(partial) pure Python HistFactory implementation
Project description
pure-python fitting/limit-setting/interval estimation HistFactory-style
The HistFactory p.d.f. template [CERN-OPEN-2012-016] is per-se independent of its implementation in ROOT and sometimes, it’s useful to be able to run statistical analysis outside of ROOT, RooFit, RooStats framework.
This repo is a pure-python implementation of that statistical model for multi-bin histogram-based analysis and its interval estimation is based on the asymptotic formulas of “Asymptotic formulae for likelihood-based tests of new physics” [arXiv:1007.1727]. The aim is also to support modern computational graph libraries such as PyTorch and TensorFlow in order to make use of features such as autodifferentiation and GPU acceleration.
Hello World
>>> import pyhf
>>> pdf = pyhf.simplemodels.hepdata_like(signal_data=[12.0, 11.0], bkg_data=[50.0, 52.0], bkg_uncerts=[3.0, 7.0])
>>> CLs_obs, CLs_exp = pyhf.infer.hypotest(1.0, [51, 48] + pdf.config.auxdata, pdf, return_expected=True)
>>> print('Observed: {}, Expected: {}'.format(CLs_obs, CLs_exp))
Observed: [0.05290116], Expected: [0.06445521]What does it support
- Implemented variations:
☑ HistoSys
☑ OverallSys
☑ ShapeSys
☑ NormFactor
☑ Multiple Channels
☑ Import from XML + ROOT via uproot
☑ ShapeFactor
☑ StatError
☑ Lumi Uncertainty
- Computational Backends:
☑ NumPy
☑ PyTorch
☑ TensorFlow
☑ JAX
- Optimizers:
☑ SciPy (scipy.optimize)
☑ MINUIT (iminuit)
All backends can be used in combination with all optimizers. Custom user backends and optimizers can be used as well.
Todo
☐ StatConfig
☐ Non-asymptotic calculators
results obtained from this package are validated against output computed from HistFactory workspaces
A one bin example
nobs = 55, b = 50, db = 7, nom_sig = 10.
A two bin example
bin 1: nobs = 100, b = 100, db = 15., nom_sig = 30.
bin 2: nobs = 145, b = 150, db = 20., nom_sig = 45.
Installation
To install pyhf from PyPI with the NumPy backend run
python -m pip install pyhfand to install pyhf with all additional backends run
python -m pip install pyhf[backends]or a subset of the options.
To uninstall run
python -m pip uninstall pyhfQuestions
If you have a question about the use of pyhf not covered in the documentation, please ask a question on Stack Overflow with the [pyhf] tag, which the pyhf dev team watches.
If you believe you have found a bug in pyhf, please report it in the GitHub Issues.
Citation
As noted in Use and Citations, the preferred BibTeX entry for citation of pyhf is
@software{pyhf,
author = "{Heinrich, Lukas and Feickert, Matthew and Stark, Giordon}",
title = "{pyhf: v0.5.0}",
version = {0.5.0},
doi = {10.5281/zenodo.1169739},
url = {https://github.com/scikit-hep/pyhf},
}Acknowledgements
Matthew Feickert has received support to work on pyhf provided by NSF cooperative agreement OAC-1836650 (IRIS-HEP) and grant OAC-1450377 (DIANA/HEP).
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file pyhf-0.5.0.tar.gz.
File metadata
- Download URL: pyhf-0.5.0.tar.gz
- Upload date:
- Size: 86.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.2.0 requests-toolbelt/0.9.1 tqdm/4.48.0 CPython/3.8.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 36a323e29ae5bdabc64201fa27a36b2e63df1da5d218c7463bda233c38cf5709 |
|
| MD5 | 355861f33d40c5bcb0dff8b7d4b648e6 |
|
| BLAKE2b-256 | 980cdf9e85c50318e29b2a0b195829be9104161a9f95ae8f086d17e97da5b842 |
File details
Details for the file pyhf-0.5.0-py2.py3-none-any.whl.
File metadata
- Download URL: pyhf-0.5.0-py2.py3-none-any.whl
- Upload date:
- Size: 114.0 kB
- Tags: Python 2, Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.2.0 requests-toolbelt/0.9.1 tqdm/4.48.0 CPython/3.8.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | ff0573517eb0d3c0d4ead295829393fa3829977d5fff4bbeccca7fa5a236be30 |
|
| MD5 | 2724d6bcb1b021b55cb884da22814f2f |
|
| BLAKE2b-256 | cdc99b29e6c436503db1627a839478db92b8a9525881267b09b1ac0fe845cc5d |
