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

Event-forecasting - Event Forecasting Algorithms With Python

A library for low-memory inferencing in PyTorch.

Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image

Revisiting, benchmarking, and refining Heterogeneous Graph Neural Networks.

Categorical Depth Distribution Network for Monocular 3D Object Detection

Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs

CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields

DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism (SVS & TTS); AAAI 2022; Official code

Tensorflow port of a full NetVLAD network

The official PyTorch implementation of recent paper - SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

一个运行在 𝐞𝐥𝐞𝐜𝐕𝟐𝐏 或 𝐪𝐢𝐧𝐠𝐥𝐨𝐧𝐠 等定时面板的签到项目

VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning

Supervised Contrastive Learning for Product Matching

GLANet - The code for Global and Local Alignment Networks for Unpaired Image-to-Image Translation arxiv

DirectVoxGO reconstructs a scene representation from a set of calibrated images capturing the scene.

Rede Neural Convolucional feita durante o processo seletivo do Laboratório de Inteligência Artificial da FACOM (UFMS)

Pytorch-Swin-Unet-V2 - a modified version of Swin Unet based on Swin Transfomer V2

Gesture-Volume-Control - This Python program can adjust the system's volume by using hand gestures

Official Implementation of Domain-Aware Universal Style Transfer

Polynomial-time Meta-Interpretive Learning