https://github.com/gilesstrong/pytorch_inferno
PyTorch implementation of inference aware neural optimisation (de Castro and Dorigo, 2018 https://www.sciencedirect.com/science/article/pii/S0010465519301948)
https://github.com/gilesstrong/pytorch_inferno
inferno likelihood-free-inference neural-networks pytorch statistical-inference
Last synced: 3 months ago
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PyTorch implementation of inference aware neural optimisation (de Castro and Dorigo, 2018 https://www.sciencedirect.com/science/article/pii/S0010465519301948)
- Host: GitHub
- URL: https://github.com/gilesstrong/pytorch_inferno
- Owner: GilesStrong
- License: apache-2.0
- Created: 2020-09-29T13:02:27.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-04-12T06:06:10.000Z (over 2 years ago)
- Last Synced: 2024-07-23T14:38:10.510Z (about 1 year ago)
- Topics: inferno, likelihood-free-inference, neural-networks, pytorch, statistical-inference
- Language: Jupyter Notebook
- Homepage: https://gilesstrong.github.io/pytorch_inferno/
- Size: 30.8 MB
- Stars: 4
- Watchers: 3
- Forks: 1
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.md
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README
# Title
[](https://pypi.python.org/pypi/pytorch_inferno)
[](https://pypi.python.org/pypi/pytorch_inferno) [](https://pypi.python.org/pypi/pytorch_inferno)
[](https://github.com/GilesStrong/pytorch_inferno/actions)
[](https://doi.org/10.5281/zenodo.4597140)
# PyTorch INFERNO
Documentation: https://gilesstrong.github.io/pytorch_inferno/
This package provides a PyTorch implementation of INFERNO ([de Castro and Dorigo, 2018](https://www.sciencedirect.com/science/article/pii/S0010465519301948)), along with a minimal high-level wrapper for training and applying PyTorch models, and running statistical inference of parameters of interest in the presence of nuisance parameters. INFERNO is implemented in the form of a callback, allowing it to be dropped in and swapped out with heavy rewriting of code.
For a presentation of the package, check out my talk at PyHEP 2021: https://www.youtube.com/watch?v=copNcyHnHBs (slides here: https://github.com/GilesStrong/talk_pyhep21_pytorch_inferno)
For a deeper overview of the package, a breakdown of the INFERNO algorithm, and an introduction to parameter inference in HEP, I have written a 5-post blog series: https://gilesstrong.github.io/website/statistics/hep/inferno/2020/12/04/inferno-1.html
The authors' Tensorflow 1 code may be found here: https://github.com/pablodecm/paper-inferno
And Lukas Layer's Tenforflow 2 version may be found here: https://github.com/llayer/inferno
For a talk and tutorial on PyTorch INFERNO, please see https://github.com/GilesStrong/talk_pyhep21_pytorch_inferno, and https://youtu.be/5aWAxvdrszw?t=13543 for the YouTube recording.
### User install
```
pip install pytorch_inferno
```
### Developer install
```
[install torch>=1.7 according to CUDA version]
pip install nbdev fastcore numpy pandas fastprogress matplotlib>=3.0.0 seaborn scipy
git clone git@github.com:GilesStrong/pytorch_inferno.git
cd pytorch_inferno
pip install -e .
nbdev_install_git_hooks
```
## Overview
Library developed and testing in `nbs` directory.
Experiments run in `experiments` directory.
Use `nbdev_build_lib` to export code to library located in `pytorch_inferno`. This overwrites any changes in `pytorch_inferno`, i.e. only edit the notebooks.
## Results
This package has been tested against the paper problem and reproduces its results within uncertainty
## Reference
If you have used this implementation of INFERNO in your analysis work and wish to cite it, the preferred reference is: *Giles C. Strong, pytorch_inferno, Zenodo (Mar. 2021), http://doi.org/10.5281/zenodo.4597140, Note: Please check https://github.com/GilesStrong/pytorch_inferno/graphs/contributors for the full list of contributors*
```
@misc{giles_chatham_strong_2021_5040810,
author = {Giles Chatham Strong},
title = {pytorch\_inferno},
month = jun,
year = 2021,
note = {{Please check https://github.com/GilesStrong/pytorch_inferno/graphs/contributors for the full list of contributors}},
doi = {10.5281/zenodo.4597140},
url = {https://doi.org/10.5281/zenodo.4597140}
}
```
The INFERNO algorithm should also be cited:
```
@article{DECASTRO2019170,
title = {INFERNO: Inference-Aware Neural Optimisation},
journal = {Computer Physics Communications},
volume = {244},
pages = {170-179},
year = {2019},
issn = {0010-4655},
doi = {https://doi.org/10.1016/j.cpc.2019.06.007},
url = {https://www.sciencedirect.com/science/article/pii/S0010465519301948},
author = {Pablo {de Castro} and Tommaso Dorigo},
}
```