feat: recommend_bucketed_config() -- discrete regime-bucketed adaptive recommendation

[jc-macdonald]

Summary

Closes #123.

RegimeSurrogate interpolates continuously across regime descriptors, but needs reasonably dense training coverage -- RF surrogates (its default backend) extrapolate poorly (effectively flat) outside their training range. There was no equivalent for the sparse case: a handful of named regimes, each far from the others and far from any existing training data, where the actual need is "find a good config for each one directly, then aggregate into named buckets."

Why this matters

Found deriving VBPCA hyperparameter buckets for extreme-aspect-ratio data shapes (yoavram-lab/VBPCApy#116) -- genomics-scale (p >> n) and ecological/survey-scale (n >> p) regimes, 2-3 per extreme. The pattern that actually worked was hand-rolled in analysis/trade_study/option_a_aspect_ratio.py: run_adaptive per regime, pick the best trial, group into named buckets, aggregate. This formalizes that as a reusable trade-study function.

Changes

recommend_bucketed_config(regimes, bucket_fn, world_factory, scorer, factors, observables, *, primary, n_trials=30, n_reps=1, seed=42):

• Runs run_adaptive independently per regime (world_factory(regime) builds a regime-scoped Simulator)
• Selects each regime's single best trial by the primary observable, respecting its Direction (minimize/maximize)
• Groups regimes into named buckets via bucket_fn(name, regime) -> bucket_name
• Aggregates each bucket's per-regime best configs: median for continuous/discrete factors, mode (most common) for categorical

Uses run_adaptive's n_reps (#122, already merged) to avoid the single-draw fragility that motivated #122 in the first place.

Test plan

• just ci passes, regime.py at 100% coverage
• New tests: median aggregation across regimes with different optima, buckets stay separate when bucket_fn doesn't merge them, categorical aggregation picks the mode, Direction.MAXIMIZE is respected, rejects empty regimes/unknown primary

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