Symmetry Preserving Attention Networks

A library for training and evaluation SPANets on jet reconstruction tasks. Originally developed for ttbar analysis, this library now supports arbitrary event topologies and symmetry groups.

Version 2 Update

We recently pushed an updated version 2 of this library which adds several new features.

1. New configuration file format with more options on inputs and event topology.
2. Allow for several different inputs, including global inputs for additional context.
3. New Regression and Classification output heads for performing per-event or per-particle predictions.
4. Gated transformers and linear layers for more robust networks. Less hyperparameter optimization.

Installation

You can install this package to use it outside of the repository after cloning.

git clone https://github.com/Alexanders101/SPANet
cd SPANet
pip install .

Alternatively, you can use pip install -e . to install in an editable mode.

Note

The configuration ini has been deprecated into a configuration yaml. The conversion should just be to change the syntax a bit, the values should remain the same. A conversion script is in the works.

The dataset format has also changed slighty, but old style datasets can be converted into a new style dataset using utils/convert_dataset.py.

The old codebase may always be accesed here: https://github.com/Alexanders101/SPANet/tree/v1.0

Dependencies

A list of the libraries necessary to fully train and evaluate SPANets. These are only the minimum versions that we tested, other versions might work.

We have updated to using an anaconda environment for simpler dependency management. You can create the environment locally with the following conda / mamba commands:

conda env create -p ./environment --file environment.yaml
conda activate ./environment

Example

We have provided a simple ttbar example in order to demonstrate how to define events, construct datasets, and train & evaluate a network.

Refer to this page for a detailed walk-through for the ttbar example.

The full ttbar dataset may be downloaded here: http://mlphysics.ics.uci.edu/data/2021_ttbar/.

Usage

Using this library requires setting up several components. Refer to the following documentation pages in order to learn about the the different setup components, or just follow the ttbar example.

1. Defining the event topology.
2. Creating a training dataset.
3. Configuring training options.

Training

Once those steps are complete, you can begin training by calling spanet.train with your chosen parameters. For more information simply run python -m spanet.train --help

You can experiment with the provided example configuration and dataset for some ttbar events by calling python -m spanet.train -of options_files/full_hadronic_ttbar/example.json --gpus NUM_GPUS where NUM_GPUS is the number of gpus available on your machine.

Evaluation

Once training is complete, you may evalute a network on a testing dataset by running spanet.test with a path to your previously trained network and a file on which to evalute on.

For example, after running the previous training run on ttbar_example, you can evaluate the network again on the example dataset by running. python -m spanet.test ./spanet_output/version_0 -tf data/full_hadronic_ttbar/example.h5

Note that the included example file is very small and you will likely not see very good performance on it.

Citation

If you use this software for a publication, please cite the following:\

@Article{10.21468/SciPostPhys.12.5.178,
	title={{SPANet: Generalized permutationless set assignment for particle physics  using symmetry preserving attention}},
	author={Alexander Shmakov and Michael James Fenton and Ta-Wei Ho and Shih-Chieh Hsu and Daniel Whiteson and Pierre Baldi},
	journal={SciPost Phys.},
	volume={12},
	pages={178},
	year={2022},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.12.5.178},
	url={https://scipost.org/10.21468/SciPostPhys.12.5.178},
}