chore: retire the tag-eval harness — it proved the heads system, job done (operator-approved)

The head-vs-centroid eval (#1130) existed to prove the 'frozen embedding +
trained head' spine; the operator accepted the tagging system and dropped the
harness. Removed per rule 22: TagEvalCard + store, /api/tag_eval blueprint,
tag_eval_run ml task, recover-stalled-tag-eval-runs sweep + beat entry,
TagEvalRun model + table (migration 0073), and its tests.

The eval's data loaders + metric helpers were NOT eval-specific — the nightly
heads trainer runs on them — so they moved verbatim to
services/ml/training_data.py (heads.py import updated; behavior unchanged).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM

backend/app/services/ml/heads.py

@@ -1,12 +1,13 @@
 """Production heads: train + score the per-concept classifiers (#114).
 
-The eval (#1130, tag_eval.py) proved the spine; this is its production form.
+The eval harness (#1130) proved the spine, then retired 2026-07-02 once the
+tagging system was accepted; this is the production form.
 - TRAIN (sync, ml worker — needs scikit-learn): for every general/character tag
   with enough labelled positives, fit a logistic-regression head on the FROZEN
   SigLIP embeddings (positives + negatives = rejections + sampled unlabeled),
   derive an honest suggest threshold + earned-auto-apply point from CROSS-
-  VALIDATED scores, and upsert a TagHead row. Reuses tag_eval's proven data
-  loaders + metric helpers so production heads match the eval's measured numbers.
+  VALIDATED scores, and upsert a TagHead row. Uses the eval-proven data loaders
+  + metric helpers (training_data.py) so heads match the measured numbers.
 - SCORE (async, API worker — numpy via pgvector, NO scikit-learn): score one
   image's embedding against all current heads → the suggestions the rail shows,
   REPLACING Camie predictions + per-tag centroids.
@@ -37,7 +38,7 @@ from ...models import (
     TagSuggestionRejection,
 )
 from ...models.tag import image_tag
-from .tag_eval import (
+from .training_data import (
     _auto_apply_point,
     _ids_with_tag,
     _l2norm,