Metadata-Version: 2.1
Name: pytorch_inferno
Version: 0.2.1
Summary: PyTorch Implementation of INFERNO
Home-page: https://github.com/GilesStrong/pytorch_inferno/tree/master/
Author: Giles Strong
Author-email: giles.strong@outlook.com
License: Apache Software License 2.0
Description: 
        # Title
        
        
        
        [![pypi pytorch_inferno version](https://img.shields.io/pypi/v/pytorch_inferno.svg)](https://pypi.python.org/pypi/pytorch_inferno)
        [![pytorch_inferno python compatibility](https://img.shields.io/pypi/pyversions/pytorch_inferno.svg)](https://pypi.python.org/pypi/pytorch_inferno) [![pytorch_inferno license](https://img.shields.io/pypi/l/pytorch_inferno.svg)](https://pypi.python.org/pypi/pytorch_inferno)
        [![CI](https://github.com/GilesStrong/pytorch_inferno/actions/workflows/main.yml/badge.svg)](https://github.com/GilesStrong/pytorch_inferno/actions)
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4597140.svg)](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 an 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
        
        ### 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
        ![title](nbs/imgs/results.png)
        
        ## 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_4597140,  
          author       = {Giles Chatham Strong},  
          title        = {LUMIN},  
          month        = mar,  
          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},
        }
        ```
        
Keywords: INFERNO,HEP,inference
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
