Minor bugfixes

Bug fixes and small changes

• enhanced loss: simple loss can take a gradient and hesse function and the default base loss provides fallbacks that work correctly between value_gradient and gradient. This maybe matters if you've implemented a custom loss and should fix any issues with it.
• multiprocessing would get stuck due to an upstream bug in TensorFlow <https://github.com/tensorflow/tensorflow/issues/66115>_. Working around it by disabling an unused piece of code.

Thanks

• acampoverde for finding the bug in the multiprocessing

Minor bugfix

0.20.2 (16 Apr 2024)

Two small bugfixes

• fix backwards incompatible change of sampler
• detect if a RegularBinning has been transformed, raise error.

Minor fixes for stability

For a guide to the 0.20 changes, see here

0.20.1 (14 Apr 2024)

Major Features and Improvements

• fix dumping and add convenience wrapper zfit.dill to dump and load objects with dill (a more powerful pickle). This way, any zfit object can be saved and loaded, such as FitResult that contains all other important objects to recreate the fit.
• improved performance for numerical gradient calculation, fixing also a minor numerical issue.

Bug fixes and small changes

• runing binned fits without a graph could deadlock, fixed.

Usability overhaul and a ton of new PDFs

0.20.0 (12 Apr 2024)

Complete overhaul of zfit with a focus on usability and a variety of new pdfs! See here for an interactive guide

Major Features and Improvements

• Parameter behavior has changed, multiple parameters with the same name can now coexist!
  The NameAlreadyTakenError has been successfully removed (yay!). The new behavior only enforces that
  names and matching parameters within a function/PDF/loss are unique, as otherwise inconsistent expectations appear (for the full discussion on this, see here <https://github.com/zfit/zfit/discussions/342>_).
• Space and limits have a complete overhaul in front of them, in short, these overcomplicated objects get simplified and the limits become more usable, in terms of dimensions. The full discussion and changes can be found here <https://github.com/zfit/zfit/discussions/533>_ .
• add an unbinned Sampler to the public namespace under zfit.data.Sampler: this object is returned in the create_sampler method and allows to resample from a function without recreating the compiled function, i.e. loss. It has an additional method update_data to update the data without recompiling the loss and can be created from a sample only. Useful to have a custom dataset in toys.
• allow to use pandas DataFrame as input where zfit Data objects are expected
• Methods of PDFs and loss functions that depend on parameters take now the value of a parameter explicitly as arguments, as a mapping of str (parameter name) to value.
• Python 3.12 support
• add GeneralizedCB PDF which is similar to the DoubleCB PDF but with different standard deviations for the left and right side.
• Added functor for PDF caching CachedPDF: pdf, integrate PDF methods can be cacheable now
• add faddeeva_humlicek function under the zfit.z.numpy namespace. This is an implementation of the Faddeeva function, combining Humlicek's rational approximations according to Humlicek (JQSRT, 1979) and Humlicek (JQSRT, 1982).
• add Voigt profile PDF which is a convolution of a Gaussian and a Cauchy distribution.
• add TruncatedPDF that allows to truncate in one or multiple ranges (replaces "MultipleLimits" and "MultiSpace")
• add LogNormal PDF, a log-normal distribution, which is a normal distribution of the logarithm of the variable.
• add ChiSquared PDF, the standard chi2 distribution, taken from tensorflow-probability implementation <https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/Chi2>_.
• add StudentT PDF, the standard Student's t distribution, taken from tensorflow-probability implementation <https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/StudentT>_.
• add GaussExpTail and GeneralizedGaussExpTail PDFs, which are a Gaussian with an exponential tail on one side and a Gaussian with different sigmas on each side and different exponential tails on each side respectively.
• add QGauss PDF, a distribution that arises from the maximization of the Tsallis entropy under appropriate constraints, see here <https://en.wikipedia.org/wiki/Q-Gaussian_distribution>_.
• add ``BifurGauss` PDF, a Gaussian distribution with different sigmas on each side of the mean.
• add Bernstein PDF, which is a PDF defined by a linear combination of Bernstein polynomials given their coefficients.
• add Gamma PDF, the Gamma distribution.
• Data has now a with_weights method that returns a new data object with different weights and an improved with_obs that allows to set obs with new limits. These replace the set_weights and set_data_range methods for a more functional approach.
• add label to different objects (PDF, Data, etc.) that allows to give a human-readable name to the object. This is used in the plotting and can be used to identify objects.
  Notably, Parameters have a label that can be arbitrary. Space has one label for each observable if the space is a product of spaces. Space.label is a string and only possible for one-dimensional spaces, while Space.labels is a list of strings and can be used for any, one- or multi-dimensional spaces.
• add zfit.data.concat(...) to concatenate multiple data objects into one along the index or along the observables. Similar to pd.concat.
• PDFs now have a to_truncated method that allows to create a truncated version of the PDF, possibly with different and multiple limits. This allows to easily create a PDF with disjoint limits.
• Data and PDF that take obs in the initialization can now also take binned observables, i.e. a zfit.Space with binning=... and will return a binned version of the object (zfit.data.BinnedData or zfit.pdf.BinnedFromUnbinned, where the latter is a generic wrapper). This is equivalent of calling to_binned on the objects)
• zfit.Data can be instantiated directly with most data types, such as numpy arrays, pandas DataFrames etc insead of using the dedicated constructors from_numpy, from_pandas etc.
  The constructors may still provide additional functionality, but overall, the switch should be seamless.

Breaking changes

This release contains multiple "breaking changes", however, the vast majority if not all apply only for edge cases and undocummented functions.

• a few arguments are now keyword-only arguments. This can break existing code if the arguments were given as positional arguments. Just use the appropriate keyword arguments instead.
  (Example: instead of using zfit.Space(obs, limits) use zfit.Space(obs, limits=limits)).
  This was introduced to make the API more robust and to avoid errors due to the order of arguments, with a few new ways of creating objects.
• Data.from_root: deprecated arguments branches and branch_aliases have been removed. Use obs and obs_aliases instead.
• NameAlreadyTakenError was removed, see above for the new behavior. This should not have an effect on any existing code except if you relied on the error being thrown.
• Data objects had an intrinsic, TensorFlow V1 legacy behavior: they were actually cut when the data was retrieved. This is now changed and the data is cut when it is created. This should not have any impact on existing code and just improve runtime and memory usage.
• Partial integration used to use some broadcasting tricks that could potentially fail. It uses now a dynamic while loop that could be slower but works for arbitrary PDFs. This should not have any impact on existing code and just improve stability (but technically, the data given to the PDF if doing partial integration is now "different", in the sense that it's now not different anymore from any other call)
• if a tf.Variable was used to store the number of sampled values in a sampler, it was possible to change the value of that variable to change the number of samples drawn. This is now not possible anymore and the number of samples should be given as an argument n to the resample method, as was possible since a long time.
• create_sampler has a breaking change for fixed_params: when the argument was set to False, any change in the parameters would be reflected when resampling.
  This highly statebased behavior was confusing and is now removed. The argument is now called params
  and behaves as expected: the sampler will remember the parameters at the time of creation,
  possibly updated with params and will not change anymore. To sample from a different set of parameters,
  the params have to be passed to the resample method explicitly.
• the default names for hesse and errors have now been changed to hesse and errors, respectively.
  This was deprecated since a while and both names were available for backwards compatibility. The old names are now removed. If you get an error, minuit_hessse or minuit_minos not found, just replace it with hesse and errors.

Deprecations

• result.fminfull is deprecated and will be removed in the future. Use result.fmin instead.
• Data.set_data_range is deprecated and will be removed in the future. Use with_range instead.
• Space has many deprecated methods, such as rect_limits and quite a few more. The full discussion can be found here <https://github.com/zfit/zfit/discussions/533>_.
• fixed_params in create_sampler is deprecated and will be removed in the future. Use params instead.
• fixed_params attribute of the Sampler is deprecated and will be removed in the future. Use params instead.
• uncertainties in GaussianConstraint is deprecated and will be removed in the future. Use either explicitly sigma or cov.
• the ComposedParameter and ComplexParameter argument value_fn is deprecated in favor of the new argument func. Identical behavior.
• zfit.run(...) is deprecated and will be removed in the future. Simply remove it should work in most cases.
  (if an explicity numpy, not just array-like, cast is needed, use np.asarray(...). But usually this is not needed). This function is an old relic from the past TensorFlow 1.x, tf.Session times and is not needed anymore. We all remember well these days :)

Bug fixes and small changes

• complete overhaul of partial integration that used some broadcasting tricks that could potentially fail. It uses now a dynamic while loop that could be slower but works for arbitrary PDFs and no problems should be encountered anymore.
• FitResult can now be used as a context manager, which will automatically set the values of the parameters to the best fit values and reset th...

Add missing dep

Hotfix release to add missing deps attrs

Bug fixes in randomness and improved caching

0.18.1 (22 Feb 2024)

Major Features and Improvements

• reduced the number of graph caching reset, resulting in significant speedups in some cases

Bug fixes and small changes

• use random generated seeds for numpy and TF, as they can otherwise have unwanted correlations

Thanks

@anjabeck for the bug report and the idea to use random seeds for numpy and TF
@acampoverde for reporting the caching issue

TF 2.15 upgrade, drop Python 3.8

Uprade to TensorFlow 2.15 (from 2.12/2.13 previously) and TensorFlow-Probability 0.23
Drop Python 3.8 support

Fixes in uncertainty corrections

0.17.0

Major Features and Improvements

• add correct uncertainty for unbinned, weighted fits with constraints and/or that are extended.
• allow mapping in zfit.param.set_values for values

Bug fixes and small changes

• fix issues where EDM goes negative, set to 999
• improved stability of the loss evaluation
• improved uncertainty calculation accuracy with zfit_error

Thanks

Daniel Craik for the idea of allowing a mapping in set_values

0.16.0 Minor fixes and improvements

0.16.0 (26 July 2023)

Major Features and Improvements

• add full option to loss call of value, which returns the unoptimized value allowing for easier statistical tests using the loss.
  This is the default behavior and should not break any backwards compatibility, as the "not full loss" was arbitrary.
• changed the FitResult to print both loss values, the unoptimized (full) and optimized (internal)

Bug fixes and small changes

• bandwidth preprocessing was ignored in KDE
• unstack_x with an obs as argument did return the wrong shape

Thanks

@schmitse for reporting the bug in the KDE bandwidth preprocessing
@lorenzopaolucci for bringing up the absolute value of the loss in the fitresult as an issue

Minor fixes for binned fits

0.15.5

fix a bug in histmodifier that would not properly take into account the yield of the wrapped PDF