M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps

This is the repository for our paper titled "M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps". This paper has been accepted at the 23rd IEEE International Conference on Machine Learning and Applications (ICMLA).

Architecture

Prerequisites and Instructions

All python packages needed are listed in pip-requirements.txt file and can be installed simply using the pip command.

Get the results for BasicMotions dataset by running

python3 main0.py --pname MCLES_BasicMotions --task_id 0 --run_mode turing --jobs_per_task 10 --samples_per_task 40 --dataset BasicMotions --algo cf --seed_value 1 --enable_lr_decay False --background_data train --background_data_perc 100 --enable_seed True --max_itr 1000 --run_id 0 --bbm dnn --enable_tvnorm True --enable_budget True --dataset_type test --l_budget_coeff 1.0 --run 1 --l_tv_norm_coeff 1.0 --l_max_coeff 1.0