This repository contains the code to reproduce the experiments of the paper Conformal Prediction with Missing Values, M. Zaffran, A. Dieuleveut, J. Josse and Y. Romano, ICML 2023.
The notebook CP_NA_Synthetic.ipynb allows to reproduce the synthetic experiments, while CP_NA_Semi-synthetic.ipynb focuses on the semi-synthetic experiments.
The corresponding _Plots notebooks contain the code for displaying the results in the same format as in the paper.
The core code for the algorithms CP-MDA-Exact and CP-MDA-Nested can be found in the prediction.py file.
imputation.py contains the functions used for imputation of the data sets.
generation.py allows to generate synthetic data (outcome and features, but also missing values).
files.py handles the file names, files writing and loading.
utils.py contains some useful functions like computing the metrics associated to interval predictions, combinatorics on patterns etc.
datasets.py pre-process the real data sets used in the semi-synthetic experiments.
Note that, as mentioned in the .py files, some piece of code are taken from other GitHub repositories, namely:
- CQR (Romano et al., 2019) repository, available here, for the (cleaning of the) data sets used in the semi-synthetic experiments;
- CHR (Sesia and Romano, 2021) repository, available here, for the Quantile Neural Network architecture.
This repository will be updated in the next few days.
MIT © Margaux Zaffran