Metabu - Learning meta-features

This is the Official code for ICLR 2022 paper "Learning meta-features for AutoML", Herilalaina Rakotoarison and Louisot Milijaona and Andry Rasoanaivo and Michèle Sebag and Marc Schoenauer.

This repository is still under active developement.

Installation

Install with pip:

pip install -r requirements.txt
python setup.py install

Use Singularity:

build local container with definition file env/metabu.def.
fetch remote container from (coming soon).

Usage

Simple to use:

from metabu import Metabu

basic_representations = pd.read_csv(...)
target_representations = pd.read_csv(...)
metabu = Metabu()
metabu.train(basic_reprs=basic_representations,
             target_reprs=target_representations,
             column_id="task_id")
metabu.predict(basic_reprs=basic_representations)
metabu.get_importances()

Try: cd examples; python metabu_adaboost.py

Feel free to create an issue if you have questions.

Experiments

Script to reproduce experiments will be available under the experiments branch.

Credits

We use the implementation of the ICML 2020 work "Learning Autoencoders with Relational Regularization" [https://arxiv.org/pdf/2002.02913.pdf] to compute the Fused-Gromov-Wasserstein distance.
We also grateful to the maintainers and contributors of the Python libraries in requirements.txt.

Cite Metabu

@inproceedings{rakotoarison2022learning,
    title       = {Learning meta-features for Auto{ML}},
    author      = {Herilalaina Rakotoarison and Louisot Milijaona and Andry Rasoanaivo and Michele Sebag and Marc Schoenauer},
    booktitle   = {International Conference on Learning Representations},
    year        = {2022},
    url         = {https://openreview.net/forum?id=DTkEfj0Ygb8}
}

herilalaina / metabu