Changelog¶

Develop¶

Major Features and Improvements¶

Breaking changes¶

Depreceations¶

Bug fixes and small changes¶

Experimental¶

Requirement changes¶

remove Python 3.6 support

Thanks¶

0.6.2¶

Minor small fixes.

Bug fixes and small changes¶

add loss to callback signature that gives full access to the model
add create_new() to losses in order to re-instantiate them with new models and data preserving their current (and future) options and other arguments

0.6.1 (31.03.2021)¶

Release for fix of minimizers that performed too bad

Breaking changes¶

remove badly performing Scipy minimizers ScipyTrustKrylovV1 and ScipyTrustNCGV1

Bug fixes and small changes¶

fix auto conversion to complex parameter using constructor

0.6.0 (30.3.2021)¶

Added many new minimizers from different libraries, all with uncertainty estimation available.

Major Features and Improvements¶

upgraded to TensorFlow 2.4
Added many new minimizers. A full list can be found in Minimize.
- IpyoptV1 that wraps the powerful Ipopt large scale minimization library
- Scipy minimizers now have their own, dedicated wrapper for each instance such as ScipyLBFGSBV1, or ScipySLSQPV1
- NLopt library wrapper that contains many algorithms for local searches such as NLoptLBFGSV1, NLoptTruncNewtonV1 or NLoptMMAV1 but also includes more global minimizers such as NLoptMLSLV1 and NLoptESCHV1.
Completely new and overhauled minimizers design, including:
- minimizers can now be used with arbitrary Python functions and an initial array independent of zfit
- a minimization can be ‘continued’ by passing init to minimize
- more streamlined arguments for minimizers, harmonized names and behavior.
- Adding a flexible criterion (currently EDM) that will terminate the minimization.
- Making the minimizer fully stateless.
- Moving the loss evaluation and strategy into a LossEval that simplifies the handling of printing and NaNs.
- Callbacks are added to the strategy.
Major overhaul of the FitResult, including:
- improved zfit_error (equivalent of MINOS)
- minuit_hesse and minuit_minos are now available with all minimizers as well thanks to an great improvement in iminuit.
- Added an approx hesse that returns the approximate hessian (if available, otherwise empty)
upgrade to iminuit v2 changes the way it works and also the Minuit minimizer in zfit, including a new step size heuristic. Possible problems can be caused by iminuit itself, please report in case your fits don’t converge anymore.
improved compute_errors in speed by caching values and the reliability by making the solution unique.
increased stability for large datasets with a constant subtraction in the NLL

Breaking changes¶

NLL (and extended) subtracts now by default a constant value. This can be changed with a new options argument. COMPARISON OF DIFFEREN NLLs (their absolute values) fails now! (flag can be deactivated)
BFGS (from TensorFlow Probability) has been removed as it is not working properly. There are many alternatives such as ScipyLBFGSV1 or NLoptLBFGSV1
Scipy (the minimizer) has been removed. Use specialized Scipy* minimizers instead.
Creating a zfit.Parameter, usign set_value or set_values now raises a ValueError if the value is outside the limits. Use assign to suppress it.

Depreceations¶

strategy to minimizer should now be a class, not an instance anymore.

Bug fixes and small changes¶

zfit_error moved only one parameter to the correct initial position. Speedup and more reliable.
FFTconv was shifted if the kernel limits were not symetrical, now properly taken into account.
circumvent overflow error in sampling
shuffle samples from sum pdfs to ensure uniformity and remove conv sampling bias
create_sampler now samples immediately to allow for precompile, a new hook that will allow objects to optimize themselves.

Requirement changes¶

ipyopt
nlopt
iminuit>=2.3
tensorflow ~= 2.4
tensorflow-probability~=12

For devs: - pre-commit - pyyaml - docformatter

Thanks¶

Hans Dembinski for the help on upgrade to imituit V2
Thibaud Humair for helpful remarks on the parameters

0.5.6 (26.1.2020)¶

Update to fix iminuit version

Bug fixes and small changes¶

Fix issue when using a ComposedParameter as the rate argument of a Poisson PDF

Requirement changes¶

require iminuit < 2 to avoid breaking changes

0.5.5 (20.10.2020)¶

Upgrade to TensorFlow 2.3 and support for weighted hessian error estimation.

Added a one dimensional Convolution PDF

Major Features and Improvements¶

upgrad to TensorFlow 2.3
new Poisson PDF

Breaking changes¶

Depreceations¶

Bug fixes and small changes¶

print parameter inside function context works now correctly

Experimental¶

Computation of the covariance matrix and hessian errors with weighted data
Convolution PDF (FFT in 1Dim) added (experimental, feedback welcome!)

Requirement changes¶

TensorFlow==2.3 (before 2.2)
tensorflow_probability==0.11
tensorflow-addons # spline interpolation in convolution

Thanks¶

0.5.4 (16.07.2020)¶

Major Features and Improvements¶

completely new doc design

Breaking changes¶

Minuit uses its own, internal gradient by default. To change this back, use use_minuit_grad=False
minimize(params=...) now filters correctly non-floating parameters.
z.log has been moved to z.math.log (following TF)

Depreceations¶

Bug fixes and small changes¶

ncalls is not correctly using the internal heuristc or the ncalls explicitly
minimize(params=...) automatically extracts independent parameters.
fix copy issue of KDEV1 and change name to ‘adaptive’ (instead of ‘adaptiveV1’)
change exp name of lambda_ to lam (in init)
add set_yield to BasePDF to allow setting the yield in place
Fix possible bug in SumPDF with extended pdfs (automatically)

Experimental¶

Requirement changes¶

upgrade to iminuit>=1.4
remove cloudpickle hack fix

Thanks¶

Johannes for the docs re-design

0.5.3 (02.07.20)¶

Kernel density estimation for 1 dimension.

Major Features and Improvements¶

add correlation method to FitResult
Gaussian (Truncated) Kernel Density Estimation in one dimension zfit.pdf.GaussianKDE1DimV1 implementation with fixed and adaptive bandwidth added as V1. This is a feature that needs to be improved and feedback is welcome
Non-relativistic Breit-Wigner PDF, called Cauchy, implementation added.

Breaking changes¶

change human-readable name of Gauss, Uniform and TruncatedGauss to remove the '_tfp' at the end of the name

Bug fixes and small changes¶

fix color wrong in printout of results, params
packaging: moved to pyproject.toml and a setup.cfg mainly, development requirements can be installed with the dev extra as (e.g.) pip install zfit[dev]
Fix shape issue in TFP distributions for partial integration
change zfit internal algorithm (zfit_error) to compute error/intervals from the profile likelihood, which is 2-3 times faster than previous algorithm.
add from_minuit constructor to FitResult allowing to create it when using directly iminuit
fix possible bias with sampling using accept-reject

Requirement changes¶

pin down cloudpickle version (upstream bug with pip install) and TF, TFP versions

0.5.2 (13.05.2020)¶

Major Features and Improvements¶

Python 3.8 and TF 2.2 support
easier debugigng with set_graph_mode that can also be used temporarily with a context manager. False will make everything execute Numpy-like.

Bug fixes and small changes¶

added get_params to loss
fix a bug with the fixed_params when creating a sampler
improve exponential PDF stability and shift when normalized
improve accept reject sampling to account for low statistics

Requirement changes¶

TensorFlow >= 2.2

0.5.1 (24.04.2020)¶

(0.5.0 was skipped)

Complete refactoring of Spaces to allow arbitrary function. New, more consistent behavior with extended PDFs. SumPDF refactoring, more explicit handling of fracs and yields. Improved graph building allowing for more fine-grained control of tracing. Stabilized minimization including a push-back for NaNs.

Major Features and Improvements¶

Arbitrary limits as well as vectorization (experimental) are now fully supported. The new Space has an additional argument for a function that tests if a vector x is inside.

To test if a value is inside a space, Space.inside can be used. To filter values, Space.filter.

The limits returned are now by default numpy arrays with the shape (1, n_obs). This corresponds well to the old layout and can, using z.unstack_x(lower) be treated like Data. This has also some consequences for the output format of rect_area: this is now a vector.

Due to the ambiguity of the name limits, area etc (since they do only reflect the rectangular case) method with leading rect_* have been added (rect_limits, rect_area etc.) and are encouraged to be used.
Extending a PDF is more straightforward and removes any “magic”. The philosophy is: a PDF can be extended or not. But it does not change the fundamental behavior of functions.
SumPDF has been refactored and behaves now as follows: Giving in pdfs (extended or not or mixed) and fracs (either length pdfs or one less) will create a non-extended SumPDF using the fracs. The fact that the pdfs are maybe extended is ignored. This will lead to highly consistent behavior. If the number of fracs given equals the number of pdfs, it is up to the user (currently) to take care of the normalization. Only if all pdfs are extended and no fracs are given, the sumpdf will be using the yields as normalized fracs and be extended.
Improved graph building and z.function
- the z.function can now, as with tf.function, be used either as a decorator without arguments or as a decorator with arguments. They are the same as in tf.function, except of a few additional ones.
- zfit.run.set_mode allows to set the policy for whether everything is run in eager mode (graph=False), everything in graph, or most of it (graph=True) or an optimized variant, doing graph building only with losses but not just models (e.g. pdf won’t trigger a graph build, loss.value() will) with graph='auto'.
- The graph cache can be cleaned manually using zfit.run.clear_graph_cache() in order to prevent slowness in repeated tasks.
Switch for numerical gradients has been added as well in zfit.run.set_mode(autograd=True/False).
Resetting to the default can be done with zfit.run.set_mode_default()
Improved stability of minimizer by adding penalty (currently in Minuit) as default. To have a better behavior with toys (e.g. never fail on NaNs but return an invalid FitResult), use the DefaultToyStrategy in zfit.mnimize.
Exceptions are now publicly available in zfit.exception
Added nice printout for FitResult and FitResult.params.
get_params is now more meaningful, returning by default all independent parameters of the pdf, including yields. Arguments (floating, is_yield) allow for more fine-grained control.

Breaking changes¶

Multiple limits are now handled by a MultiSpace class. Each Space has only “one limit” and no complicated layout has to be remembered. If you want to have a space that is defined in disconnected regions, use the + operator or functionally zfit.dimension.add_spaces

To extract limits from multiple limits, MultiSpace and Space are both iterables, returning the containing spaces respectively itself (for the Space case).
SumPDF changed in the behavior. Read above in the Major Features and Improvement.
Integrals of extended PDFs are not extended anymore, but ext_integrate now returns the integral multiplied by the yield.

Deprecations¶

ComposedParameter takes now params instead of dependents as argument, it acts now as the arguments to the value_fn. To stay future compatible, create e.g. def value_fn(p1, pa2) and using params = ['param1, param2], value_fn will then be called as value_fn(param1, parma2). value_fn without arguments will probably break in the future.
FitResult.error has been renamed to errors to better reflect that multiple errors, the lower and upper are returned.

Bug fixes and small changes¶

fix a (nasty, rounding) bug in sampling with multiple limits
fix bug in numerical calculation
fix bug in SimplePDF
fix wrong caching signature may lead to graph not being rebuild
add zfit.param.set_values method that allows to set the values of multiple parameters with one command. Can, as the set_value method be used with a context manager.
wrong size of weights when applying cuts in a dataset
with_coords did drop axes/obs
Fix function not traced when an error was raised during first trace
MultipleLimits support for analytic integrals
zfit.param.set_values(..) now also can use a FitResult as values argument to set the values from.

Experimental¶

added a new error method, ‘zfit_error’ that is equivalent to ‘minuit_minos’, but not fully stable. It can be used with other minimizers as well, not only Minuit.

Requirement changes¶

remove the outdated typing module
add tableformatter, colored, colorama for colored table printout

Thanks¶

Johannes Lade for code review and discussions.
Hans Dembinski for useful inputs to the uncertainties.

0.4.3 (11.3.2020)¶

Major Features and Improvements¶

refactor hesse_np with covariance matrix, make it available to all minimizers

Behavioral changes¶

Bug fixes and small changes¶

fix bug in hesse_np

Requirement changes¶

Thanks¶

0.4.2 (27.2.2020)¶

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.
Add hesse error method (‘hesse_np’) available to all minimizers (not just Minuit).

Behavioral changes¶

Changed default step size to an adaptive scheme, a fraction (1e-4) of the range between the lower and upper limits.

Bug fixes and small changes¶

Add use_minuit_grad option to Minuit optimizer to use the internal gradient, often for more stable fits
added experimental flag zfit.experimental_loss_penalty_nan, which adds a penalty to the loss in case the value is nan. Can help with the optimisation. Feedback welcome!

Requirement changes¶

Thanks¶

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)

Thanks¶

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

Thanks¶

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¶

None

Bug fixes and small changes¶

fixed various small bugs

Thanks¶

for the contribution of the Constraints to Matthieu Marinangeli <matthieu.marinangeli@cern.ch>

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.

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