Closed MilesCranmer closed 1 year ago
Merging #168 (b42e0ea) into master (cfcdc6a) will decrease coverage by
0.36%
. The diff coverage is33.33%
.
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@@ Coverage Diff @@
## master #168 +/- ##
==========================================
- Coverage 96.38% 96.03% -0.36%
==========================================
Files 9 10 +1
Lines 526 529 +3
==========================================
+ Hits 507 508 +1
- Misses 19 21 +2
Impacted Files | Coverage Δ | |
---|---|---|
src/LossFunctions.jl | 100.00% <ø> (ø) |
|
src/losses.jl | 100.00% <ø> (ø) |
|
ext/LossFunctionsCategoricalArraysExt.jl | 33.33% <33.33%> (ø) |
|
src/losses/other.jl | 91.11% <33.33%> (ø) |
Tested on 1.9 and 1.6 – everything seems to work.
Can we actually drop versions < v1.9? Do you have requirements to stick to older Julia versions? We are updating the entire stack to require Julia v1.9, it would be nice to do it here already.
I don't recommend that, as Julia 1.6.* is under long-term support for at least the next few years (LTS happens on a five year cycle): https://julialang.org/downloads/#long_term_support_release. Until a new LTS is released I would advise making key dependencies like this one backwards compatible. You can use Compat.jl for using new features on old Julia versions FYI.
A lot of packages depend on LossFunctions.jl and strive to have backwards compatibility to the LTS version. e.g., if the Julia version is fixed at 1.9 here I would probably need to drop the LossFunctions.jl dependency in SymbolicRegression because some of my users are still using 1.7 (for whatever reason; maybe their cluster uses it for stability?).
It is ok to keep the PR as is 👍🏽 Many ecosystems are already requiring Julia v1.9, so I wondered if we could clean up the Requires.jl boilerplate here as well.
Fixes #167. This should be completely backwards compatible. It just decreases load time if the user is not using CategoricalArrays.jl in their application.
Before:
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