Robust Hybrid Learning With Expert Augmentation

This repository contains the official implementations of experiments made in the paper "Robust Hybrid Learning With Expert Augmentation " from Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Joern-Henrik Jacobsen. You can use the following reference to cite our work:

@article{wehenkel2023robust,
  title={Robust Hybrid Learning With Expert Augmentation},
  author={Wehenkel, Antoine and Behrmann, Jens and Hsu, Hsiang and Sapiro, Guillermo and Louppe, Gilles and Jacobsen, J{\"o}rn-Henrik},
  journal={Transactions on Machine Learning Research},
  year={2023}
}

Dependencies

You can install dependencies from the files requirements.txt and requirements_pip.txt as:

conda env create -f requirements.txt -n RHL
conda activate RHL
pip install -r requirements_pip.txt

To be able to run all commands from the root of the repository you must also execute: export PYTHONPATH=.

Data generation

Before running any experiments you must generate train, validation and test sets by running the GenerateDataset.py python code. For instance to generate data for the Damped Pendulum experiments you would do:

python code/data/DampedPendulum/GenerateDataset.py

Double pendulum data

You can get the data of the double pendulum experiments from this website and fill in the folder code/data/DoublePendulum/dpc_dataset_csv with the data from there.

Training hybrid learning methods:

You can simply run the run_experiments.py file from the code/scripts subfolder with the appropriate configuration file. For instance the configuration file code/scripts/configs/Pendulum/APHYNITY.yaml will train an hybrid model with APHYNITY on the Damped Pendulum data:

python code/scripts/run_experiments.py --config code/scripts/configs/Pendulum/APHYNITY.yaml