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/models/__init__.py

@@ -33,7 +33,6 @@ from .subscribestar_failed_media import SubscribeStarFailedMedia
 from .subscribestar_seen_media import SubscribeStarSeenMedia
 from .tag import Tag, TagKind, image_tag
 from .tag_alias import TagAlias
-from .tag_eval_run import TagEvalRun
 from .tag_head import TagHead
 from .tag_positive_confirmation import TagPositiveConfirmation
 from .tag_suggestion_rejection import TagSuggestionRejection
@@ -75,7 +74,6 @@ __all__ = [
     "HeadMetricsSnapshot",
     "HeadTrainingRun",
     "TagAlias",
-    "TagEvalRun",
     "TagHead",
     "TagPositiveConfirmation",
     "TagSuggestionRejection",

backend/app/models/head_training_run.py

@@ -1,7 +1,7 @@
 """HeadTrainingRun — persisted lifecycle of a head-training batch (#114).
 
-Mirrors TagEvalRun so the run SURVIVES navigation and the admin card can show
-live + historical status instead of holding it in transient frontend state.
+A persisted run row (not transient frontend state) so the run SURVIVES
+navigation and the admin card can show live + historical status.
 Training is idempotent (it upserts tag_head rows), so a SIGKILL'd run is harmless
 — a maintenance recovery sweep flips a stalled `running` row to `error`, and the
 next run re-trains. State machine: running → ready / error.
@@ -37,8 +37,8 @@ class HeadTrainingRun(Base):
     n_trained: Mapped[int | None] = mapped_column(Integer, nullable=True)
     n_skipped: Mapped[int | None] = mapped_column(Integer, nullable=True)
     error: Mapped[str | None] = mapped_column(Text, nullable=True)
-    # Last time the task made progress — the recovery sweep tells a live run from
-    # a SIGKILL'd one by this (mirrors TagEvalRun).
+    # Last time the task made progress — the recovery sweep tells a live run
+    # from a SIGKILL'd one by this.
     last_progress_at: Mapped[datetime | None] = mapped_column(
         DateTime(timezone=True), nullable=True
     )

backend/app/models/tag_eval_run.py

@@ -1,45 +0,0 @@
-"""TagEvalRun — persisted lifecycle of a head-vs-centroid tagging eval (#1130).
-
-Mirrors LibraryAuditRun so the result SURVIVES navigation: the run + its full
-report live in this row, and the admin card rehydrates from it on mount instead
-of holding the report in transient frontend state. State machine:
-running → ready / error. The async ml-queue task writes `report` (JSONB) when
-done; a maintenance recovery sweep flips a stalled `running` row to `error`.
-"""
-
-from datetime import datetime
-from typing import Any
-
-from sqlalchemy import DateTime, Integer, String, Text, func
-from sqlalchemy.dialects.postgresql import JSONB
-from sqlalchemy.orm import Mapped, mapped_column
-
-from .base import Base
-
-
-class TagEvalRun(Base):
-    __tablename__ = "tag_eval_run"
-
-    id: Mapped[int] = mapped_column(Integer, primary_key=True)
-    # The eval parameters: {concepts: [...], curve_points: [...], neg_ratio,
-    # cv_folds, ...} — echoed back so the report is self-describing.
-    params: Mapped[dict[str, Any]] = mapped_column(JSONB, nullable=False)
-    status: Mapped[str] = mapped_column(
-        String(16), nullable=False, default="running", index=True,
-    )
-    # running | ready | error
-    started_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=True), nullable=False, server_default=func.now(),
-    )
-    finished_at: Mapped[datetime | None] = mapped_column(
-        DateTime(timezone=True), nullable=True,
-    )
-    # The full result: per-concept metrics (head vs centroid), learning-curve
-    # points, and example image ids. Null until the task finishes.
-    report: Mapped[dict[str, Any] | None] = mapped_column(JSONB, nullable=True)
-    error: Mapped[str | None] = mapped_column(Text, nullable=True)
-    # Last time the task made progress — the recovery sweep tells a live run
-    # from a SIGKILL'd one by this (mirrors LibraryAuditRun).
-    last_progress_at: Mapped[datetime | None] = mapped_column(
-        DateTime(timezone=True), nullable=True,
-    )