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.
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.
| Library | Minimum Version |
|---|---|
| python | 3.8 |
| numpy | 1.19.5 |
| sympy | 1.8 |
| scikit_learn | 0.24.2 |
| pytorch | 1.8.1 |
| pytorch-lightning | 1.2.10 |
| opt_einsum | 3.3.0 |
| h5py | 2.10 |
| numba | 0.53.1 |
A docker container with python and all of these libraries already installed is available here: https://hub.docker.com/r/spanet/spanet-python
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/.
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.
Once those steps are complete, you can begin training by
calling train.py with your chosen parameters. For more information
simply run python train.py --help
You can experiment with the provided example configuration and dataset
for some ttbar events by calling
python train.py -of options_files/ttbar_example.json --gpus NUM_GPUS
where NUM_GPUS is the number of gpus available on your machine.
Once training is complete, you may evalute a network on
a testing dataset by running test.py 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 test.py ./spanet_output/version_0 -tf ./data/ttbar/ttbar_example.h5
Note that the included example file is very small and you will likely not see very good performance on it.