condo-adapter

ConDo Adapter performs Confounded Domain Adaptation, which corrects for batch effects while conditioning on confounding variables. We hope it sparks joy as you clean up your data!

Installation

Installation from pip

You can install the toolbox through PyPI with:

pip install condo

Note: If you have issues with importing torchmin, you may need to install from source, as shown below. Or you can try re-installing pytorch-minimize from source.

Installation from source

After cloning this repo, install the dependencies on the command-line via:

pip install -r requirements.txt

In this directory, run

pip install -e .

Usage

Import ConDo and create the adapter:

from condo import ConDoAdapterKLD
condoer = ConDoAdapterKLD()

Try using it:

import numpy as np

X_T = np.sort(np.random.uniform(0, 8, size=(100, 1)))
X_S = np.sort(np.random.uniform(4, 8, size=(100, 1)))
Y_T = np.random.normal(4 * X_T + 1, 1 * X_T + 1)
Y_Strue = np.random.normal(4 * X_S + 1, 1 * X_S + 1)
Y_S = 5 * Y_Strue + 2
condoer.fit(Y_S, Y_T, X_S, X_T)
Y_S2T = condoer.transform(Y_S)
print(f"before ConDo: {np.mean((Y_S - Y_Strue) ** 2):.3f}")
print(f"after ConDo:  {np.mean((Y_S2T - Y_Strue) ** 2):.3f}")

More thorough examples are provided in the examples directory, and all code for experiments are in the papers directory.

Development

Testing

In this directory run

pytest

Citing this toolbox

If you use this toolbox in your research and find it useful, please cite ConDo using the following reference to the TMLR paper:

In Bibtex format:

@article{
mccarter2024towards,
title={Towards Backwards-Compatible Data with Confounded Domain Adaptation},
author={Calvin McCarter},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2024},
url={https://openreview.net/forum?id=GSp2WC7q0r},
note={}
}

License Information

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.