Code for Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions

Last update: Nov 25, 2022

Related tags

Deep Learning EMC-COLS-recourse

Overview

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

Code for Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions

Related tags

Overview

EMS-COLS-recourse

Initial Code for Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions

Folder structure:

Making the environment

Steps for running experiments.

To train a new blackbox model

Owner

Prateek Yadav

Official implementation of the paper 'Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution'

Source Code for ICSE 2022 Paper - ``Can We Achieve Fairness Using Semi-Supervised Learning?''

Contour-guided image completion with perceptual grouping (BMVC 2021 publication)

Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners

PyTorch implementation for ComboGAN

HIVE: Evaluating the Human Interpretability of Visual Explanations

Official Implementation for Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation

Adversarially Learned Inference

NLP made easy

A simple baseline for 3d human pose estimation in PyTorch.

A booklet on machine learning systems design with exercises

Implementation for HFGI: High-Fidelity GAN Inversion for Image Attribute Editing

Simple Dynamic Batching Inference

3D2Unet: 3D Deformable Unet for Low-Light Video Enhancement (PRCV2021)

Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulation

Official Implementation for the "An Empirical Investigation of 3D Anomaly Detection and Segmentation" paper.

Alfred-Restore-Iterm-Arrangement - An Alfred workflow to restore iTerm2 window Arrangements

Robust Self-augmentation for NER with Meta-reweighting

Joint Gaussian Graphical Model Estimation: A Survey

Python periodic table module