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https://github.com/Rupt/paper-which-is-real
Code and data to reproduce content for "A method to challenge symmetries in data with self-supervised learning" (https://arxiv.org/abs/2111.05442)
https://github.com/Rupt/paper-which-is-real
machine-learning physics symmetry
Last synced: 14 days ago
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Code and data to reproduce content for "A method to challenge symmetries in data with self-supervised learning" (https://arxiv.org/abs/2111.05442)
- Host: GitHub
- URL: https://github.com/Rupt/paper-which-is-real
- Owner: Rupt
- License: mit
- Archived: true
- Created: 2021-11-05T22:38:33.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-10-01T09:37:46.000Z (about 1 year ago)
- Last Synced: 2024-08-01T16:46:24.768Z (3 months ago)
- Topics: machine-learning, physics, symmetry
- Language: Jupyter Notebook
- Homepage: https://doi.org/10.1088/1748-0221/17/08/P08024
- Size: 3.44 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Supporting code for:
A method to challenge symmetries in data with self-supervised learning
- JINST: https://doi.org/10.1088/1748-0221/17/08/P08024
- arXiv: https://arxiv.org/abs/2111.05442
- Zenodo: https://doi.org/10.5281/zenodo.6861702
Test symmetries with sklearn decision tree models.
# Setup
Begin from an environment with a recent version of python 3. \
We recommend using anaconda:
```bash
conda create -n which-is-real python==3.10.4
```
followed by
```bash
conda activate which-is-real
```
Install and activate a python virtual environment with our required packages:
```bash
source setup.sh
```
You can leave the environment with `deactivate`. \
To clean up fully, remove the installation directory `env/`.
# Run experiments
We include scripts to reproduce all experiments from the paper and a few others. \
To run all, execute:
```bash
make
```
This dumps plots into the various directories `example_*/`.
We describe each in a little more detail below.
## Experiment 1: Gappy detector
The cylindrical particle detector with holes and varying efficiencies. \
In the paper, this is illustrated in Figures 2 and 3, and discussed in Section 5.
Notebook version: `example_ring_paper.ipynb`. \
You should be able to view this on GitHub with all figures and other outputs.
Execute the plain python version with:
```bash
make example_ring_paper.log
```
This reproduces plots from the paper and dumps them into the directory `example_ring_paper/`. \
## Experiment 2: Height map
The topographical map from Figure 4 and Section 6 of the paper.
Notebook version: `example_map_paper.ipynb`. \
You should be able to view this on GitHub with all figures and other outputs.
Execute the plain python version with:
```bash
make example_map_paper.log
```
This reproduces plots from the paper and dumps them into the directory `example_map_paper/`.
## Bonus experiments:
* `make example_ring.log`
* Similar to Experiment 1, plotted in 2D, showing both real and fake (transformed) data.
* Plots dump to `example_ring/`.
* `make example_map.log`
* Similar to Experiment 2, with more variety in its filtering symmetry-breaking wave.
* Plots dump to `example_map/`.
* `make example_step.log`
* A minimal example testing translation symmetry on the unit interval.
* Uses rejection sampling for fakes.
* Plots dump to `example_step/`.
These `make` commands run corresponding `example_*.py` scripts
after ensuring that input data are prepared.