code for reproducing the research paper [Probabilistic energy forecasting through quantile regression in reproducing kernel Hilbert spaces](https://arxiv.org/pdf/2408.04405). This article has been published in the ACM SIGEnergy Energy Informatics Review.
Example of kernel quantile regression on the Melbourne temperature data [[2]](#2).
## Installation
Use the package manager [pip](https://pypi.org/project/kernel-quantile-regression/) to install kernel_quantile_regression.
```bash
pip install kernel-quantile-regression
```
## Usage
```python
from kernel_quantile_regression.kqr import KQR
# create model instance
# specify your quantile q and hyperparameters C and gamma
kqr_1=KQR(alpha=q, C=100, gamma=0.5)
# train model
kqr_1.fit(X_train, y_train)
# predict
kqr_1.predict(X_test)
```
## Repo files
- Data/
The Data directory contains the raw files for the GEFCom2014 challenge [[3]](#3), data can be accessed from Dr. Tao Hong blog http://blog.drhongtao.com/2017/03/gefcom2014-load-forecasting-data.html. The Data folder contains also the transformed raw data, those constitute the input for our probabilistic forecasting study.
- plots/
Plots for the tutorial and experiments.
- src/kernel_quantile_regression
Source code.
- train_test
scripts to train the models, saved and test them.
- models
contains , for each quantile, the pickled trained models.
- utils
Utility functions for extracting, loading and transforming raw data of the GEFCom2014 challenge.
- kqr_tutorial.py
Getting started example, where our method is compared against other valid quantile regressors.
## References
<a id="1">[1]</a> Ichiro Takeuchi, Quoc V. Le, Timothy D. Sears, and Alexander J. Smola. 2006. Non-
parametric Quantile Estimation. Journal of Machine Learning Research 7, 45 (2006),
1231–1264. https://www.jmlr.org/papers/volume7/takeuchi06a/takeuchi06a.pdf
<a id="2">[2]</a> Rob J Hyndman, David M Bashtannyk, and Gary K Grunwald. 1996. Estimating and
visualizing conditional densities. Journal of Computational and Graphical Statistics
5, 4 (1996), 315–336. https://www.jstor.org/stable/1390887
<a id="3">[3]</a> Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli, and Rob J.Hyndman. 2016b. Probabilistic energy forecasting: Global Energy Forecasting
Competition 2014 and beyond. International Journal of Forecasting 32, 3 (2016),
896–913. https://www.sciencedirect.com/science/article/abs/pii/S0169207016000133?via%3Dihub
## License
[MIT](https://choosealicense.com/licenses/mit/)
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"description": "# Kernel quantile regression\nThe kernel_quantile_regression package is an open source implementation of the quantile regressor technique introduced in [[1]](#1).\n\nThis repo contains the code for reproducing the research paper [Probabilistic energy forecasting through quantile regression in reproducing kernel Hilbert spaces](https://arxiv.org/pdf/2408.04405). This article has been published in the ACM SIGEnergy Energy Informatics Review.\n\nExample of kernel quantile regression on the Melbourne temperature data [[2]](#2).\n\n\n\n## Installation\nUse the package manager [pip](https://pypi.org/project/kernel-quantile-regression/) to install kernel_quantile_regression.\n\n```bash\npip install kernel-quantile-regression\n```\n\n## Usage\n\n```python\nfrom kernel_quantile_regression.kqr import KQR\n\n# create model instance\n# specify your quantile q and hyperparameters C and gamma\nkqr_1=KQR(alpha=q, C=100, gamma=0.5)\n\n# train model\nkqr_1.fit(X_train, y_train)\n\n# predict\nkqr_1.predict(X_test)\n```\n## Repo files\n- Data/\nThe Data directory contains the raw files for the GEFCom2014 challenge [[3]](#3), data can be accessed from Dr. Tao Hong blog http://blog.drhongtao.com/2017/03/gefcom2014-load-forecasting-data.html. The Data folder contains also the transformed raw data, those constitute the input for our probabilistic forecasting study.\n\n- plots/\nPlots for the tutorial and experiments.\n\n- src/kernel_quantile_regression\nSource code.\n\n- train_test\nscripts to train the models, saved and test them.\n - models\n contains , for each quantile, the pickled trained models.\n\n\n- utils\nUtility functions for extracting, loading and transforming raw data of the GEFCom2014 challenge.\n\n- kqr_tutorial.py\nGetting started example, where our method is compared against other valid quantile regressors.\n\n## References\n<a id=\"1\">[1]</a> Ichiro Takeuchi, Quoc V. Le, Timothy D. Sears, and Alexander J. Smola. 2006. Non-\nparametric Quantile Estimation. Journal of Machine Learning Research 7, 45 (2006),\n1231\u20131264. https://www.jmlr.org/papers/volume7/takeuchi06a/takeuchi06a.pdf\n\n<a id=\"2\">[2]</a> Rob J Hyndman, David M Bashtannyk, and Gary K Grunwald. 1996. Estimating and\nvisualizing conditional densities. Journal of Computational and Graphical Statistics\n5, 4 (1996), 315\u2013336. https://www.jstor.org/stable/1390887\n\n<a id=\"3\">[3]</a> Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli, and Rob J.Hyndman. 2016b. Probabilistic energy forecasting: Global Energy Forecasting\nCompetition 2014 and beyond. International Journal of Forecasting 32, 3 (2016),\n896\u2013913. https://www.sciencedirect.com/science/article/abs/pii/S0169207016000133?via%3Dihub\n\n## License\n[MIT](https://choosealicense.com/licenses/mit/)",
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