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.
The code inside was used to run the experiments in our preprint, 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}
}
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 are very welcome! Please, check our Contribution guide.