PAC Bayes Bounds Framework - A Toolkit for PAC-Bayes Analysis

About

The pacbb repository provides a collection of handy tools for PAC Bayes bounds evaluation. It is designed to simplify the work of PAC Bayes enthusiasts by offering various utilities and resources for easier implementation and experimentation.

We have prepared an arXiv paper that outlines the structure of pacbb, demonstrates its usage, structure, and presents a series of experiments conducted using the toolkit.

Links

• Documentation: https://fortuinlab.github.io/pacbb/core.html
• PDF: https://github.com/fortuinlab/pacbb/blob/main/doc/pacbb.pdf
• PyPI: https://pypi.org/project/pacbb/
• Source Code: https://github.com/fortuinlab/pacbb
• Issues: https://github.com/fortuinlab/pacbb/issues

Installation

To install the pacbb package, use the following command:

pip install pacbb

Example

For a complete example, please refer to the full script in scripts/generic_train.py.

Here is a part of this script showing how to convert a standard model to a Probabilistic Neural Network (ProbNN), which can be used for PAC Bayes boundaries calculation:

from core.model import dnn_to_probnn
from core.distribution import GaussianVariable
from core.distribution.utils import from_random, from_zeros

# Initialize prior
prior_reference = from_zeros(model=model, 
                             rho=torch.log(torch.exp(torch.Tensor([sigma])) - 1), 
                             distribution=GaussianVariable, 
                             requires_grad=False)

prior = from_random(model=model, 
                    rho=torch.log(torch.exp(torch.Tensor([sigma])) - 1), 
                    distribution=GaussianVariable, 
                    requires_grad=True)

# Convert the model to ProbNN
dnn_to_probnn(model, prior, prior_reference)

Distributions creation and model conversion are explained in details in the arXive paper.

Experiments

To run the experiments from the arXiv paper, follow these steps:

1. Clone the repository:

git clone https://github.com/Yauhenii/pacbb.git

2. Set up the environment:

conda create --prefix=./conda_env python=3.11 pip install -r requirements.txt

3. Create your desired experiment configuration:

./config

4. Run the configuration using the Python script directly:

export PYTHONPATH="${PYTHONPATH}:$(pwd)" 
python scripts/ivon_generic_train.py --config ./config/ivon_generic_configs/best_ivon.yaml

Alternatively, run multiple configuration files using a bash script wrapper:

export PYTHONPATH="${PYTHONPATH}:$(pwd)" 
bash jobs/runnig_ivon_configs_in_the_folder.sh ./config/ivon_generic_configs

Contribution

Contributions to pacbb are welcome! To contribute:

1. Fork the repository.
2. Create a new branch from main.
3. Make your changes.
4. Submit a pull request to the main branch.

Please use the following naming conventions for your branches:

• feature/short-description for new feature proposals.
• bugfix/short-description for bug fixes.
• experiments/short-description for changes related to the scripts module.

Acknowledgments

Special thanks to Vincent Fortuin and Alex Immer for their supervision, support, and contributions to this project. Their guidance has been invaluable throughout the development of pacbb.

Authors

• Yauhenii (Yauheni Mardan)

• maxtretiakov (Maksym Tretiakov)

Citing

If you use this code, please cite as:

@software{mardan2025pacbbframework,
  author       = {Yauheni Mardan and Maksym Tretiakov and Alexander Immer and Vincent Fortuin},               
  title        = {pacbb: PAC-Bayes Bounds Evaluation Framework},
  month        = {march},
  year         = {2025},
  doi          = {10.5281/zenodo.15082669},
  url          = {https://doi.org/10.5281/zenodo.15082670}
  howpublished = {https://github.com/fortuinlab/pacbb}
}