# LOTUS: learning to learn with optimal transport for unsupervised scenarios

Welcome to repository for our paper, This repository contains a modified fork of GAMA
https://github.com/openml-labs/gama

LOTUS experiments requires additional libraries
- PyOD
- MetaOD
- ott-jax
- ADBench : https://github.com/Minqi824/ADBench

## Notes

1. Because of ADBenchs design LOTUS should be run inside ADBenchs clone repository. To avoid confusion we did not provide it here but it can be simply cloned from the github repo.

2. Our final goal will be to open source it and make itt more user friendly by making a pip package, we can't do it now because of time contrains and anonimity issues. 

3. Our experiments ran on wandb and that's where we stored our algorithm store, you cansimply run gama on datasets and store it ina  csv file. 

4. Experiments can't run out of the box, the code is in paper/ folder. if you just want to try it for one dataset you can simply run single_dataset.py(this code has been tested on colab so it should work), but that will include GAMA for run for 2 hours as well including distance computations. make sure you insert the right address in sys.path.append

4. Other experiments were ran on a slurm cluster in parallel

In the end we thank the authors of ADBench Songqiao Han, Xiyang Hu, Hailiang Huang, Minqi Jiang, Yue Zhao, Author of GAMA: Pieter Gijsbers and authors of OTT-JAX Marco Cuturi, Laetitia Meng-Papaxanthos, Yingtao Tian, Charlotte Bunne, Geoff Davis, Olivier Teboul for making their tools avaiable, this research would not have been possible without these open source tools

In the end we thank the reviews for taking time to look at our code as well :)