Probfit iminuit version
Kjertan Lyster
I have been using iminuit and probfit for a while now. As I was reinstalling Python on a new computer, I realised that the iminiuit version is 1.5.4 when installing probfit and not the most recent. Is this intentional? Should I update iminuit to the most recent one?
Best Regards,
Kjertan
Jonas Eschle
Hi Kjertan,
if you upgrade, this will very likely break things as iminuit had a major change in its API for v2.
probfit is depreceated though and is not actively maintained
anymore. Depending on your needs, I would suggest to use one of
the recommended alternatives:
- for binned, histfactory-like fits, pyhf
- for general fits, especially unbinned, zfit
These are fitting libraries that provide building models and everything about it (since you used probfit, I assume this is your use case. Both can use iminuit but also offer alternative minimizers.
- If you wand to use iminuit directly and unleash the full power of it, e.g. if you have the CDF and your data, you can also build your likelihood with iminuit cost functions
If you have more questions on a specific library, it is maybe worth to write directly there on github by making an issue.
For an overview, you find the same information that I provided
above (and a variety of other libraries) also on the Scikit-HEP
page under fitting or statistics
Best regards,
Jonas
Jim Pivarski
Hi,
I have been using iminuit and probfit for a while now. As I was reinstalling Python on a new computer, I realised that the iminiuit version is 1.5.4 when installing probfit and not the most recent. Is this intentional? Should I update iminuit to the most recent one?
Best Regards,
Kjertan
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Jim Pivarski
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Hans Dembinski
I think the incompatibility in probfit can be resolved with moderate effort by writing a thin compatibility layer similar to the package six, which calls the right methods for iminuit v1.x and iminuit v2.x, respectively. This is to keep old code running, new users should not use probfit anymore. Fortunately, there are fewer and fewer reasons to rely on probfit.
Cost functions have been integrated directly into the iminuit package, making probfit largely obsolete for new code. The cost functions in iminuit are more powerful, too. For example, one can add them and add penalty terms to constrain parameters.
Some of the pdfs in probfit have been implemented in my other package numba-stats. In general, people should use the pdfs from scipy and contribute pdfs to scipy. Numba-stats mostly provides implementations of the scipy pdfs that work inside the Numba JIT.
What iminuit does not cover - it is outside of its scope -, is to provide a high-level way to defining and combining pdfs. I am talking about addable pdfs that are automatically normalised or computing pdf convolutions automatically.
Best regards,
Hans