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.
For an in depth walkthrough of usage of the latest release of pyhf visit the pyhf tutorial.
This is how you use the pyhf Python API to build a statistical model and run basic inference:
>>> import pyhf
>>> pyhf.set_backend("numpy")
>>> model = pyhf.simplemodels.uncorrelated_background(
... signal=[12.0, 11.0], bkg=[50.0, 52.0], bkg_uncertainty=[3.0, 7.0]
... )
>>> data = [51, 48] + model.config.auxdata
>>> test_mu = 1.0
>>> CLs_obs, CLs_exp = pyhf.infer.hypotest(
... test_mu, data, model, test_stat="qtilde", return_expected=True
... )
>>> print(f"Observed: {CLs_obs:.8f}, Expected: {CLs_exp:.8f}")
Observed: 0.05251497, Expected: 0.06445321
Alternatively the statistical model and observational data can be read from its serialized JSON representation (see next section).
>>> import pyhf
>>> import requests
>>> pyhf.set_backend("numpy")
>>> url = "https://raw.githubusercontent.com/scikit-hep/pyhf/main/docs/examples/json/2-bin_1-channel.json"
>>> wspace = pyhf.Workspace(requests.get(url).json())
>>> model = wspace.model()
>>> data = wspace.data(model)
>>> test_mu = 1.0
>>> CLs_obs, CLs_exp = pyhf.infer.hypotest(
... test_mu, data, model, test_stat="qtilde", return_expected=True
... )
>>> print(f"Observed: {CLs_obs:.8f}, Expected: {CLs_exp:.8f}")
Observed: 0.35998409, Expected: 0.35998409
Finally, you can also use the command line interface that pyhf provides
$ cat << EOF | tee likelihood.json | pyhf cls
{
"channels": [
{ "name": "singlechannel",
"samples": [
{ "name": "signal",
"data": [12.0, 11.0],
"modifiers": [ { "name": "mu", "type": "normfactor", "data": null} ]
},
{ "name": "background",
"data": [50.0, 52.0],
"modifiers": [ {"name": "uncorr_bkguncrt", "type": "shapesys", "data": [3.0, 7.0]} ]
}
]
}
],
"observations": [
{ "name": "singlechannel", "data": [51.0, 48.0] }
],
"measurements": [
{ "name": "Measurement", "config": {"poi": "mu", "parameters": []} }
],
"version": "1.0.0"
}
EOF
which should produce the following JSON output:
{
"CLs_exp": [
0.0026062609501074576,
0.01382005356161206,
0.06445320535890459,
0.23525643861460702,
0.573036205919389
],
"CLs_obs": 0.05251497423736956
}
scipy.optimize)iminuit)All backends can be used in combination with all optimizers. Custom user backends and optimizers can be used as well.
results obtained from this package are validated against output computed from HistFactory workspaces
import pyhf
import numpy as np
import matplotlib.pyplot as plt
from pyhf.contrib.viz import brazil
pyhf.set_backend("numpy")
model = pyhf.simplemodels.uncorrelated_background(
signal=[10.0], bkg=[50.0], bkg_uncertainty=[7.0]
)
data = [55.0] + model.config.auxdata
poi_vals = np.linspace(0, 5, 41)
results = [
pyhf.infer.hypotest(
test_poi, data, model, test_stat="qtilde", return_expected_set=True
)
for test_poi in poi_vals
]
fig, ax = plt.subplots()
fig.set_size_inches(7, 5)
brazil.plot_results(poi_vals, results, ax=ax)
fig.show()
pyhf
ROOT
import pyhf
import numpy as np
import matplotlib.pyplot as plt
from pyhf.contrib.viz import brazil
pyhf.set_backend("numpy")
model = pyhf.simplemodels.uncorrelated_background(
signal=[30.0, 45.0], bkg=[100.0, 150.0], bkg_uncertainty=[15.0, 20.0]
)
data = [100.0, 145.0] + model.config.auxdata
poi_vals = np.linspace(0, 5, 41)
results = [
pyhf.infer.hypotest(
test_poi, data, model, test_stat="qtilde", return_expected_set=True
)
for test_poi in poi_vals
]
fig, ax = plt.subplots()
fig.set_size_inches(7, 5)
brazil.plot_results(poi_vals, results, ax=ax)
fig.show()
pyhf
ROOT
To install pyhf from PyPI with the NumPy backend run
python -m pip install pyhf
and 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 pyhf
For model specification, API reference, examples, and answers to FAQs visit the pyhf documentation.
If you have a question about the use of pyhf not covered in the documentation, please ask a question on the GitHub Discussions.
If you believe you have found a bug in pyhf, please report it in the GitHub Issues. If you're interested in getting updates from the pyhf dev team and release announcements you can join the pyhf-announcements mailing list.
As noted in Use and Citations, the preferred BibTeX entry for citation of pyhf includes both the Zenodo archive and the JOSS paper:
@software{pyhf,
author = {Lukas Heinrich and Matthew Feickert and Giordon Stark},
title = "{pyhf: v0.7.6}",
version = {0.7.6},
doi = {10.5281/zenodo.1169739},
url = {https://doi.org/10.5281/zenodo.1169739},
note = {https://github.com/scikit-hep/pyhf/releases/tag/v0.7.6}
}
@article{pyhf_joss,
doi = {10.21105/joss.02823},
url = {https://doi.org/10.21105/joss.02823},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {58},
pages = {2823},
author = {Lukas Heinrich and Matthew Feickert and Giordon Stark and Kyle Cranmer},
title = {pyhf: pure-Python implementation of HistFactory statistical models},
journal = {Journal of Open Source Software}
}
pyhf is openly developed by Lukas Heinrich, Matthew Feickert, and Giordon Stark.
Please check the contribution statistics for a list of contributors.
Matthew Feickert has received support to work on pyhf provided by NSF cooperative agreements OAC-1836650 and PHY-2323298 (IRIS-HEP) and grant OAC-1450377 (DIANA/HEP).
pyhf is a NumFOCUS Affiliated Project.
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