feat(ml): tag-eval backend — head-vs-centroid learning-curve eval (persisted)

Slice 1 of milestone #114 (tagging v2). Proves the frozen-embedding + trained-
head spine on the operator's own data, reusing the SigLIP embeddings already
stored on image_record — no re-embedding, no GPU.

Per concept: train a logistic-regression HEAD (positives + negatives = explicit
rejections + sampled unlabeled) vs the old single-CENTROID baseline; report
cross-validated precision/recall/AP for both, a LEARNING CURVE (AP/F1 as tagged
positives grow 10→30→100→300), and example image ids (head-would-suggest /
head-doubts-positive) to eyeball.

Persisted so the report SURVIVES navigation (operator-flagged): the run + full
report live in a new tag_eval_run row (mirrors library_audit_run); the admin
card will rehydrate from GET on mount, not transient state.

- models.TagEvalRun + migration 0056; runs on the ml queue (only worker with
  numpy/sklearn) — numpy/sklearn lazy-imported so the API can still enqueue.
- services/ml/tag_eval (compute + start helper, one-running guard), tasks.ml
  .tag_eval_run, api/tag-eval (POST create, GET history light / detail w/ report).
- recover_stalled_tag_eval_runs sweep + retention (keep last 20) + 5-min beat
  (rule 89). scikit-learn added to requirements-ml.
- tests: param normalization + the rehydrate read-path + create/conflict.

Frontend admin card (trigger + render persisted report) follows next.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

backend/app/models/__init__.py

@@ -29,6 +29,7 @@ from .subscribestar_seen_media import SubscribeStarSeenMedia
 from .tag import Tag, TagKind, image_tag
 from .tag_alias import TagAlias
 from .tag_allowlist import TagAllowlist
+from .tag_eval_run import TagEvalRun
 from .tag_reference_embedding import TagReferenceEmbedding
 from .tag_suggestion_rejection import TagSuggestionRejection
 from .task_run import TaskRun
@@ -65,6 +66,7 @@ __all__ = [
     "MLSettings",
     "TagAlias",
     "TagAllowlist",
+    "TagEvalRun",
     "TagReferenceEmbedding",
     "TagSuggestionRejection",
     "TaskRun",

backend/app/models/tag_eval_run.py

@@ -0,0 +1,45 @@
+"""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,
+    )