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

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
Code accompanying the paper "ProxyFL: Decentralized Federated Learning through Proxy Model Sharing"

ProxyFL Code accompanying the paper "ProxyFL: Decentralized Federated Learning through Proxy Model Sharing" Authors: Shivam Kalra*, Junfeng Wen*, Jess

Layer6 Labs 14 Dec 06, 2022
Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX

CQL-JAX This repository implements Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX (FLAX). Implementation is built on

Karush Suri 8 Nov 07, 2022
TOOD: Task-aligned One-stage Object Detection, ICCV2021 Oral

One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of

264 Jan 09, 2023
A Kitti Road Segmentation model implemented in tensorflow.

KittiSeg KittiSeg performs segmentation of roads by utilizing an FCN based model. The model achieved first place on the Kitti Road Detection Benchmark

Marvin Teichmann 890 Jan 04, 2023
Some methods for comparing network representations in deep learning and neuroscience.

Generalized Shape Metrics on Neural Representations In neuroscience and in deep learning, quantifying the (dis)similarity of neural representations ac

Alex Williams 45 Dec 27, 2022
Waymo motion prediction challenge 2021: 3rd place solution

Waymo motion prediction challenge 2021: 3rd place solution ๐Ÿ“œ Technical report ๐Ÿ—จ๏ธ Presentation ๐ŸŽ‰ Announcement ๐Ÿ›†Motion Prediction Channel Website ๐Ÿ›†

158 Jan 08, 2023
A platform for intelligent agent learning based on a 3D open-world FPS game developed by Inspir.AI.

Wilderness Scavenger: 3D Open-World FPS Game AI Challenge This is a platform for intelligent agent learning based on a 3D open-world FPS game develope

46 Nov 24, 2022
Explicable Reward Design for Reinforcement Learning Agents [NeurIPS'21]

Explicable Reward Design for Reinforcement Learning Agents [NeurIPS'21]

3 May 12, 2022
Fast and Easy Infinite Neural Networks in Python

Neural Tangents ICLR 2020 Video | Paper | Quickstart | Install guide | Reference docs | Release notes Overview Neural Tangents is a high-level neural

Google 1.9k Jan 09, 2023
Official PyTorch implementation of "Improving Face Recognition with Large AgeGaps by Learning to Distinguish Children" (BMVC 2021)

Inter-Prototype (BMVC 2021): Official Project Webpage This repository provides the official PyTorch implementation of the following paper: Improving F

Jungsoo Lee 16 Jun 30, 2022
Deep-Learning-Image-Captioning - Implementing convolutional and recurrent neural networks in Keras to generate sentence descriptions of images

Deep Learning - Image Captioning with Convolutional and Recurrent Neural Nets ========================================================================

23 Apr 06, 2022
Code for Fold2Seq paper from ICML 2021

[ICML2021] Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design Environment file: environment.yml Data and Feat

International Business Machines 43 Dec 04, 2022
Conditional Generative Adversarial Networks (CGAN) for Mobility Data Fusion

This code implements the paper, Kim et al. (2021). Imputing Qualitative Attributes for Trip Chains Extracted from Smart Card Data Using a Conditional Generative Adversarial Network. Transportation Re

Eui-Jin Kim 2 Feb 03, 2022
TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform

TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform

2.6k Jan 04, 2023
Official implementation of "Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection" in CVPR 2022.

Jadena Official implementation of "Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection" in CVPR 2022. arXiv

Qing Guo 13 Nov 29, 2022
Winning solution of the Indoor Location & Navigation Kaggle competition

This repository contains the code to generate the winning solution of the Kaggle competition on indoor location and navigation organized by Microsoft

Tom Van de Wiele 62 Dec 28, 2022
PyTorch implementation of deep GRAph Contrastive rEpresentation learning (GRACE).

GRACE The official PyTorch implementation of deep GRAph Contrastive rEpresentation learning (GRACE). For a thorough resource collection of self-superv

Big Data and Multi-modal Computing Group, CRIPAC 186 Dec 27, 2022
OpenMMLab Pose Estimation Toolbox and Benchmark.

Introduction English | ็ฎ€ไฝ“ไธญๆ–‡ MMPose is an open-source toolbox for pose estimation based on PyTorch. It is a part of the OpenMMLab project. The master b

OpenMMLab 2.8k Dec 31, 2022
DeepLab resnet v2 model in pytorch

pytorch-deeplab-resnet DeepLab resnet v2 model implementation in pytorch. The architecture of deepLab-ResNet has been replicated exactly as it is from

Isht Dwivedi 601 Dec 22, 2022
Object detection on multiple datasets with an automatically learned unified label space.

Simple multi-dataset detection An object detector trained on multiple large-scale datasets with a unified label space; Winning solution of E

Xingyi Zhou 407 Dec 30, 2022