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per-metric model selection in MBM #3032

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@sdaulton sdaulton commented Nov 7, 2024

Summary: This diff adds support for per-metric model selection in MBM using leave-one-out cross-validation. This will automatically select the best model for each outcome (among specified choices for each outcome) according to a specified criterion (e.g. rank correlation) using leave-one-out cross-validation. The selected models are then assembled into a ModelListGP.

Differential Revision: D64971370

@facebook-github-bot facebook-github-bot added the CLA Signed Do not delete this pull request or issue due to inactivity. label Nov 7, 2024
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This pull request was exported from Phabricator. Differential Revision: D64971370

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Codecov Report

Attention: Patch coverage is 97.57576% with 4 lines in your changes missing coverage. Please review.

Project coverage is 95.64%. Comparing base (22bd1b2) to head (24fde2b).

Files with missing lines Patch % Lines
ax/models/torch/botorch_modular/surrogate.py 93.44% 4 Missing ⚠️
Additional details and impacted files
@@           Coverage Diff            @@
##             main    #3032    +/-   ##
========================================
  Coverage   95.64%   95.64%            
========================================
  Files         483      483            
  Lines       49048    49172   +124     
========================================
+ Hits        46910    47030   +120     
- Misses       2138     2142     +4     

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Summary:

This diff adds support for per-metric model selection in MBM using leave-one-out cross-validation. This will automatically select the best model for each outcome (among specified choices for each outcome) according to a specified criterion (e.g. rank correlation) using leave-one-out cross-validation. The selected models are then assembled into a ModelListGP.

Differential Revision: D64971370
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This pull request was exported from Phabricator. Differential Revision: D64971370

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