This repository contains the code to reproduce the experiments of the paper:
NeuMiss networks: differential programming for supervised learning with missing values
If you want to try NeuMiss, we advise you to look at the NeuMiss_sota repository, which provides an easy-to-use PyTorch module implementing NeuMiss.
The file NeuMiss.yml indicates the packages required as well as the versions used in our experiments.
The methods used are implemented in the following files:
- neumannS0_mlp: the NeuMiss network.
- mlp: the feedforward neural network.
- estimators: the other methods used.
The files ground_truth and amputation contain the code for data simulation and the code for the Bayes predictors.
To reproduce the experiments, use:
python launch_simu_perf MCARpython launch_simu_perf MAR_logisticpython launch_simu_perf gaussian_smpython launch_simu_perf probit_smpython launch_simu_depth_effectpython launch_simu_architecture
These scripts save their results as csv files in the results foder. The plots can be obtained from these csv files by running the plots_xxx files.