Implementation for our work "Model Shapley: Equitable Model Valuation with Black-box Access" in the Thirty-seventh Conference on Neural Information Processing Systems 2023 (NeurIPS'23): Xinyi Xu, Thanh Lam, Chuan Sheng Foo, Bryan Kian Hsiang Low. | Paper | Poster | Presentation |
We provide the following instructions on using this implementation. The python environment is managed through anaconda, and the environment.yml file provides the used packages.
There are several key parts in the implementation, outlined here:
- Part I: training and saving ML models (weights) on specified datasets and hyperparameters;
- Part II: loading the saved ML models, obtaining their Dirichlet abstractions and computing/estimating Model Shapley values;
- Part III: (optional) perform regression learning on Model Shapley values (MSVs), using Gaussian process regression;
- Part IV: others, including the networking plot in Fig. 11 (in our full paper).
Tested OS platform: Ubuntu 20.04 with Nvidia driver version 515.43 and CUDA Version: 11.7.
conda env create -f environment.yml
Example: training models on MNIST with varying model types for
$10$ models each type:
python run_mnist.py -N 10 -t models
Example: training models on CIFAR-10 with varying dataset sizes for
$5$ models each dataset size:
python run_cifar10.py -N 5 -t datasets
Executing the above command will create a directory save_models under which the weights of the trained Pytorch models are saved for loading later.
Example: obtaining the Dirichlet abstractions, using by-class partition, and the MSVs for completed model training of MNIST with
$10$ models under different model types.
python get_MSVs.py -d MNIST -iN 10 --by_class -t models
Example: obtaining the Dirichlet abstractions, not using by-class partition, and the MSVs for completed model training of MNIST with
$10$ models under different model types.
python get_MSVs.py -d MNIST -iN 10 --not_by_class -t models
Executing the above command will create a directory MNIST_results (if dataset is MNIST) under which the results are saved (in the corresonding subdirectories).
Example: performing learning of MSVs on saved Dirichlet abstractions (saved alphas) for MNIST and using RBF kernel for the ExactGP inference.
python learn_MSV.py -d MNIST --not_l1
Example: performing learning of MSVs on saved Dirichlet abstractions (saved alphas) for MNIST and using
$\ell_1$ distance for the ExactGP inference.
python learn_MSV.py -d MNIST --l1
Note: need to provide the right directory storing the saved alphas in the code.
Executing the above command will create a directory learn_MNIST_l2 (if dataset is MNIST) under which the results are saved (in the corresonding subdirectories).
Check out deeplearning_ensemble.py and deeplearning_ensemble_SV_multiple.py.
Example: performing deepensemble pruning on the MNIST dataset, with
$20$ base learners. The ensemble (voting classifer) is trained for$20$ epochs. The entire experiment is repeated for$3$ times.
python deeplearning_ensemble_SV_multiple.py -d MNIST -N 20 -E 20 -M 3
Executing the above command will create a diretory DeepEnsemblePruning_results and save the results (and logs and the checkpoints of the ensemble learner) therein. Base learners (e.g., "LeNet5" for MNIST) will be trained and then used to construct an ensemble learner (i.e.., the VotingClassifier class from "torchensemble"), and the pruning based on the (estimated) MSVs of the base learners is performed and the results plotted.
Need to first have computed multiple models (e.g., draw_network.py by loading the correct distances from the right directories.
If you have found our work to be useful in your research, please consider citing it:
@inproceedings{Xu2023,
author = {Xu, Xinyi and Lam, Thanh and Foo, Chuan Sheng and Low, Bryan Kian Hsiang},
booktitle = {Advances in Neural Information Processing Systems},
title = {Model {S}hapley: Equitable Model Valuation with Black-box Access},
year = {2023}
}