https://github.com/pawel-czyz/labelshift

Bayesian Quantification with Black-Box Estimators
https://github.com/pawel-czyz/labelshift

data-shift label-shift machine-learning prior-probability-shift quantification

Last synced: 2 months ago
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Bayesian Quantification with Black-Box Estimators

• Host: GitHub
• URL: https://github.com/pawel-czyz/labelshift
• Owner: pawel-czyz
• License: bsd-3-clause
• Created: 2021-05-02T12:16:34.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2024-06-13T11:37:07.000Z (11 months ago)
• Last Synced: 2025-01-18T04:06:37.966Z (4 months ago)
• Topics: data-shift, label-shift, machine-learning, prior-probability-shift, quantification
• Language: Python
• Homepage: https://openreview.net/forum?id=Ft4kHrOawZ
• Size: 85.9 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
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# Bayesian quantification with black-box estimators

*Quantification* is the problem of estimating the label prevalence from an unlabeled data set. In this repository we provide the code associated with our [manuscript](https://openreview.net/forum?id=Ft4kHrOawZ), which can be used to reproduce the experiments.

## Installation

We recommend using [Micromamba](https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html) to set a new Python 3.11 environment.

Then, the package can be installed with:

```bash

$ pip install -e .

```

To reproduce the experiments, install [Snakemake](https://snakemake.readthedocs.io/en/stable/) using the instructions provided. Then, install additional dependencies:

```bash

$ pip install -r requirements.txt

```

The experiments can be reproduced by running:

```bash

$ snakemake -c4 -s workflows/WORKFLOW_NAME.smk

```

## Citation

In order to cite this code, please use:

```

@article{

    Ziegler-Czyz-2024-Bayesian-Quantification,

    title={Bayesian Quantification with Black-Box Estimators},

    author={Albert Ziegler and Pawe{\l} Czy{\.z}},

    journal={Transactions on Machine Learning Research},

    issn={2835-8856},

    year={2024},

    url={https://openreview.net/forum?id=Ft4kHrOawZ}

}

```