zfit is a highly scalable and customizable model manipulation and likelihood fitting library. It uses the same computational backend as TensorFlow <https://www.tensorflow.org/>_ and is optimised for simple and direct manipulation of probability density functions. The project is affiliated with and well integrated into Scikit-HEP <https://scikit-hep.org/>_, the HEP Python ecosystem.

Tutorials: Interactive introduction and tutorials <https://zfit-tutorials.readthedocs.io/en/latest/>_
Quick start: Example scripts <examples>_
Documentation: See stable documentation_ or latest documentation_
Questions: see the FAQ <https://github.com/zfit/zfit/wiki/FAQ>, ask on StackOverflow <https://stackoverflow.com/questions/ask?tags=zfit> (with the zfit tag) or contact_ us directly.
Physics, HEP: zfit-physics <https://github.com/zfit/zfit-physics>_ is the place to contribute and find more HEP related content
Statistical inference: hepstats <https://github.com/scikit-hep/hepstats>_ for limits, CI, sWeights and more

If you use zfit in research, please consider citing <https://www.sciencedirect.com/science/article/pii/S2352711019303851>_.

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_ us.

Installation

zfit is available on pip and conda-forge. To install it (recommended: use a virtual/conda env!) with all the dependencies (minimizers, uproot, ...), use

.. code-block:: bash

pip install -U zfit[all]

(the -U just indicates to upgrade zfit, in case you have it already installed) or for minimal dependencies

.. code-block:: bash

pip install zfit

For conda/mamba, use

.. code-block:: bash

conda install -c conda-forge zfit

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

.. code-block:: python

obs = zfit.Space('x', -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(obs=obs, data=data_np)
# or sample from model
data = gauss.sample()

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

# minimize (20+ interchangeable minimizers available!)
minimizer = zfit.minimize.Minuit()
result = minimizer.minimize(nll).update_params()

# calculate errors
sym_errors = result.hesse()
asym_errors = result.errors()

This follows the zfit workflow

.. image:: docs/images/zfit_workflow_v2.png :alt: 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:

.. code-block:: python

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

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

.. code-block:: python

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:

.. code-block:: python

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

Fits are performed in three steps:

Creation of a loss function, in our case a negative log-likelihood.
Instantiation of our minimiser of choice, in the example the Minuit.
Minimisation of the loss function.

.. code-block:: python

# 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).update_params()

The .update_params() changes the default values of the parameters (this is currently happen by default but won't anymore in the future)

Symmetric errors are calculated with a further function call to avoid running potentially expensive operations if not needed. Asymmetric errors using a profiling method can also be obtained:

.. code-block:: python

sym_errors = result.hesse()
asym_errors = result.errors()

Once we've performed the fit and obtained the corresponding uncertainties, we can examine the fit results by printing it or looking at individual parts

.. code-block:: python

print(result)  # nice representation of a whole result

print("Function minimum:", result.fmin)
print("Converged:", result.converged)

# 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 latest documentation_.

Why?

The basic idea behind zfit is to offer a Python oriented alternative to the very successful RooFit library from the ROOT <https://root.cern.ch/>_ 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 t o 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.

Prerequisites

zfit works with Python versions 3.9 and above. The main dependency is tensorflow <https://www.tensorflow.org/>_: zfit follows a close version compatibility with TensorFlow.

For a full list of all dependencies, check the requirements <requirements.txt>_.

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_.

.. _Contributing guide: CONTRIBUTING.rst

Contact

You can contact us directly:

via e-mail: zfit@physik.uzh.ch
join our Gitter channel <https://gitter.im/zfit/zfit>_

Original Authors

| Jonas Eschle jonas.eschle@cern.ch | Albert Puig albert.puig@cern.ch | Rafael Silva Coutinho rsilvaco@cern.ch

See here for all authors and contributors_

.. _all authors and contributors: AUTHORS.rst

Acknowledgements

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

The idea of zfit is inspired by the TensorFlowAnalysis <https://gitlab.cern.ch/poluekt/TensorFlowAnalysis>_ framework developed by Anton Poluektov and TensorProb <https://github.com/tensorprob/tensorprob>_ by Chris Burr and Igor Babuschkin using the TensorFlow open source library and more libraries.

.. _latest documentation: https://zfit.readthedocs.io/en/latest/ .. _stable documentation: https://zfit.readthedocs.io/en/stable/

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