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[Tracking issue] Add raw_training_scoresaccessor function #960

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ablaom opened this issue Aug 19, 2022 · 1 comment
Closed
10 tasks

[Tracking issue] Add raw_training_scoresaccessor function #960

ablaom opened this issue Aug 19, 2022 · 1 comment

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@ablaom
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ablaom commented Aug 19, 2022

To address #841 (comment) I suggest we add a raw_training_scores accessor function, following the established pattern for feature_importances and training_losses.

@davnn Are you happy with the name of this function? I'm thinking here of consistency with training_losses.

Here are the steps (adapted from #747).

Step 1. Prepare a bunch of PR's to be rolled out after testing, all called scores:

  • Add reports_raw_training_scores trait to StatisticalTraits, defaulting to false, as here for feature_importances (but need to also add to list of all traits at top of file)
  • Add raw_training_scores(model, fitresult, report) stub to MLJModelInterface (in model_api.jl), as here; fallback to return nothing.
  • Overload MLJModelInterface.raw_training_scores(mach::Machine) following this pattern and re-export raw_training_scores (the reports_raw_training_scores trait will be automatically re-exported in MLJBase and MLJ - no action required)
  • In MLJ: re-export raw_training_scores

Step 2

  • @blaom to check MLJTestIntegration runs with no new surprises (and maybe adds some detector-specific testing)

Step 3

  • Merge and rollout the scores PRs and tags new releases, taking care to bump compats accordingly. Probably @ablaom to do.
  • Update MLJ model API docs

Step 4

  • Roll out implementations for packages that already report
    scores (in, eg, their report); @davnn to complete this checklist:
    • OutlierDetection.jl
    • ?
@ablaom
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ablaom commented Jun 9, 2024

closing as stale

@ablaom ablaom closed this as completed Jun 9, 2024
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