EMS-COLS-recourse
Initial Code for Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions
Folder structure:
- data folder contains raw and final preprocessed data, along with the pre-processing script.
- Src folder contain the code for our method.
- trained_model contains the trained black box model checkpoint.
Making the environment
conda create -n rec_gen python=3.8.1
conda activate rec_gen
pip install -r requirements.txt
Steps for running experiments.
change current working directory to src
cd ./src/
- Run data_io.py to dump mcmc cost samples.
python ./utils/data_io.py --save_data --data_name adult_binary --dump_negative_data --num_mcmc 1000
python ./utils/data_io.py --save_data --data_name compas_binary --dump_negative_data --num_mcmc 1000
- run main experiments on COLS and P-COLS.
python run.py --data_name adult_binary --num_mcmc 1000 --model ls --num_cfs 10 --project_name exp_main --budget 5000
python run.py --data_name compas_binary --num_mcmc 1000 --model ls --num_cfs 10 --project_name exp_main --budget 5000
python run.py --data_name adult_binary --num_mcmc 1000 --model pls --num_cfs 10 --project_name exp_main --budget 5000
python run.py --data_name compas_binary --num_mcmc 1000 --model pls --num_cfs 10 --project_name exp_main --budget 5000
- Run ablation Experiments
python run.py --data_name adult_binary --num_mcmc 1000 --model ls --num_cfs 10 --project_name exp_ablation --budget 3000 --eval cost
python run.py --data_name adult_binary --num_mcmc 1000 --model ls --num_cfs 10 --project_name exp_ablation --budget 3000 --eval cost_simple
python run.py --data_name adult_binary --num_mcmc 1000 --model ls --num_cfs 10 --project_name exp_ablation --budget 3000 --eval proximity
python run.py --data_name adult_binary --num_mcmc 1000 --model ls --num_cfs 10 --project_name exp_ablation --budget 3000 --eval sparsity
python run.py --data_name adult_binary --num_mcmc 1000 --model ls --num_cfs 10 --project_name exp_ablation --budget 3000 --eval diversity
- Run experiments with budget
python run.py --data_name adult_binary --model ls --num_cfs 10 --num_users 100 --project_name exp_budget --budget 500
python run.py --data_name adult_binary --model ls --num_cfs 10 --num_users 100 --project_name exp_budget --budget 1000
python run.py --data_name adult_binary --model ls --num_cfs 10 --num_users 100 --project_name exp_budget --budget 2000
python run.py --data_name adult_binary --model ls --num_cfs 10 --num_users 100 --project_name exp_budget --budget 3000
python run.py --data_name adult_binary --model ls --num_cfs 10 --num_users 100 --project_name exp_budget --budget 5000
python run.py --data_name adult_binary --model ls --num_cfs 10 --num_users 100 --project_name exp_budget --budget 10000
- Run experiments with number of counterfactuals
python run.py --data_name adult_binary --model model_name --num_cfs 1 --num_users 100 --project_name exp_cfs --budget 5000
python run.py --data_name adult_binary --model model_name --num_cfs 2 --num_users 100 --project_name exp_cfs --budget 5000
python run.py --data_name adult_binary --model model_name --num_cfs 3 --num_users 100 --project_name exp_cfs --budget 5000
python run.py --data_name adult_binary --model model_name --num_cfs 5 --num_users 100 --project_name exp_cfs --budget 5000
python run.py --data_name adult_binary --model model_name --num_cfs 10 --num_users 100 --project_name exp_cfs --budget 5000
python run.py --data_name adult_binary --model model_name --num_cfs 20 --num_users 100 --project_name exp_cfs --budget 5000
python run.py --data_name adult_binary --model model_name --num_cfs 30 --num_users 100 --project_name exp_cfs --budget 5000
- Experiment with respect to Monte Carlo samples
- Run these commands for different num_mcmc values. Default set to 5 in commands.
python ./utils/data_io.py --save_data --data_name adult_binary --dump_negative_data --num_mcmc 5
python run.py --data_name adult_binary --num_mcmc 5 --model model_name --num_cfs 10 --project_name exp_mcmc --budget 5000 --num_users 100
To train a new blackbox model
- Run this right after preprocessing the data.
python train_model.py --data_name adult --max_epochs 1000 --check_val_every_n_epoch=1 --learning_rate=0.0001
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