Conformal prediction under feedback covariate shift for biomolecular design

This repo contains the code accompanying the following paper:

C. Fannjiang, S. Bates, A. Angelopoulos, J. Listgarten, M. I. Jordan, Conformal prediction under feedback covariate shift for biomolecular design. 2022. Proceedings of the National Academy of Sciences, 119(43), e2204569119. link

See calibrate.py for implementations of both the full and split conformal prediction algorithms we describe. assay.py contains utilities and classes for handling the fluorescence and AAV datasets, which are stored (along with relevant models and results) in fluorescence/ and aav/, respectively.

Notebooks for reproducing and plotting the results of the simulated protein design experiments are as follows:

• notebooks/fluorescence-experiments.ipynb shows how we ran the fluorescent protein design experiments, which uses full conformal prediction under feedback covariate shift, algorithmically optimized for ridge regression (Alg. S2 in the SI Appendix).
• notebooks/fluorescence-figures.ipynb creates Figs. 3 and 4 in the main paper and Fig. S2 in the SI Appendix.
• notebooks/aav-experiments.ipynb shows how we ran the AAV design experiments, which uses a randomized version of split conformal prediction under covariate shift (Alg. S1 in the SI Appendix).
• notebooks/aav-figures.ipynb creates Fig. 5 in the main paper.