FIX keep CrossSession results session-specific for custom CV folds

[lindicaphxag-tech]

Summary

CrossSessionEvaluation supports arbitrary scikit-learn cv_class values, so a custom test fold can contain more than one recording session. The current scoring path still assumes each fold holds out exactly one session and labels the entire fold with groups[test][0].

That silently gives multi-session folds the provenance of whichever session appears first.

Context in the evaluation-safety series

This is a follow-up found while validating the custom-CV path around #1207. #1207 makes explicit top-level splitters usable; this PR closes the corresponding result-provenance invariant for CrossSessionEvaluation: once a custom fold may contain multiple sessions, result rows must still remain session-specific rather than inheriting the first group label.

Fix

Keep fitting once per CV fold, but score and store each held-out session separately.

• the legacy evaluation path partitions each test fold by its actual session before scoring;
• the flattened/parallel path enables the existing score_per_session machinery, which already implements the same grouping;
• default Leave-One-Session-Out behavior is unchanged because each fold already contains one session.

Regression coverage

A focused test uses GroupKFold(n_splits=2) on a 4-session, 2-subject fake dataset. Each CV fold therefore holds out two sessions at once. It verifies that:

• results contain one row per real held-out session;
• all four session identities are preserved;
• n_samples_test is session-specific;
• flattened and legacy execution produce identical result keys, scores, and test sizes.

No splitter API or grouping semantics are changed.

[lindicaphxag-tech]

Exact-head validation is green on 92609a3c: MOABB pre-commit passes and the focused multi-session custom-CV regression passes on the current branch. The regression verifies that parallel and legacy CrossSession paths emit the same per-session provenance/scores across a 4-session GroupKFold split instead of collapsing a multi-session test fold onto its first session. Evidence: https://github.com/lindicaphxag-tech/lindicaphxag-tech/actions/runs/37145135118. I’ll hold scope here for review.

[lindicaphxag-tech]

Updated exact-head validation: on 150ada97754deff06ef246edcb726101ae69e5c0, current-head pre-commit and the multi-session custom-CV provenance regression both pass. This supersedes the earlier evidence SHA after the branch cleanup/squash. Evidence: https://github.com/lindicaphxag-tech/lindicaphxag-tech/actions/runs/37183338993. I’ll hold this head for review.

[lindicaphxag-tech]

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[lindicaphxag-tech]

@bruAristimunha this is the smallest independent piece of the evaluation-provenance series and is ready for review. It only fixes CrossSession custom-CV folds that hold out multiple sessions: one fit is retained, but scoring/result attribution stays session-specific, with legacy/parallel parity covered. It does not depend on #1207/#1211, so it can merge independently; I’ll keep this head frozen unless review finds an issue.

[bruAristimunha]

Thanks @lindicaphxag-tech. The fix is well scoped to CrossSessionEvaluation; the default LeaveOneGroupOut path still produces one row per fold since every fold holds exactly one session (the np.unique(test_sessions) loop collapses to one iteration), and the GroupKFold(n_splits=2) regression checks that the flattened and legacy paths agree on keys, scores and n_samples_test. I've just approved the Actions run; I'll approve the PR once Test/Test-braindecode are green on 150ada9. One request: since the flattened path previously returned one aggregated row per fold and now one per held-out session, please mention it under API changes in whats_new.rst as well.

[bruAristimunha]

CI is green on 150ada9 (Test matrix, Test-braindecode, Docs). Approving — please still add the whats_new API note about one row per held-out session for custom folds before merge if you get a chance, @lindicaphxag-tech. Thanks.