cmonge 0.0.1

Extension of the Monge Gap to learn conditional optimal transport maps

Conditional Monge Gap

An extension of the Monge Gap, an approach to estimate transport maps conditionally on arbitrary context vectors. It is based on a two-step training procedure combining an encoder-decoder architecture with an OT estimator. The model is applied to 4i and scRNA-seq datasets.

Environment setup

The environment is managed with poetry. We recommend setting up a virtual environment. The code was tested in Python 3.10.

pip install poetry
poetry install -v

If the installation was successful you can run the tests using pytest

poetry shell # activate env
pytest

Data

The preprocessed version of the Sciplex3 and 4i datasets can be downloaded here.

Example usage

You can find example config in configs/conditional-monge-sciplex.yml. To train an autoencoder model:

from cmonge.datasets.conditional_loader import ConditionalDataModule
from cmonge.trainers.ae_trainer import AETrainerModule
from cmonge.utils import load_config


config_path = Path("configs/conditional-monge-sciplex.yml")
config = load_config(config_path)
config.data.ae = True

datamodule = ConditionalDataModule(config.data, config.condition)
ae_trainer = AETrainerModule(config.ae)

ae_trainer.train(datamodule)
ae_trainer.evaluate(datamodule)

To train a conditional monge model:

from cmonge.datasets.conditional_loader import ConditionalDataModule
from cmonge.trainers.conditional_monge_trainer import ConditionalMongeTrainer
from cmonge.utils import load_config

config_path = Path("configs/conditional-monge-sciplex.yml")
logger_path = Path("logs")
config = load_config(config_path)

datamodule = ConditionalDataModule(config.data, config.condition)
trainer = ConditionalMongeTrainer(jobid=1, logger_path=logger_path, config=config.model, datamodule=datamodule)

trainer.train(datamodule)
trainer.evaluate(datamodule)

Citation

If you use the package, please cite:

@inproceedings{
  harsanyi2024learning,
  title={Learning Drug Perturbations via Conditional Map Estimators},
  author={Benedek Harsanyi and Marianna Rapsomaniki and Jannis Born},
  booktitle={ICLR 2024 Workshop on Machine Learning for Genomics Explorations},
  year={2024},
  url={https://openreview.net/forum?id=FE7lRuwmfI}
}