Approximate Vanishing Ideal Computations at Scale

Code for the paper: Wirth, E.S., Kera, H. and Pokutta, S., 2022, September. Approximate Vanishing Ideal Computations at Scale. In Proceedings of the Eleventh International Conference on Learning Representations.

References

This project is an extension of the previously published Git Repository CGAVI, which is the code corresponding to the following paper:

Wirth, E. S., & Pokutta, S. (2022, May). Conditional gradients for the approximately vanishing ideal. In Proceedings of the International Conference on Artificial Intelligence and Statistics (pp. 2191-2209). PMLR.

Installation guide

Download the repository and store it in your preferred location, say ~/tmp.

Open your terminal and navigate to ~/tmp.

Run the command:

$ conda env create --file environment.yml

This will create the conda environment avi_at_scale.

Activate the conda environment with:

$ conda activate avi_at_scale

Run the tests:

>>> python3 -m unittest

No errors should occur.

Execute the experiments:

>>> python3 experiments_all.py

This will create folders named data_frames and plots, which contain subfolders containing the experiment results and the plots, respectively.

The performance experiments can be displayed as latex_code by executing:

>>> experiments_results_to_latex.py