3006e84cc0
Daily scheduled_retract_auto_tags re-scores standing auto-applied tags and drops the ones the model no longer supports: - retract_auto_applied_heads: per graduated head, re-score its source='head_auto' images (bounded — only the images already carrying the auto-tag, not the whole library) and remove ones now < auto_apply_threshold. - retract_auto_applied_ccip: per source='ccip_auto' character tag, max-cosine the image's figure vectors vs that character's prototypes; remove ones now below the ccip auto-apply threshold. Both SKIP operator-confirmed tags (TagPositiveConfirmation) and are SILENT — a low score isn't proof the tag was wrong, so no hard negative is recorded (that's reserved for an operator removal). No-op unless the relevant auto-apply switch is on. New daily beat. sklearn-free tests for both paths + the disabled no-op. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
154 lines
5.5 KiB
Python
154 lines
5.5 KiB
Python
"""Soft auto-apply (milestone 139): the retraction sweeps drop standing
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head_auto/ccip_auto tags now below their threshold, keep the ones still above,
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and never touch manual or operator-confirmed tags. Sync + sklearn-free (they
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score with STORED weights/vectors), so tested directly via db_sync."""
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import pytest
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from sqlalchemy import select
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from backend.app.models import (
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CharacterPrototype,
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ImageRecord,
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ImageRegion,
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MLSettings,
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Tag,
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TagHead,
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TagKind,
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TagPositiveConfirmation,
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)
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from backend.app.models.tag import image_tag
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from backend.app.services.ml.character_prototypes import retract_auto_applied_ccip
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from backend.app.services.ml.heads import retract_auto_applied_heads
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pytestmark = pytest.mark.integration
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def _emb(slot: int) -> list[float]:
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v = [0.0] * 1152
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v[slot] = 3.0
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return v
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def _ccip(slot: int) -> list[float]:
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v = [0.0] * 768
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v[slot] = 1.0
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return v
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def _img(db, sha: str, emb=None) -> ImageRecord:
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img = ImageRecord(
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path=f"/images/{sha}.jpg", sha256=sha, size_bytes=1, mime="image/jpeg",
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width=1, height=1, origin="imported_filesystem",
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integrity_status="unknown", siglip_embedding=emb,
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)
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db.add(img)
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db.flush()
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return img
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def _figure(db, image_id: int, ccip) -> None:
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db.add(ImageRegion(
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image_record_id=image_id, kind="figure",
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rx=0.0, ry=0.0, rw=1.0, rh=1.0,
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ccip_embedding=ccip, embedding_version="ccip-test",
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))
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db.flush()
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def _tag(db, name: str, kind: TagKind) -> Tag:
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t = Tag(name=name, kind=kind)
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db.add(t)
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db.flush()
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return t
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def _apply(db, image_id: int, tag_id: int, source: str) -> None:
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db.execute(image_tag.insert().values(
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image_record_id=image_id, tag_id=tag_id, source=source,
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))
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def _version(db) -> str:
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return db.execute(
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select(MLSettings.embedder_model_version).where(MLSettings.id == 1)
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).scalar_one()
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def _head(db, tag_id: int, slot: int, threshold: float, version: str) -> None:
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w = [0.0] * 1152
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w[slot] = 1.0
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db.add(TagHead(
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tag_id=tag_id, embedding_version=version, weights=w, bias=0.0,
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suggest_threshold=0.5, auto_apply_threshold=threshold,
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n_pos=60, n_neg=180, ap=0.9, precision_cv=0.98, recall=0.7,
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))
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db.flush()
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def _has_tag(db, image_id: int, tag_id: int) -> bool:
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return db.execute(
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select(image_tag.c.tag_id)
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.where(image_tag.c.image_record_id == image_id)
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.where(image_tag.c.tag_id == tag_id)
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).first() is not None
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def test_retract_head_auto(db_sync):
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ver = _version(db_sync)
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tag = _tag(db_sync, "glasses", TagKind.general)
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_head(db_sync, tag.id, slot=0, threshold=0.7, version=ver)
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hi = _img(db_sync, "a" * 64, _emb(0)) # aligned → ~0.73 ≥ 0.7 → keep
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lo = _img(db_sync, "b" * 64, _emb(5)) # orthogonal → 0.5 < 0.7 → retract
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man = _img(db_sync, "c" * 64, _emb(5)) # low score but manual → keep
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conf = _img(db_sync, "d" * 64, _emb(5)) # low score, head_auto, CONFIRMED → keep
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_apply(db_sync, hi.id, tag.id, "head_auto")
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_apply(db_sync, lo.id, tag.id, "head_auto")
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_apply(db_sync, man.id, tag.id, "manual")
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_apply(db_sync, conf.id, tag.id, "head_auto")
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db_sync.add(TagPositiveConfirmation(image_record_id=conf.id, tag_id=tag.id))
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db_sync.commit()
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assert retract_auto_applied_heads(db_sync) == 1
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assert not _has_tag(db_sync, lo.id, tag.id) # retracted (below threshold)
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assert _has_tag(db_sync, hi.id, tag.id) # kept (still above)
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assert _has_tag(db_sync, man.id, tag.id) # kept (manual, not auto)
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assert _has_tag(db_sync, conf.id, tag.id) # kept (operator-confirmed)
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def test_retract_head_auto_noop_when_disabled(db_sync):
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s = db_sync.execute(select(MLSettings).where(MLSettings.id == 1)).scalar_one()
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s.head_auto_apply_enabled = False
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ver = _version(db_sync)
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tag = _tag(db_sync, "glasses", TagKind.general)
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_head(db_sync, tag.id, slot=0, threshold=0.7, version=ver)
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lo = _img(db_sync, "e" * 64, _emb(5)) # would be below threshold
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_apply(db_sync, lo.id, tag.id, "head_auto")
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db_sync.commit()
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assert retract_auto_applied_heads(db_sync) == 0
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assert _has_tag(db_sync, lo.id, tag.id) # switch off → nothing retracted
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def test_retract_ccip_auto(db_sync):
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char = _tag(db_sync, "Raven", TagKind.character)
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db_sync.add(CharacterPrototype(tag_id=char.id, ccip_embedding=_ccip(0)))
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hi = _img(db_sync, "f" * 64) # figure matches prototype → keep
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lo = _img(db_sync, "g" * 64) # figure orthogonal → retract
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conf = _img(db_sync, "h" * 64) # orthogonal, CONFIRMED → keep
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man = _img(db_sync, "i" * 64) # orthogonal, manual → keep
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_figure(db_sync, hi.id, _ccip(0))
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_figure(db_sync, lo.id, _ccip(5))
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_figure(db_sync, conf.id, _ccip(5))
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_figure(db_sync, man.id, _ccip(5))
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_apply(db_sync, hi.id, char.id, "ccip_auto")
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_apply(db_sync, lo.id, char.id, "ccip_auto")
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_apply(db_sync, conf.id, char.id, "ccip_auto")
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_apply(db_sync, man.id, char.id, "manual")
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db_sync.add(TagPositiveConfirmation(image_record_id=conf.id, tag_id=char.id))
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db_sync.commit()
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assert retract_auto_applied_ccip(db_sync) == 1
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assert not _has_tag(db_sync, lo.id, char.id) # retracted (below threshold)
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assert _has_tag(db_sync, hi.id, char.id) # kept (match ≥ threshold)
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assert _has_tag(db_sync, conf.id, char.id) # kept (operator-confirmed)
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assert _has_tag(db_sync, man.id, char.id) # kept (manual, not auto)
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