jpmorganchase / dropout-rashomon-set-exploration

JPMorganChase Global Technology Aplied Research dropout rashomon set exploration project

This is the official Python implementation of the paper Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation (ICLR 2024).

This repository contains 3 different algorithms to explore the Rashomon set (re-training, dropout and adversarial weight perturbation (AWP)) and the estimation of 6 predictive multplicity metrics.

Installation

Download the repo and create a folder results/

git clone https://github.com/jpmorganchase/dropout-rashomon-set-exploration.git
cd dropout-rashomon-set-exploration
mkdir results

A suitable conda environment named dropout-rashomon can be created and activated with:

conda create --name dropout-rashomon python=3.8.13
conda activate dropout-rashomon
pip install -r requirements.txt

• If you encounter the Runtime error: GET was unable to find an engine to execute, see here for a quick fix.

Usage

synthetic/ contains the jupyter notebook to reproduce the results in Figure 1.

uci-tabular/ contains the experiments for the UCI tabular datasets.

First, run train-tabular.py for different strategies to explore the Rashomon set; for example:

## Base Model
python3 train-tabular.py --dataset 'credit-approval' --method 'base'

## Re-training Strategy (sampling)
python3 train-tabular.py --dataset 'credit-approval' --method 'sampling' --sampling_nmodel 100 --nepoch 20
python3 train-tabular.py --dataset 'credit-approval' --method 'sampling' --sampling_nmodel 100 --nepoch 25
python3 train-tabular.py --dataset 'credit-approval' --method 'sampling' --sampling_nmodel 100 --nepoch 30
python3 train-tabular.py --dataset 'credit-approval' --method 'sampling' --sampling_nmodel 100 --nepoch 35
python3 train-tabular.py --dataset 'credit-approval' --method 'sampling' --sampling_nmodel 100 --nepoch 40
python3 train-tabular.py --dataset 'credit-approval' --method 'sampling' --sampling_nmodel 100 --nepoch 45
python3 train-tabular.py --dataset 'credit-approval' --method 'sampling' --sampling_nmodel 100 --nepoch 50
python3 train-tabular.py --dataset 'credit-approval' --method 'sampling' --sampling_nmodel 100 --nepoch 55

## Dropout Strategy
python3 train-tabular.py --dataset 'credit-approval' --method 'dropout' --dropoutmethod 'bernoulli' --drp_nmodel 100 --drp_max_ratio 0.2
python3 train-tabular.py --dataset 'credit-approval' --method 'dropout' --dropoutmethod 'gaussian' --drp_nmodel 100 --drp_max_ratio 0.6

## AWP Strategy
python3 train-tabular.py --dataset 'credit-approval' --method 'awp' --awp_eps 0.000,0.004,0.008,0.012,0.016,0.020,0.024,0.028,0.032,0.036,0.040

You can change the dataset credit-approval to other UCI datasets, including adult, dermatology, mammo, contrac and bank. Note that the AWP Strategy is very time-consuming, and it is suggested not to run it on the Adult Income and Bank datasets.

Then run ../utils/compute_metrics.py to evaluate the predictive multplicity metrics and the results will be saved in results/; for example:

python3 ../utils/compute_metrics.py --dataset 'credit-approval' --method 'sampling' --sampling_nmodel 100 --epoch 20,25,30,35,40,45,50,55
python3 ../utils/compute_metrics.py --dataset 'credit-approval' --method 'dropout' --dropoutmethod 'bernoulli' --drp_nmodel 100 --drp_max_ratio 0.2
python3 ../utils/compute_metrics.py --dataset 'credit-approval' --method 'dropout' --dropoutmethod 'gaussian' --drp_nmodel 100 --drp_max_ratio 0.6
python3 ../utils/compute_metrics.py --dataset 'credit-approval' --method 'awp' --awp_eps 0.000,0.004,0.008,0.012,0.016,0.020,0.024,0.028,0.032,0.036,0.040

Similarly for dropout ensembles, run the following scripts:

## Ensemble Models
python3 train-tabular.py --dataset 'credit-approval' --method 'dropout' --dropoutmethod 'bernoulli' --drp_nmodel 10000 --drp_max_ratio 0.2
python3 train-tabular.py --dataset 'credit-approval' --method 'dropout' --dropoutmethod 'gaussian' --drp_nmodel 10000 --drp_max_ratio 0.6
python3 ../utils/ensemble.py --dataset 'credit-approval' --dropoutmethod 'bernoulli' --drp_nmodel 10000 --drp_max_ratio 0.2 --ensemble_size 1,2,5,10,20,50,100 --nensemble 100
python3 ../utils/ensemble.py --dataset 'credit-approval' --dropoutmethod 'gaussian' --drp_nmodel 10000 --drp_max_ratio 0.6  --ensemble_size 1,2,5,10,20,50,100 --nensemble 100

For model selection, run the following scripts:

## Model Selection
python3 train-model-selection.py --dataset 'credit-approval' --nretraining 10  --dropoutmethod 'bernoulli' --drp_nmodel 100 --drp_max_ratio 0.2
python3 train-model-selection.py --dataset 'credit-approval' --nretraining 10  --dropoutmethod 'gaussian' --drp_nmodel 100 --drp_max_ratio 0.6
python3 ../utils/compute_model_selection.py --dataset 'credit-approval' --dropoutmethod 'bernoulli' --nretraining 10 --nepoch 100
python3 ../utils/compute_model_selection.py --dataset 'credit-approval' --dropoutmethod 'gaussian' --nretraining 10 --nepoch 100

vision/ contains the experiments for the vision datasets (CIFAR-10 and CIFAR-100).

Run train-vision.py for different strategies to explore the Rashomon set; for example:

## Base Model
python3 train-vision.py --dataset 'cifar10' --model 'vgg16' --method 'base' --nepoch 7

## Re-training Strategy (sampling)
python3 train-vision.py --dataset 'cifar10' --model 'vgg16' --method 'sampling' --sampling_nmodel 20 --nepoch 5
python3 train-vision.py --dataset 'cifar10' --model 'vgg16' --method 'sampling' --sampling_nmodel 20 --nepoch 6
python3 train-vision.py --dataset 'cifar10' --model 'vgg16' --method 'sampling' --sampling_nmodel 20 --nepoch 7
python3 train-vision.py --dataset 'cifar10' --model 'vgg16' --method 'sampling' --sampling_nmodel 20 --nepoch 8
python3 train-vision.py --dataset 'cifar10' --model 'vgg16' --method 'sampling' --sampling_nmodel 20 --nepoch 9

## Dropout Strategy
python3 train-vision.py --dataset 'cifar10' --model 'vgg16' --nepoch 7 --method 'dropout' --dropoutmethod 'bernoulli' --drp_nmodel 50 --ndrp 5 --drp_max_ratio 0.008
python3 train-vision.py --dataset 'cifar10' --model 'vgg16' --nepoch 7 --method 'dropout' --dropoutmethod 'gaussian' --drp_nmodel 50  --ndrp 5 --drp_max_ratio 0.1

Then run ../utils/compute_metrics.py to evaluate the predictive multplicity metrics; for example:

python3 ../utils/compute_metrics.py --dataset 'cifar10' --model 'vgg16' --base_epoch 7 --method 'sampling' --sampling_nmodel 20 --epoch 5,6,7,8,9 --neps 6 --eps_max 0.05
python3 ../utils/compute_metrics.py --dataset 'cifar10' --model 'vgg16' --base_epoch 7 --method 'dropout' --dropoutmethod 'bernoulli' --drp_nmodel 50 --neps 6 --eps_max 0.05 --drp_max_ratio 0.008
python3 ../utils/compute_metrics.py --dataset 'cifar10' --model 'vgg16' --base_epoch 7 --method 'dropout' --dropoutmethod 'gaussian'  --drp_nmodel 50 --neps 6 --eps_max 0.05 --drp_max_ratio 0.1

detection/ contains the codes and figure generation for the experiments of human detection. See detection/README for more information.

utils/ is the main codebase, containing data loader, dropout methods, the AWP algorithm, and the computation of predictive multplicity metrics.

notebooks/ contains the jupyter notebooks to generate figures for the UCI tabular and CIFAR-10/-100 datasets by reading the evaluation results from results/.

Citation

@article{hsu2024dropout,
  title={Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation},
  author={Hsu, Hsiang and Li, Guihong and Hu, Shaohan and Chen, Chun-Fu (Richard)},
  journal={International Conference on Learning Representations},
  year={2024}
}

Contact

If you have any questions, please feel free to contact us through email ([e-mail]). Enjoy!