Zfit

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Bug fixes in randomness and improved caching

Major Features and Improvements
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- reduced the number of graph caching reset, resulting in significant speedups in some cases


Bug fixes and small changes
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- use random generated seeds for numpy and TF, as they can otherwise have unwanted correlations


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

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Major Features and Improvements
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- update to TensorFlow 2.15, TensorFlow Probability 0.23
- drop Python 3.8 support

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TensorFlow 2.15, drop Python 3.8 support

Major Features and Improvements
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- 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
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- fix issues where EDM goes negative, set to 999
- improved stability of the loss evaluation
- improved uncertainty calculation accuracy with ``zfit_error``



Thanks
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Daniel Craik for the idea of allowing a mapping in ``set_values``

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Major Features and Improvements
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- 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
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- bandwidth preprocessing was ignored in KDE
- ``unstack_x`` with an ``obs`` as argument did return the wrong shape


Thanks
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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

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Bug fixes and small changes
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- fix a bug in histmodifier that would not properly take into account the yield of the wrapped PDF

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Fix missing ``attrs`` dependency

Major Features and Improvements
-------------------------------
- add option ``full`` in loss to return the full, unoptimized value (currently not default), allowing for easier statistical tests using the loss