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

Codes for the AAAI'22 paper "TransZero: Attribute-guided Transformer for Zero-Shot Learning"

A collection of inference modules for fastai2

Pairwise model for commonlit competition

An implementation of the Contrast Predictive Coding (CPC) method to train audio features in an unsupervised fashion.

PyTorch implementation of SmoothGrad: removing noise by adding noise.

Tutorial repo for an end-to-end Data Science project

Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.

Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search

Official implementation of CVPR2020 paper "Deep Generative Model for Robust Imbalance Classification"

Gapmm2: gapped alignment using minimap2 (align transcripts to genome)

Neural Network to colorize grayscale images

Automatic learning-rate scheduler

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Disentangled Lifespan Face Synthesis

Official implementation of cosformer-attention in cosFormer: Rethinking Softmax in Attention

A Keras implementation of YOLOv3 (Tensorflow backend)

Parametric Contrastive Learning (ICCV2021)

Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included.

The Body Part Regression (BPR) model translates the anatomy in a radiologic volume into a machine-interpretable form.

CALVIN - A benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks