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

Related tags

Deep LearningEMC-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/
  1. 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
  1. 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
  1. 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
  1. 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
  1. 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
  1. 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
Owner
Prateek Yadav
Prateek Yadav
GitHub Repository
Repository of the paper Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models at ML4AD @ NeurIPS 2021.

Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models Code and supplementary materials Repository of the p

Daniel Bogdoll 4 Jul 13, 2022
Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering (NAACL 2021)

Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering Abstract In open-domain question answering (QA), retrieve-and-read mec

Clova AI Research 34 Apr 13, 2022
MPI Interest Group on Algorithms on 1st semester 2021

MPI Algorithms Interest Group Introduction Lecturer: Steve Yan Location: TBA Time Schedule: TBA Semester: 1 Useful URLs Typora: https://typora.io Goog

Ex10si0n 13 Sep 08, 2022
[AI6101] Introduction to AI & AI Ethics is a core course of MSAI, SCSE, NTU, Singapore

[AI6101] Introduction to AI & AI Ethics is a core course of MSAI, SCSE, NTU, Singapore. The repository corresponds to the AI6101 of Semester 1, AY2021-2022, starting from 08/2021. The instructors of

AccSrd 1 Sep 22, 2022
Set of methods to ensemble boxes from different object detection models, including implementation of "Weighted boxes fusion (WBF)" method.

Set of methods to ensemble boxes from different object detection models, including implementation of "Weighted boxes fusion (WBF)" method.

1.4k Jan 05, 2023
Deep Multi-Magnification Network for multi-class tissue segmentation of whole slide images

Deep Multi-Magnification Network This repository provides training and inference codes for Deep Multi-Magnification Network published here. Deep Multi

Computational Pathology 12 Aug 06, 2022
Code for database and frontend of webpage for Neural Fields in Visual Computing and Beyond.

Neural Fields in Visual Computing—Complementary Webpage This is based on the amazing MiniConf project from Hendrik Strobelt and Sasha Rush—thank you!

Brown University Visual Computing Group 29 Nov 30, 2022
Hardware-accelerated DNN model inference ROS2 packages using NVIDIA Triton/TensorRT for both Jetson and x86_64 with CUDA-capable GPU

Isaac ROS DNN Inference Overview This repository provides two NVIDIA GPU-accelerated ROS2 nodes that perform deep learning inference using custom mode

NVIDIA Isaac ROS 62 Dec 14, 2022
ChatBot-Pytorch - A GPT-2 ChatBot implemented using Pytorch and Huggingface-transformers

ChatBot-Pytorch A GPT-2 ChatBot implemented using Pytorch and Huggingface-transf

ParZival 42 Dec 09, 2022
Reading list for research topics in Masked Image Modeling

awesome-MIM Reading list for research topics in Masked Image Modeling(MIM). We list the most popular methods for MIM, if I missed something, please su

ligang 231 Dec 07, 2022
Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

Amazon Forest Computer Vision Satellite Image tagging code using PyTorch / Keras Here is a sample of images we had to work with Source: https://www.ka

Mamy Ratsimbazafy 359 Jan 05, 2023
PyTorch Code for NeurIPS 2021 paper Anti-Backdoor Learning: Training Clean Models on Poisoned Data.

Anti-Backdoor Learning PyTorch Code for NeurIPS 2021 paper Anti-Backdoor Learning: Training Clean Models on Poisoned Data. Check the unlearning effect

Yige-Li 51 Dec 07, 2022
Implementation of "Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency"

Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency (ICCV2021) Paper Link: https://arxiv.org/abs/2107.11355 This implementation bui

32 Nov 17, 2022
PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021.

GCResNet PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021. The code will

11 May 19, 2022
[NeurIPS 2021] Code for Unsupervised Learning of Compositional Energy Concepts

Unsupervised Learning of Compositional Energy Concepts This is the pytorch code for the paper Unsupervised Learning of Compositional Energy Concepts.

45 Nov 30, 2022
High-resolution networks and Segmentation Transformer for Semantic Segmentation

High-resolution networks and Segmentation Transformer for Semantic Segmentation Branches This is the implementation for HRNet + OCR. The PyTroch 1.1 v

HRNet 2.8k Jan 07, 2023
Athena is the only tool that you will ever need to optimize your portfolio.

Athena Portfolio optimization is the process of selecting the best portfolio (asset distribution), out of the set of all portfolios being considered,

Indrajit 1 Mar 25, 2022
Serverless proxy for Spark cluster

Hydrosphere Mist Hydrosphere Mist is a serverless proxy for Spark cluster. Mist provides a new functional programming framework and deployment model f

hydrosphere.io 317 Dec 01, 2022
The Submission for SIMMC 2.0 Challenge 2021

The Submission for SIMMC 2.0 Challenge 2021 challenge website Requirements python 3.8.8 pytorch 1.8.1 transformers 4.8.2 apex for multi-gpu nltk Prepr

5 Jul 26, 2022
ISBI 2022: Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image.

Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Introduction This repository contains the PyTorch implem

25 Nov 09, 2022