Awesome

<ins> Split Conformal Prediction under Data Contamination </ins>

This repository contains code to re-produce all the results, plots and tables presented within the paper.

This code was developed using Python 3.9.12. Please start by installing the required packages using the requirements.txt file.

Classification

Synthetic Data

To reproduce the results with the synthetic data, use the command

python3 main.py -c {config_file}

The names of the config files can be found in the experiment_configs/ directory.

To reproduce the results presented across different types of classifiers, run

python3 main.py -c logistic_by_model.yaml
python3 main.py -c hypercube_by_model.yaml

Then use the notebook synthetic_by_model_table.ipynb to generate the table.

Similarly for the experiment varying epsilon, run

python3 main.py -c logistic_by_eps.yaml

Then use the notebook logistic_by_eps.ipynb to generate the plot.

For both the table and the plots, you will need to copy and paste the name of your run into the notebook, which can be found in the results/ directory.

Real Data

For the real data, you will need to begin by downloading the CIFAR10N dataset from http://noisylabels.com/. Place the files CIFAR-10_human.npy and CIFAR10_human.pt into the data/CIFAR10N folder.

The first step is to train the ResNet18 models for each noise setting. This is done by running

python3 train_resnets_cifar10n.py  

We recommend using a GPU for this, although the device is just set to auto in pytorch lightning. This script will create a directory structured as

models/
- aggre_label/
- clean_label/
- random_label1/
    ...

with one folder per label noise setting. Each folder will contain the trained model as well as the predicted logits for the given split of the data.

After running this, use the command

python3 main.py -c cifar10n.yaml

The table can then be generated using the cifar100n_table.ipynb notebook.

Regression

Finally, the regression plot can be re-created using the regression_plot.ipynb notebook.