refactor(ml): DRY pass — shared sweep helpers + table-driven settings (#161)

Consolidate duplication accrued across the ML tagging + settings backend,
behavior-preserving (over-DRY guard applied — the three auto-apply sweep
BODIES stay separate; only their shared inner helpers are extracted).

- _sigmoid / _conflict_scores / _insert_presentation_review (heads.py): the
  score→prob transform (6 inlined sites), the presentation conflict signal
  (2 sites), and the ring-loud PresentationReview insert (2 sites, single-
  sourced so the mode column can't drift on the shared composite PK).
- _applied_or_rejected (training_data.py): the per-tag "applied ∪ rejected"
  skip-set, byte-identical at 3 sweep sites (heads.py x2, tasks/ml.py ccip).
- ccip sweep divergence fixes: import ccip._FIGURE_KINDS + training_data._l2norm
  instead of local copies that silently drift when the canonical changes.
- MLSettings.load / .load_sync classmethods (mirror ImportSettings); route all
  8 scalar_one singleton reads through them (the session.get None-path stays).
- GET serializers for MLSettings + ImportSettings are now table-driven off the
  same _EDITABLE tuples PATCH writes, so a new field can't be silently absent
  from GET (the split that historically dropped fields).
- AUTO_APPLY_THRESHOLD_MIN/MAX constant single-sources the [0.5,0.999] operating
  range across the service clamp + the 5 API validators.
- test_ml_dry_helpers.py pins _applied_or_rejected + _sigmoid.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NsmJSQxnNxGgtM5Yz4GAqi

backend/app/services/ml/training_data.py

@@ -94,6 +94,24 @@ def _rejected_ids(session: Session, tag_id: int) -> list[int]:
     ]
 
 
+def _applied_or_rejected(session: Session, tag_ids) -> dict[int, set[int]]:
+    """Per-tag skip set for the auto-apply sweeps: every image that ALREADY carries
+    the tag (ANY source — not just training positives) OR has rejected it. A sweep
+    never re-applies to these. Shared by auto_apply_sweep + system_tag_auto_apply_sweep
+    (heads.py) and scheduled_ccip_auto_apply (tasks/ml.py). Callers mutate the returned
+    sets in-place to also dedupe within a single run."""
+    skip: dict[int, set[int]] = {}
+    for tid in tag_ids:
+        ids = {
+            r[0] for r in session.execute(
+                select(image_tag.c.image_record_id).where(image_tag.c.tag_id == tid)
+            ).all()
+        }
+        ids.update(_rejected_ids(session, tid))
+        skip[tid] = ids
+    return skip
+
+
 def _sample_unlabeled(session: Session, exclude: set[int], limit: int) -> list[int]:
     """Random image ids (with an embedding) NOT carrying the tag. Concepts are
     sparse, so an untagged image is almost always a true negative."""