crepes: a Python Package for Generating Conformal Regressors and Predictive Systems

Henrik Boström
Proceedings of the Eleventh Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 179:24-41, 2022.

Abstract

The recently released Python package crepes can be used to generate both conformal regressors, which transform point predictions into prediction intervals for specified levels of confidence, and conformal predictive systems, which transform the point predictions into cumulative distribution functions (conformal predictive distributions). The \texttt{crepes} package implements standard, normalized and Mondrian conformal regressors and predictive systems, and is completely model-agnostic, using only the residuals for the calibration instances, possibly together with difficulty estimates and Mondrian categories as input, when forming the conformal regressors and predictive systems. This allows the user to easily incorporate and evaluate novel difficulty estimates and ways of forming Mondrian categories, as well as combinations thereof. Examples from using the package are given, illustrating how to incorporate some standard options for difficulty estimation, forming Mondrian categories and the use of out-of-bag predictions for calibration, through helper functions defined in a separate module, called \texttt{crepes.fillings}. The relation to other software packages for conformal regression is also pointed out.

Cite this Paper

BibTeX
@InProceedings{pmlr-v179-bostrom22a, title = {crepes: a Python Package for Generating Conformal Regressors and Predictive Systems}, author = {Bostr\"{o}m, Henrik}, booktitle = {Proceedings of the Eleventh Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {24--41}, year = {2022}, editor = {Johansson, Ulf and Boström, Henrik and An Nguyen, Khuong and Luo, Zhiyuan and Carlsson, Lars}, volume = {179}, series = {Proceedings of Machine Learning Research}, month = {24--26 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v179/bostrom22a/bostrom22a.pdf}, url = {https://proceedings.mlr.press/v179/bostrom22a.html}, abstract = {The recently released Python package crepes can be used to generate both conformal regressors, which transform point predictions into prediction intervals for specified levels of confidence, and conformal predictive systems, which transform the point predictions into cumulative distribution functions (conformal predictive distributions). The \texttt{crepes} package implements standard, normalized and Mondrian conformal regressors and predictive systems, and is completely model-agnostic, using only the residuals for the calibration instances, possibly together with difficulty estimates and Mondrian categories as input, when forming the conformal regressors and predictive systems. This allows the user to easily incorporate and evaluate novel difficulty estimates and ways of forming Mondrian categories, as well as combinations thereof. Examples from using the package are given, illustrating how to incorporate some standard options for difficulty estimation, forming Mondrian categories and the use of out-of-bag predictions for calibration, through helper functions defined in a separate module, called \texttt{crepes.fillings}. The relation to other software packages for conformal regression is also pointed out.} }
Endnote
%0 Conference Paper %T crepes: a Python Package for Generating Conformal Regressors and Predictive Systems %A Henrik Boström %B Proceedings of the Eleventh Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2022 %E Ulf Johansson %E Henrik Boström %E Khuong An Nguyen %E Zhiyuan Luo %E Lars Carlsson %F pmlr-v179-bostrom22a %I PMLR %P 24--41 %U https://proceedings.mlr.press/v179/bostrom22a.html %V 179 %X The recently released Python package crepes can be used to generate both conformal regressors, which transform point predictions into prediction intervals for specified levels of confidence, and conformal predictive systems, which transform the point predictions into cumulative distribution functions (conformal predictive distributions). The \texttt{crepes} package implements standard, normalized and Mondrian conformal regressors and predictive systems, and is completely model-agnostic, using only the residuals for the calibration instances, possibly together with difficulty estimates and Mondrian categories as input, when forming the conformal regressors and predictive systems. This allows the user to easily incorporate and evaluate novel difficulty estimates and ways of forming Mondrian categories, as well as combinations thereof. Examples from using the package are given, illustrating how to incorporate some standard options for difficulty estimation, forming Mondrian categories and the use of out-of-bag predictions for calibration, through helper functions defined in a separate module, called \texttt{crepes.fillings}. The relation to other software packages for conformal regression is also pointed out.
APA
Boström, H.. (2022). crepes: a Python Package for Generating Conformal Regressors and Predictive Systems. Proceedings of the Eleventh Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 179:24-41 Available from https://proceedings.mlr.press/v179/bostrom22a.html.

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