Python implementation of the conformal prediction framework [1].
Primarily to be used as an extension to the scikit-learn library.
API documentation: http://donlnz.github.io/nonconformist/
(API documentation is currently severely deprecated; for instructions on basic usage, please refer to README.ipynb, and the running examples available under /examples/ in the repository.)
nonconformist requires:
- Python (tested under Python 3.5)
- numpy
- scipy
- scikit-learn
The easiest way to install the latest release version is via pip:
pip install nonconformistThe development version is available here on github:
git clone https://github.com/donlnz/nonconformist- Exchangeability testing [2].
- Interpolated p-values [3,4].
- Conformal prediction trees [5].
- Venn predictors [?]
- Venn-ABERS predictors [?]
- Nonparametric distribution prediction [?]
[1] Vovk, V., Gammerman, A., & Shafer, G. (2005). Algorithmic learning in a random world. Springer Science & Business Media.
[2] Fedorova, V., Gammerman, A., Nouretdinov, I., & Vovk, V. (2012). Plug-in martingales for testing exchangeability on-line. In Proceedings of the 29th International Conference on Machine Learning (ICML-12) (pp. 1639-1646).
[3] Carlsson, L., Ahlberg, E., Boström, H., Johansson, U., Linusson, & H. (2015). Modifications to p-values of Conformal Predictors. In Proceedings of the 3rd International Symposium on Statistical Learning and Data Sciences (SLDS 2015). (In press).
[4] Johansson, U., Ahlberg, E., Boström, H., Carlsson, L., Linusson, H., Sönströd, C. (2015). Handling Small Calibration Sets in Mondrian Inductive Conformal Regressors. In Proceedings of the 3rd International Symposium on Statistical Learning and Data Sciences (SLDS 2015). (In press).
[5] Johansson, U., Sönströd, C., Linusson, H., & Boström, H. (2014, October). Regression trees for streaming data with local performance guarantees. In Big Data (Big Data), 2014 IEEE International Conference on (pp. 461-470). IEEE.