Towards Rigorous Interpretations

Source code of our paper "Towards Rigorous Interpretations: a Formalisation of Feature Attribution", by Darius Afchar, Romain Hennequin and Vincent Guigue.

Link to our paper

Link to our poster and slides at ICML

This paper is part of the proceedings of the 38th International Conference on Machine Learning (ICML 2021).

Install dependencies

pip install -r requirement.txt

Task sets

We provide the three task sets we have used in our paper:

• multivariate.npy: 1010 tasks with dimension ranging from 2 to 11 ;
• univariate.npy: 1010 tasks with dimension ranging from 2 to 11 ;
• tuning: 110 tasks with dimension ranging from 2 to 11.

i.e. we provide 100 (respectively 10) supervised tasks to solve for the main experiments per dimension d (respectively for tuning). The provided files are dictionaries indexed by keys {d}_1 to {d}_100 + 1 simple sanity-check task {d}_0. Each corresponding dict value contains a tuple of the centroids coordinates, associated labels, and ground-truth selection. The associated continuous distributions p' are generated using the functions predict, predict_marginal and grad_predict in the methods.py file.

Run the experiments

Multivariate selection task

Example command:

python eval_multivariate.py --task_set multivariate --output results --tuned_params tuning/tunedparams.npy

Documentation:

usage: eval_multivariate.py [-h] [--gpu GPU] [--task_set TASK_SET]
                            [--tuned_params TUNED_PARAMS]
                            [--start_dim START_DIM] [--end_dim END_DIM]
                            [--output OUTPUT] [--exp_name EXP_NAME]
                            [--mode MODE]
                            
optional arguments:
  -h, --help            show this help message and exit
  --gpu GPU             GPU index to use
  --task_set TASK_SET   Task set to choose
  --tuned_params TUNED_PARAMS .npy file with tuned parameters
  --start_dim START_DIM Task dim range start
  --end_dim END_DIM     Task dim range end
  --output OUTPUT       Output directory with intermediate results
  --exp_name EXP_NAME   Unique identifier for a given experiment
  --mode MODE           Execute all methods or given subset

Univariate selection task

Example command:

python eval_univariate.py --task_set univariate --output results --tuned_params tuning/tunedparams.npy

Intermediate results are saved every ten minutes.

Contact

Contact us at research@deezer.com