Closed sjvrijn closed 4 years ago
thank you for this submission @sjvrijn our next pyopensci checkin in is tomorrow at 11am mountain time! we will discuss this package then. you are welcome to join us as well as it's an open meeting!!!
As discussed in the December 5 meeting, this submission is withdrawn/on hold
See the meeting notes for details.
hey @sjvrijn thank you for closing this and for understanding!! I hope that you still are interested in being involved with us. We just don't have the capacity yet to review this particular submission BUT i do suspect that in the future we may have that capacity!!
@lwasser No problem, and yes I'm still interested. How do I sign up as a reviewer/supporter?
awesome! and funny you should ask!! i finally created a good form for this very purpose. You can fill it out here -- https://forms.gle/5MuYMVs8xpCxk9w29 and please let me know if you have any issues with filling it out or if there are other "categories" of domain expertise that i am missing!! i am sure i've missed some important ones.
Submitting Author: Sander van Rijn(@sjvrijn)
Package Name: mf2 One-Line Description of Package: A collection of multi-fidelity analytical benchmark functions Repository Link (if existing): https://github.com/sjvrijn/mf2
Description
This package provides a number of multi-fidelity function implementations commonly used to benchmark the performance of multi-fidelity (surrogate-model-assisted) optimization algorithms. It provides a common and simple interface to all included functions, with corresponding references available in the documentation.
Scope
Please indicate which category or categories this package falls under:
Explain how the and why the package falls under these categories (briefly, 1-2 sentences). Please note any areas you are unsure of:
Analytical benchmark functions are a like well-established datasets in the field of machine-learning: by comparing your algorithm on the same ones, you can accurately compare your results. They are essential for reproducibility, as typos in the formulas will obviously give very different results, but are not an 'external' utility like the examples given under Package Categories in the guidebook.
The target audience is any scientist working in the field of multi-fidelity optimization algorithms, as they will likely want to use functions from this collection to benchmark the performance of their algorithms.
I was not aware of any when writing the package. Since recently, there seem to be two packages
emukit
andsmt
that also contain a partially overlapping set of the same functions. Those packages however are mostly focussed on providing algorithms to apply on these kinds of problems, while mymf2
is a separate package consisting of just functions.In my opinion, that makes mf2:
None