# 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:

[x] HistoSys

[x] OverallSys

[x] ShapeSys

[x] NormFactor

[x] Multiple Channels

[x] Import from XML + ROOT via uproot

[x] ShapeFactor

[x] StatError

[x] Lumi Uncertainty

Computational Backends:

[x] NumPy

[x] PyTorch

[x] TensorFlow

([x]) MXNet (MXNet support is experimental)

Available Optimizers

NumPy |
Tensorflow |
PyTorch |
MxNet |
---|---|---|---|

SLSQP ( |
Newton’s Method (autodiff) |
Newton’s Method (autodiff) |
N/A |

MINUIT ( |
. |
. |
. |

## Todo¶

[ ] StatConfig

[ ] Non-asymptotic calculators

results obtained from this package are validated against output computed from HistFactory workspaces

## 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