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amkrajewski / nimCSO

nim Composition Space Optimization is a high-performance tool leveraging metaprogramming to implement several methods for selecting components (data dimensions) in compositional datasets, as to optimize the data availability and density for applications such as machine learning.
https://nimcso.phaseslab.org
MIT License
23 stars 1 forks source link

JOSS Review Suggestions #4

Closed bdice closed 1 month ago

bdice commented 2 months ago

See https://github.com/amkrajewski/nimCSO/issues/3

This PR attempts to resolve some of the suggestions from my review, along with a handful of typo fixes and similar edits.

Please feel free to accept or reject any of the changes in this PR. Some changes to benchmarks and Dockerfiles are slightly opinionated. If you want to reject any changes, you can just comment on the PR where you want to keep the current code, and revert the commits where those changes were made. (e.g. git revert 7a9840b) That will make it easier to track.

RMeli commented 1 month ago

Hi @amkrajewski, this PR looks very helpful to get https://github.com/openjournals/joss-reviews/issues/6731 moving forward. Is there anything blocking its review and merge?

amkrajewski commented 1 month ago

@RMeli Thanks for the ping! I was waiting for Reviewer 3 before making any modifications (which I got last week). I am traveling right now, but I should be able to start working on revisions soon and finish them next week.

amkrajewski commented 1 month ago

Thank you, @bdice, for your thorough and impactful contributions in this PR, with attention to detail and performance optimizations. I very much appreciate these!

I will now re-benchmark everything, adjust the table in the paper, and get back to other points you've raised in #3

bdice commented 1 month ago

Glad it helped!