(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.utils.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
- MXNet
Available Optimizers
| NumPy | Tensorflow | PyTorch | MxNet |
|---|---|---|---|
SLSQP (scipy.optimize) |
Newton's Method (autodiff) | Newton's Method (autodiff) | N/A |
MINUIT (iminuit) |
. | . | . |
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
pip install pyhf
and to install pyhf with additional backends run
pip install pyhf[tensorflow,torch,mxnet]
or a subset of the options.
To uninstall run
pip uninstall pyhf
Authors
Please check the contribution statistics for a list of contributors
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