Major Features and Improvements

  • Refactoring of the Constraints, dividing into ProbabilityConstraint that can be sampled from and more general constraints (e.g. for parameter boundaries) that can not be sampled from.

  • Doc improvements in the constraints.

Behavioral changes

Bug fixes and small changes

Requirement changes


0.4.1 (12.1.20)

Release to keep up with TensorFlow 2.1

Major Features and Improvements

  • Fixed the comparison in caching the graph (implementation detail) that leads to an error.

0.4.0 (7.1.2020)

This release switched to TensorFlow 2.0 eager mode. In case this breaks things for you and you need urgently a running version, install a version < 0.4.1. It is highly recommended to upgrade and make the small changes required.

Please read the upgrade guide <docs/project/upgrade_guide.rst> on a more detailed explanation how to upgrade.

TensorFlow 2.0 is eager executing and uses functions to abstract the performance critical parts away.

Major Features and Improvements

  • Dependents (currently, and probably also in the future) need more manual tracking. This has mostly an effect on CompositeParameters and SimpleLoss, which now require to specify the dependents by giving the objects it depends (indirectly) on. For example, it is sufficient to give a ComplexParameter (which itself is not independent but has dependents) to a SimpleLoss as dependents (assuming the loss function depends on it).

  • ComposedParameter does no longer allow to give a Tensor but requires a function that, when evaluated, returns the value. It depends on the dependents that are now required.

  • Added numerical differentiation, which allows now to wrap any function with z.py_function (zfit.z). This can be switched on with zfit.settings.options[‘numerical_grad’] = True

  • Added gradient and hessian calculation options to the loss. Support numerical calculation as well.

  • Add caching system for graph to prevent recursive graph building

  • changed backend name to z and can be used as zfit.z or imported from it. Added:

    • function decorator that can be used to trace a function. Respects dependencies of inputs and automatically caches/invalidates the graph and recreates.

    • py_function, same as tf.py_function, but checks and may extends in the future

    • math module that contains autodiff and numerical differentiation methods, both working with tensors.

Behavioral changes

  • EDM goal of the minuit minimizer has been reduced by a factor of 10 to 10E-3 in agreement with the goal in RooFits Minuit minimizer. This can be varied by specifying the tolerance.

  • known issue: the projection_pdf has troubles with the newest TF version and may not work properly (runs out of memory)

Bug fixes and small changes

Requirement changes

  • added numdifftools (for numerical differentiation)


0.3.7 (6.12.19)

This is a legacy release to add some fixes, next release is TF 2 eager mode only release.

Major Features and Improvements

  • mostly TF 2.0 compatibility in graph mode, tests against 1.x and 2.x

Behavioral changes

Bug fixes and small changes

  • get_depentents returns now an OrderedSet

  • errordef is now a (hidden) attribute and can be changed

  • fix bug in polynomials

Requirement changes

  • added ordered-set

0.3.6 (12.10.19)

Special release for conda deployment and version fix (TF 2.0 is out)

This is the last release before breaking changes occur

Major Features and Improvements

  • added ConstantParameter and zfit.param namespace

  • Available on conda-forge

Behavioral changes

  • an implicitly created parameter with a Python numerical (e.g. when instantiating a model) will be converted to a ConstantParameter instead of a fixed Parameter and therefore cannot be set to floating later on.

Bug fixes and small changes

  • added native support TFP distributions for analytic sampling

  • fix Gaussian (TFP Distribution) Constraint with mixed up order of parameters

  • from_numpy automatically converts to default float regardless the original numpy dtype, dtype has to be used as an explicit argument

Requirement changes

  • TensorFlow >= 1.14 is required


  • Chris Burr for the conda-forge deployment

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


Bug fixes and small changes

  • fixed various small bugs


for the contribution of the Constraints to Matthieu Marinangeli <>

0.3.3 (15-05-19)

Fixed Partial numeric integration

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

  • 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


  • 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,)


  • 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.