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ECCCo

This is the code base used for our AAAI 2024 paper Faithful Model Explanations through Energy-Constrained Counterfactual Explanations. The pre-print version of the paper is available on arXiv.

Inspecting the Package Code

This code base is structured as a Julia package. The package code is located in the src/ folder.

Inspecting the Results

All results have been carefully reported either in the paper itself or in the supplementary material. In addition, we have released our results as binary files. These will be made publicly available after the review process.

Reproducing the Results

This repo contains a small Julia package that will eventually be merged into CounterfactualExplanations.jl and is therefore not registered on the general registry. You can install the package directly from here as follows:

using Pkg
Pkg.add(url="https://github.com/pat-alt/ECCCo.jl")

This will automatically set up the environment and install all the necessary dependencies.

Sequential

The experiments/ folder contains separate Julia scripts for each dataset and a run_experiments.jl that calls the individual scripts. You can either run these scripts inside a Julia session or just use the command line to execute them as described in the following.

To run the experiment for a single dataset, (e.g. linearly_separable) simply run the following command:

julia --project=experiments/ experiments/run_experiments.jl -- data=linearly_separable

We use the following identifiers:

  • linearly_separable (Linearly Separable data)
  • moons (Moons data)
  • circles (Circles data)
  • california_housing (California Housing data)
  • gmsc (GMSC data)
  • german_credit (German Credit data)
  • mnist (MNIST data)
  • fmnist (Fashion MNIST data)

To run experiments for multiple datasets at once simply separate them with a comma ,

julia --project=experiments/ experiments/run_experiments.jl -- data=linearly_separable,moons,circles

To run all experiments at once you can instead run

julia --project=experiments/ experiments/run_experiments.jl -- run-all

Pre-trained versions of all of our black-box models have been archived as Pkg artifacts and are used by default. Should you wish to retrain the models as well, simply use the retrain flag as follows:

julia --project=experiments experiments/run_experiments.jl -- retrain data=linearly_separable

Multi-threading

To use multi-threading, proceed as follows:

julia --threads 16 --project=experiments experiments/run_experiments.jl -- data=linearly_separable threaded

Multi-Processing

To use multi-processing, proceed as follows:

mpiexecjl --project=experiments -n 4 julia experiments/run_experiments.jl -- data=linearly_separable mpi

Multi-processing and multi-threading can be combined:

mpiexecjl --project=experiments -n 4 julia experiments/run_experiments.jl -- data=linearly_separable threaded mpi

Reproducing Figures

To recreate the exact figures shown in the main paper you can use two notebooks:

  • experiments/notebooks/figure2.qmd: Figure 2 (gradient fields)
  • experiments/notebooks/figure1and3.qmd: Figures 1 and 3 (MNIST examples)