Use StratifiedStandardize for per-task Y standardization in TL (#5194)#5194
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Use StratifiedStandardize for per-task Y standardization in TL (#5194)#5194hvarfner wants to merge 2 commits into
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…ook#5194) Summary: Adds per-task outcome standardization to the transfer learning adapter, ensuring each task's observations are standardized independently rather than jointly. Updates the default transform pipeline to use TL-specific outcome transforms. This removes ambiguity on whether the right transforms have been applied (e.g. QuickBO/warm-starting), where standardization is not performed across, but within experiments. Differential Revision: D102197139
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…ook#5194) Summary: Adds per-task outcome standardization to the transfer learning adapter, ensuring each task's observations are standardized independently rather than jointly. Updates the default transform pipeline to use TL-specific outcome transforms. This removes ambiguity on whether the right transforms have been applied (e.g. QuickBO/warm-starting), where standardization is not performed across, but within experiments. Differential Revision: D102197139
Codecov Report❌ Patch coverage is
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## main #5194 +/- ##
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+ Coverage 96.38% 96.61% +0.23%
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Files 617 617
Lines 69605 69638 +33
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+ Hits 67090 67284 +194
+ Misses 2515 2354 -161 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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…ook#5194) Summary: Adds per-task outcome standardization to the transfer learning adapter, ensuring each task's observations are standardized independently rather than jointly. Updates the default transform pipeline to use TL-specific outcome transforms. This removes ambiguity on whether the right transforms have been applied (e.g. QuickBO/warm-starting), where standardization is not performed across, but within experiments. Differential Revision: D102197139
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…acebook#5200) Summary: **Motivation:** model_space/search_space was not properly used - parameter bounds on the search space would be set from the union of source and target, and parameters that were fixed on the target would be RangeParameters if the model_space contained a Fixed/Range change in the parameter. Adds a `data_parameters` argument to `TorchAdapter._get_fit_args` that decouples SSD construction (model params) from data column extraction (target params). This lets the TL adapter set `_model_space` to include source-only RangeParameters directly, so the SSD naturally covers the full joint feature space -- eliminating the need for the `_expand_ssd_to_joint_space` post-hoc expansion. Differential Revision: D104702983
…ook#5194) Summary: Adds per-task outcome standardization to the transfer learning adapter, ensuring each task's observations are standardized independently rather than jointly. Updates the default transform pipeline to use TL-specific outcome transforms. This removes ambiguity on whether the right transforms have been applied (e.g. QuickBO/warm-starting), where standardization is not performed across, but within experiments. Differential Revision: D102197139
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Summary:
Adds per-task outcome standardization to the transfer learning adapter, ensuring each task's observations are standardized independently rather than jointly. Updates the default transform pipeline to use TL-specific outcome transforms.
This removes ambiguity on whether the right transforms have been applied (e.g. QuickBO/warm-starting), where standardization is not performed across, but within experiments.
Differential Revision: D102197139