Experiments for the paper 'Attribution based Explanations cannot be robust and recourse sensitive'

This repository contains all the relevant code to run the experiments that were used in the paper 'Attribution based Explanations cannot be Robust and Recourse sensitive'.

Installing the requirements

It is advised to create a new environment and install all necessary dependencies.

pip install -r requirements.txt

There can be an issue that the package cv2 is not installed. To make sure that it is, run the following command:

pip install opencv-python

Running the experiments

All experiments can be run by running the following script

python main.py 

Options

If you do not want to run all experiments, but only a subset of them, you can pass a [y/n] argument indicating which experiments you want to run. For example

python main.py --gradient y --lime n --shap n

will only generate the pictures that use the gradient based explanations.

Running the experiments with Docker

Alternatively, if you want to run the experiments using docker. You can build the image using

docker build -t recourse_experiments . 

To run the experiment you type

docker run \
    -v /path/to/save/results/pickled_data:/usr/src/pickled_data \
    -v /path/to/save/results/vanilla_gradients:/usr/src/vanilla_gradients \
    -v /path/to/save/results/smoothgrad:/usr/src/smoothgrad \
    -v /path/to/save/results/integrated_gradients:/usr/src/integrated_gradients \
    -v /path/to/save/results/lime_pictures:/usr/src/lime_pictures \
    -v /path/to/save/results/shap_pictures:/usr/src/shap_pictures \
    recourse_experiments:latest