feat(heads): incremental retraining — refit only changed tags (#1317 phase 2, m138)

train_all_heads is now incremental by default: a per-tag training-data
fingerprint (positive + rejection count/latest-timestamp, stored on
tag_head.train_fingerprint) means a manual Retrain refits ONLY the tags whose
data changed — O(what you touched), not O(all heads). The nightly
scheduled_train_heads passes full=True to reconcile sampled-negative + hygiene
drift across every head. First incremental run after deploy still refits
everyone (NULL fingerprints), stamping them, then it's incremental.

The refit decision + fingerprint are split into sklearn-free helpers
(_head_fingerprints, _heads_needing_retrain) so the incremental logic is
unit-tested directly (train_head itself needs scikit-learn). Migration 0080.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM

backend/app/tasks/ml.py

@@ -356,7 +356,10 @@ def scheduled_train_heads() -> str:
         if running is not None:
             return "already running"
         run = HeadTrainingRun(
-            params={"source": "scheduled"}, status="running",
+            # Nightly = FULL reconcile (refit every head) so sampled-negative +
+            # hygiene drift is folded in; the manual Retrain button stays
+            # incremental (#1317 p2).
+            params={"source": "scheduled", "full": True}, status="running",
             last_progress_at=datetime.now(UTC),
         )
         session.add(run)