1995subhankar1995 / ncp

Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis(AAAI-2023)

NCP (Neighborhood Conformal Prediction)

This repository contains the code and models necessary to replicate the results of our recent paper:- Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis
Please follow the project page for new updates.

Contents

The major content of our repo are:

• CP/ The main folder containing the python scripts for running the experiments.
• checkpoints/ Our pre trained models.
• datasets/ A folder that contains the datasets used in our experiments CIFAR10, CIFAR100, Imagenet.
• Results/ A folder that contains different files from different experiments.

CP folder contains:

1. Classification.py: the main code for running experiments.

Prerequisites

Prerequisites for running our code:

• numpy
• scipy
• sklearn
• torch
• tqdm
• seaborn
• torchvision
• pandas
• plotnine

Running instructions

1. Install dependencies:

conda create -n NCP python=3.8
conda activate NCP
conda install -c conda-forge numpy
conda install -c conda-forge scipy
conda install -c conda-forge scikit-learn
conda install -c conda-forge tqdm
conda install -c conda-forge seaborn
conda install -c conda-forge pandas
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
conda install -c conda-forge plotnine

i. Download our trained models from here and extract them to NCP/checkpoints.

ii. If you want to run ImageNet experiments, obtain a copy of ImageNet ILSVRC2012 validation set from here and extract the data. Put the created folders in /datasets/.

3. The current working directory when running the scripts should be the folder NCP.

To reproduce the results for CIFAR10:

python -W ignore ClassificationTestMyFunctions.py --dataset CIFAR10
python -W ignore plot_classification.py --dataset CIFAR10

To reproduce the results for CIFAR100:

python -W ignore ClassificationTestMyFunctions.py --dataset CIFAR100
python -W ignore plot_classification.py --dataset CIFAR100

To reproduce the results for ImageNet:

python -W ignore ClassificationTestMyFunctions.py --dataset ImageNet
python -W ignore plot_classification.py --dataset ImageNet