zfit 0.3.5

scalable pythonic model fitting for high energy physics

zfit: scalable pythonic fitting

zfit is a highly scalable and customizable model manipulation and fitting library. It uses TensorFlow as its computational backend and is optimised for simple and direct manipulation of probability density functions.

• Tutorials: Interactive IPython Tutorials

• Quick start: Example scripts

• Documentation: Full documentation and API

• Questions: see the FAQ, ask on StackOverflow with the zfit tag or contact us directly.

• Physics/HEP: zfit-physics is the place to contribute/find more HEP related content

If you use zfit in research, please consider citing.

N.B.: zfit is currently in beta stage, so while most core parts are established, some may still be missing and bugs may be encountered. It is, however, mostly ready for production, and is being used in analyses projects. If you want to use it for your project and you are not sure if all the needed functionality is there, feel free to contact.

Why?

The basic idea behind zfit is to offer a Python oriented alternative to the very successful RooFit library from the ROOT data analysis package that can integrate with the other packages that are part if the scientific Python ecosystem. Contrary to the monolithic approach of ROOT/RooFit, the aim of zfit is to be light and flexible enough to integrate with any state-of-art tools and to allow scalability going to larger datasets.

These core ideas are supported by two basic pillars:

• The skeleton and extension of the code is minimalist, simple and finite: the zfit library is exclusively designed for the purpose of model fitting and sampling with no attempt to extend its functionalities to features such as statistical methods or plotting.

• zfit is designed for optimal parallelisation and scalability by making use of TensorFlow as its backend. The use of TensorFlow provides crucial features in the context of model fitting like taking care of the parallelisation and analytic derivatives.

How to use

While the zfit library provides a model fitting and sampling framework for a broad list of applications, we will illustrate its main features with a simple example by fitting a Gaussian distribution with an unbinned likelihood fit and a parameter uncertainty estimation.

Example in short

obs = zfit.Space('x', limits=(-10, 10))

# create the model
mu    = zfit.Parameter("mu"   , 2.4, -1, 5)
sigma = zfit.Parameter("sigma", 1.3,  0, 5)
gauss = zfit.pdf.Gauss(obs=obs, mu=mu, sigma=sigma)

# load the data
data_np = np.random.normal(size=10000)
data = zfit.Data.from_numpy(obs=obs, array=data_np)

# build the loss
nll = zfit.loss.UnbinnedNLL(model=gauss, data=data)

# minimize
minimizer = zfit.minimize.Minuit()
result = minimizer.minimize(nll)

# calculate errors
param_errors = result.error()

This follows the zfit workflow

Full explanation

The default space (e.g. normalization range) of a PDF is defined by an observable space, which is created using the zfit.Space class:

obs = zfit.Space('x', limits=(-10, 10))

To create a simple Gaussian PDF, we define its parameters and their limits using the zfit.Parameter class.

# syntax: zfit.Parameter("any_name", value, lower, upper)
  mu    = zfit.Parameter("mu"   , 2.4, -1, 5)
  sigma = zfit.Parameter("sigma", 1.3,  0, 5)
  gauss = zfit.pdf.Gauss(obs=obs, mu=mu, sigma=sigma)

For simplicity, we create the dataset to be fitted starting from a numpy array, but zfit allows for the use of other sources such as ROOT files:

mu_true = 0
sigma_true = 1
data_np = np.random.normal(mu_true, sigma_true, size=10000)
data = zfit.Data.from_numpy(obs=obs, array=data_np)

Fits are performed in three steps:

1. Creation of a loss function, in our case a negative log-likelihood.

2. Instantiation of our minimiser of choice, in the example the Minuit.

3. Minimisation of the loss function.

# Stage 1: create an unbinned likelihood with the given PDF and dataset
nll = zfit.loss.UnbinnedNLL(model=gauss, data=data)

# Stage 2: instantiate a minimiser (in this case a basic minuit)
minimizer = zfit.minimize.Minuit()

# Stage 3: minimise the given negative log-likelihood
result = minimizer.minimize(nll)

Errors are calculated with a further function call to avoid running potentially expensive operations if not needed:

param_errors = result.error()

Once we’ve performed the fit and obtained the corresponding uncertainties, we can examine the fit results:

print("Function minimum:", result.fmin)
print("Converged:", result.converged)
print("Full minimizer information:", result.info)

# Information on all the parameters in the fit
params = result.params
print(params)

# Printing information on specific parameters, e.g. mu
print("mu={}".format(params[mu]['value']))

And that’s it! For more details and information of what you can do with zfit, checkout the documentation.

Prerequisites

zfit works with Python versions 3.6 and 3.7. The following packages (amongst others) are required:

• tensorflow >= 1.10.0

• tensorflow_probability >= 0.3.0

• scipy >=1.2

• uproot

• iminuit

… and some minor packages. For a full list, check the requirements.

Installing

To install zfit, run this command in your terminal:

$ pip install zfit

For the newest development version, you can install the version from git with

$ pip install git+https://github.com/zfit/zfit

Contributing

Any idea of how to improve the library? Or interested to write some code? Contributions are always welcome, please have a look at the Contributing guide.

Contact

You can contact us directly:

• via e-mail: [e-mail]

• join our Gitter channel

Main Authors

Jonas Eschle <[e-mail]>

Albert Puig <[e-mail]>

Rafael Silva Coutinho <[e-mail]>

Acknowledgements

zfit has been developed with support from the University of Zürich and the Swiss National Science Foundation (SNSF) under contracts 168169 and 174182.

The idea of zfit is inspired by the TensorFlowAnalysis framework developed by Anton Poluektov using the TensorFlow open source library.

Changelog

0.3.4 (30-07-19)

This is the last release before breaking changes occur

Major Features and Improvements

• create Constraint class which allows for more fine grained control and information on the applied constraints.

• Added Polynomial models

• Improved and fixed sampling (can still be slightly biased)

Behavioral changes

None

Bug fixes and other changes

• fixed various small bugs

Thanks

for the contribution of the Constraints to Matthieu Marinangeli <[e-mail]>

0.3.3 (15-05-19)

Fixed Partial numeric integration

Bugfixes mostly, a few major fixes. Partial numeric integration works now.

Bugfixes

• data_range cuts are now applied correctly, also in several dimensions when a subset is selected (which happens internally of some Functors, e.g. ProductPDF). Before, only the selected obs was respected for cuts.

• parital integration had a wrong take on checking limits (now uses supports).

0.3.2 (01-05-19)

With 0.3.2, bugfixes and three changes in the API/behavior

Breaking changes

• tfp distributions wrapping is now different with dist_kwargs allowing for non-Parameter arguments (like other dists)

• sampling allows now for importance sampling (sampler in Model specified differently)

• model.sample now also returns a tensor, being consistent with pdf and integrate

Bugfixes

• shape handling of tfp dists was “wrong” (though not producing wrong results!), fixed. TFP distributions now get a tensor with shape (nevents, nobs) instead of a list of tensors with (nevents,)

Improvements

• refactor the sampling for more flexibility and performance (less graph constructed)

• allow to use more sophisticated importance sampling (e.g. phasespace)

• on-the-fly normalization (experimentally) implemented with correct gradient

0.3.1 (30-04-19)

Minor improvements and bugfixes including:

• improved importance sampling allowing to preinstantiate objects before it’s called inside the while loop

• fixing a problem with ztf.sqrt

0.3.0 (2019-03-20)

Beta stage and first pip release

0.0.1 (2018-03-22)

• First creation of the package.