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Optuna grid-search tests doubled PR CI time since #1252 (~23 → ~47 min); cap their search budget in tests

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Assessment

Difficulty
2/5
Estimated time
1-3 hours
Newbie friendliness
78/100
Issue type
Refactor
Clarity
Clearly specified
Activity status
Active
Tech stack
python
Domain
machine-learning, testing

Research direction

Locate the eight test_eval_grid_search_multimetric_scorer[*-True] cases and test_eval_grid_search_optuna, then read how they configure OptunaSearchCV and its evaluation call. Pass time_out=1 to those evaluations and add a whats_new.rst entry under Code health. Run the affected tests and confirm they still cover the Optuna path and primary-metric selection.

Written by the indexing model from the issue text.

Description

Problem. Since 2026-10-14 the Test workflow takes ~47 min per PR, up from ~23 min. Most of the increase is the Optuna-backed evaluation tests added in #1252: each OptunaSearchCV uses the default 10 trials over a 2-point grid (csp__metric: euclid/riemann), so the 8 test_eval_grid_search_multimetric_scorer[*-True] cases and test_eval_grid_search_optuna take ~110 s each. On the ubuntu py3.12 leg that is ~1,200 of ~2,150 s of pytest (job 117553166134, run 39086137050), against ~230 s on 2026-10-09 (job 113726805695).

Proposed change (test-only, ~4 lines). Pass time_out=1 to the evaluation in those Optuna test cases, so Optuna stops after the trial in flight. The tests still exercise the Optuna path and the primary-metric selection; only the search budget changes. No library behaviour changes. Plus a whats_new.rst line under Code health.

Measured on a fork (agarg21/moabb, PR-triggered Test workflow on develop, 3 runs per arm, all green, identical 5,269 passed / 1,255 skipped):

  • PR wall median 46.9 → 23.2 min (−50%)
  • ubuntu py3.12 job 40.8 → 23.2 min, windows py3.12 46.8 → 17.4 min, macOS py3.12 34.2 → 22.2 min
  • runner time ~197 → ~114 job-min per run (−42%)
  • Runs before: 39220244267, 39231604955, 39264518650; after: 39226327039, 39237394932, 39281666313

Happy to open the PR against develop if this approach sounds good.

Prepared with AI assistance (Claude Code); measured on a fork and reviewed before posting.

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Merged PRs (30d)
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