alekosiosifidis/evaluatingcounterfactuals

This repository contains the code used to quantitatively evaluate counterfactual examples in the associated paper.

On Quantitative Evaluations of Counterfactuals

Install

To install required packages with conda, run the following command:

> conda env create -f requirements.yml

Code

The code contains all the evaluation metrics used in the paper as well as the models and the data.

To evaluate methods, you need to choose a config from the configs directory and to choose which metric to apply. The code will then evaluate the chosen metrics on counterfactuals from all three methods (GB, GL, GEN) and store the results in an appropriate subdirectory in outputs. If you, e.g., want to run all metrics on the MNIST dataset, use the following command:

(cfeval) > python main.py --eval -c configs/mnist/mnist.ini -a

Afterwards you can enumerate the directory by

(cfeval) > python main.py --list

to get an output like the following:

> Listing dirs
000: ./output/celeba_makeup_[0]
001: ./output/fake_mnist_[0]
002: ./output/mnist_0_1_[0]
003: ./output/mnist_[0]

Now, results can be printed for the MNIST dataset (idx 3 above) by

(cfeval) > python main.py --print -c 3 

To get a result like

# # # # # # # # # # # # # # # # # # # # 
# MNIST
# # # # # # # # # # # # # # # # # # # # 
Method \ Metric    TargetClassValidity    ElasticNet    IM1          IM2             FID  Oracle
-----------------  ---------------------  ------------  -----------  -----------  ------  ------------
GB                 99.59 (0.13)           16.07 (0.18)  0.99 (0.00)  0.55 (0.01)   50.23  73.38 (0.87)
GL                 100.00 (0.00)          42.76 (0.31)  0.99 (0.00)  0.53 (0.00)  308.43  37.71 (0.95)
GEN                99.97 (0.03)           99.17 (0.58)  0.88 (0.00)  0.17 (0.00)   90.73  93.13 (0.50)

Directory overview:

File	Description
ckpts	Contains all the (Keras) models used by the various metrics.
data	Contains the data used, both counterfactual examples from GB, GL, and GEN, and original input data.
configs	Contains config files specifying experimental details like dataset, normalization, etc.
data	Contains the data in numpy arrays.
dataset	Code for loading data.
evaluate	Implementations of all the metrics.
output	Directory to hold computed results. Directory already contains results from paper.
config.py	Reads config files from configs
constants.py	Method and metric names.
listing.py	Utility for indexing output dirs (see description below)
main.py	Main file to run all code through.
print_results.py	Utillity function for printing results from json files in the output directory.