Use get_heterogeneous_feature_mapping in LFI argparse dispatcher (#5195)#5195
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Use get_heterogeneous_feature_mapping in LFI argparse dispatcher (#5195)#5195hvarfner wants to merge 4 commits into
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April 29, 2026 07:57
…TaskGP (facebook#5192) Summary: X-link: meta-pytorch/botorch#3296 Automatically configures learned feature imputation for models that pad heterogeneous per-task data to the full joint feature space. Models with native heterogeneity support are excluded from this automatic configuration. Differential Revision: D101841497
facebook#5193) Summary: Switches the default heterogeneous transfer learning model from a specialized per-task kernel model to a standard multi-task GP with learned feature imputation. The previous default model class is marked as deprecated. Differential Revision: D102197137
…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
…ebook#5195) Summary: Refactors the learned imputation argument dispatcher to delegate feature index computation to the dataset's built-in mapping utility. This eliminates duplicated feature-ordering logic and ensures consistency with the canonical ordering convention. Differential Revision: D102197138
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Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #5195 +/- ##
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- Coverage 96.38% 96.38% -0.01%
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Files 617 617
Lines 69463 69481 +18
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+ Hits 66954 66970 +16
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Summary:
Refactors the learned imputation argument dispatcher to delegate feature index computation to the dataset's built-in mapping utility. This eliminates duplicated feature-ordering logic and ensures consistency with the canonical ordering convention.
Differential Revision: D102197138