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@mfeurer mfeurer commented Mar 3, 2020

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codecov-io commented Mar 5, 2020

Codecov Report

Merging #791 into development will decrease coverage by 0.96%.
The diff coverage is 89.74%.

Impacted file tree graph

@@               Coverage Diff               @@
##           development     #791      +/-   ##
===============================================
- Coverage         86.7%   85.73%   -0.97%     
===============================================
  Files              129      129              
  Lines             9211     9206       -5     
===============================================
- Hits              7986     7893      -93     
- Misses            1225     1313      +88
Impacted Files Coverage Δ
autosklearn/evaluation/__init__.py 85.97% <ø> (-4.88%) ⬇️
autosklearn/__init__.py 80% <ø> (ø) ⬆️
...data_preprocessing/data_preprocessing_numerical.py 89.65% <100%> (+2.55%) ⬆️
autosklearn/automl.py 81.81% <100%> (ø) ⬆️
...ta_preprocessing/data_preprocessing_categorical.py 89.65% <100%> (+2.55%) ⬆️
autosklearn/metrics/classification_metrics.py 90.62% <100%> (ø) ⬆️
autosklearn/pipeline/regression.py 84.11% <100%> (ø) ⬆️
autosklearn/pipeline/classification.py 86.88% <100%> (ø) ⬆️
autosklearn/smbo.py 73.42% <100%> (-12.99%) ⬇️
autosklearn/pipeline/base.py 86.39% <81.81%> (+0.59%) ⬆️
... and 10 more

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@mfeurer mfeurer marked this pull request as ready for review March 5, 2020 18:59
@mfeurer mfeurer merged commit 7ee603d into development Mar 5, 2020
@mfeurer mfeurer deleted the bump_smac branch March 5, 2020 18:59
@PetarMPetrov
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We are trying to use SMAC v0.12.0 and auto-sklearn in the same environment but there is version collision. I was delighted to see that it is sorted here already. When can we expect this bump to smac 0.12.0 to make its way to the release version?

@mfeurer
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mfeurer commented Apr 2, 2020

I currently expect that it will take us until early May to finish up a new release.

franchuterivera pushed a commit to franchuterivera/auto-sklearn that referenced this pull request Aug 21, 2020
* bump SMAC version

* fix that example

* reduce the number of warnings
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3 participants