From aab0ab25e1e6ef9a3347753e9cd29b53e2999d19 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pawe=C5=82=20Czy=C5=BC?= Date: Wed, 1 Mar 2023 23:09:41 +0100 Subject: [PATCH] Add information about the preprint. --- README.md | 21 +++++++++++++++++++-- 1 file changed, 19 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 45c7843..975b049 100644 --- a/README.md +++ b/README.md @@ -2,14 +2,31 @@ # Label Shift -Python library for *label shift* (known as prior probability shift, target shift) and *quantification* (estimating the class prevalences in an unlabeled data set under the prior probability shift assumption). +Python library for *quantification* (estimating the class prevalence in an unlabeled data set) under the prior probability shift assumption. + This module is created with two purposes in mind: - easily apply state-of-the-art quantification algorithms to the real problems, - benchmark novel quantification algorithms against others. It is compatible with any classifier using any machine learning framework. -Contributions are very welcome! Please, check our [Contribution guide](CONTRIBUTING.md). +The code inside was used to run the experiments in [our preprint](https://arxiv.org/abs/2302.09159), which can be cited as: +``` +@misc{https://doi.org/10.48550/arxiv.2302.09159, + doi = {10.48550/ARXIV.2302.09159}, + url = {https://arxiv.org/abs/2302.09159}, + author = {Ziegler, Albert and Czyż, Paweł}, + title = {Bayesian Quantification with Black-Box Estimators}, + publisher = {arXiv}, + year = {2023} +} +``` ## Installation Currently the module is in early development stage and is not ready to be installed. It does not have proper documentation either. We hope to change it soon – thank you for your patience! + +## Contributions +Contributions are very welcome! Please, check our [Contribution guide](CONTRIBUTING.md). + + +