fix(quickadapter): recheck weight support after feature pipeline (#150)

fix(quickadapter): recheck weight support after feature pipeline (#150)

Label-weight support checks (min_pivot_equivalent_count,
min_positive_label_weight_fraction, min_effective_sample_size Kish ESS)
previously ran only before feature_pipeline.fit_transform, which can remove
training rows and renormalize the surviving weights below those thresholds
without re-invoking support_policy.

Recheck support post-pipeline on the surviving rows: carry the raw base and
label weight components through fit_transform as temporary object-keyed label
columns (row-filtered in lockstep with X/y/sample_weight, since DataSieve
resets indexes), pop them, restore the label schema, and re-gate via
support_policy. _sanitize_pipeline_weights fails closed on any malformed
post-pipeline weight shape (None/scalar/2-D/wrong-length). Base-only weights
carry no label support and skip the re-gate. Selection training and final
refit share the path; eval (val/test) weights still bypass support_policy but
now fail closed on shape.

No tunable, canonical default, or public API change.

Fixes #93