(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: v0.5.1}",
version = {0.5.1},
doi = {10.5281/zenodo.1169739},
url = {https://github.com/scikit-hep/pyhf},
}Milestones
2020-07-28: 1000 GitHub issues and pull requests. (See PR #1000)
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).
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