(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
Available Optimizers
| NumPy | Tensorflow | PyTorch |
|---|---|---|
SLSQP (scipy.optimize) |
Newton's Method (autodiff) | Newton's Method (autodiff) |
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]
or a subset of the options.
To uninstall run
pip uninstall pyhf
Authors
Please check the contribution statistics for a list of contributors
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-0.3.2.tar.gz.
File metadata
- Download URL: pyhf-0.3.2.tar.gz
- Upload date:
- Size: 6.9 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.7.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | a28114f0824c8cdd750808f4ef0f280ad3d07c0dea87072c0c3f48c5dd284323 |
|
| MD5 | fca387e3c40e8c66d6c4d51af8910411 |
|
| BLAKE2b-256 | cc7b48fb7e9df41e6ed4447360a568f14a8bc7bd751737987a00ca2f52531899 |
File details
Details for the file pyhf-0.3.2-py2.py3-none-any.whl.
File metadata
- Download URL: pyhf-0.3.2-py2.py3-none-any.whl
- Upload date:
- Size: 91.1 kB
- Tags: Python 2, Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.7.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | bc82d2a425bc7b69a94db630fae8e5a1dbf34c0273a3d5a7e3debd2531030755 |
|
| MD5 | 6162ef3444cf0f45372a3561772bea3b |
|
| BLAKE2b-256 | e60f3b53077011c9cfe89205f5d99d509ab120ae3111ecd69980ae8504e52427 |
