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tests/factories.py holds image_row/make_image/make_image_async/make_tag. The 17 byte-identical _img/_tag helpers (15 modules) now import them under their old names, so no call site changed. frontend/test/support/stubFetch.js replaces 15 copies that differed only in formatting. Copies whose bodies differ (other defaults, other columns, a url-only stub) are left as they are; folding those needs a look at each caller. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01LVjrnpQjRgHdvq95rASoiR
179 lines
7.0 KiB
Python
179 lines
7.0 KiB
Python
"""Process-group auto-apply sweep (#1464): wip / editor screenshot auto-tag at a
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flat threshold with a PROVISIONAL source (`process_auto`) so the head never trains
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on its own output, and stay VISIBLE (unlike chrome). Mirrors the chrome guards.
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numpy-only (no sklearn), tested directly via the sync session."""
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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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ImageRecord,
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MLSettings,
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PresentationReview,
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Tag,
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TagHead,
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TagKind,
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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.heads import system_tag_auto_apply_sweep
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from backend.app.services.ml.training_data import _ids_with_tag
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from tests.factories import make_image as _img
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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 _head(db, tag_id: int, slot: int, *, weight=1.0):
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s = db.execute(select(MLSettings).where(MLSettings.id == 1)).scalar_one()
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w = [0.0] * 1152
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w[slot] = weight
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db.add(TagHead(
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tag_id=tag_id, embedding_version=s.embedder_model_version,
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weights=w, bias=0.0, suggest_threshold=0.5, auto_apply_threshold=0.5,
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n_pos=60, n_neg=90, ap=0.9, precision_cv=0.98, recall=0.7,
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))
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def _system_tag(db, name):
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return db.execute(
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select(Tag).where(Tag.is_system.is_(True), Tag.name == name)
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).scalar_one()
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def _enable_process(db):
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# process auto-apply is opt-in (default False) — turn it on for these tests.
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db.execute(select(MLSettings).where(MLSettings.id == 1)).scalar_one().process_auto_apply_enabled = True
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def _source(db, image_id, tag_id):
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return db.execute(
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select(image_tag.c.source)
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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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).scalar_one_or_none()
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def test_process_sweep_applies_wip_and_editor(db_sync):
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_enable_process(db_sync)
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wip = _system_tag(db_sync, "wip")
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editor = _system_tag(db_sync, "editor screenshot")
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_head(db_sync, wip.id, 0, weight=3.0)
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_head(db_sync, editor.id, 1, weight=3.0)
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w_img = _img(db_sync, "a" * 64, _emb(0))
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e_img = _img(db_sync, "b" * 64, _emb(1))
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db_sync.commit()
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res = system_tag_auto_apply_sweep(db_sync, mode="process")
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assert res["n_applied"] == 2
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assert _source(db_sync, w_img.id, wip.id) == "process_auto"
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assert _source(db_sync, e_img.id, editor.id) == "process_auto"
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def test_process_sweep_disabled_by_default_is_noop(db_sync):
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# process_auto_apply_enabled defaults False (opt-in) — no enable = no-op.
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wip = _system_tag(db_sync, "wip")
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_head(db_sync, wip.id, 0, weight=3.0)
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img = _img(db_sync, "c" * 64, _emb(0))
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db_sync.commit()
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res = system_tag_auto_apply_sweep(db_sync, mode="process")
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assert res["n_applied"] == 0
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assert _source(db_sync, img.id, wip.id) is None
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def test_process_sweep_skips_valued_image(db_sync):
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# Guard 1: never auto-apply to an image the operator already content-tagged.
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_enable_process(db_sync)
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wip = _system_tag(db_sync, "wip")
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_head(db_sync, wip.id, 0, weight=3.0)
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content = Tag(name="mychar", kind=TagKind.character)
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db_sync.add(content)
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db_sync.flush()
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img = _img(db_sync, "d" * 64, _emb(0))
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db_sync.execute(image_tag.insert().values(
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image_record_id=img.id, tag_id=content.id, source="manual"))
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db_sync.commit()
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res = system_tag_auto_apply_sweep(db_sync, mode="process")
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assert res["n_applied"] == 0
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assert _source(db_sync, img.id, wip.id) is None
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def test_process_sweep_flags_conflict_with_process_mode(db_sync):
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# Guard 2: also scores high on a content head → still applied, but flagged
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# for review with mode='process' (the ring-loud guard).
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_enable_process(db_sync)
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wip = _system_tag(db_sync, "wip")
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_head(db_sync, wip.id, 0, weight=3.0)
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content = Tag(name="looksreal", kind=TagKind.general)
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db_sync.add(content)
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db_sync.flush()
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_head(db_sync, content.id, 0, weight=1.0)
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img = _img(db_sync, "e" * 64, _emb(0))
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db_sync.commit()
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res = system_tag_auto_apply_sweep(db_sync, mode="process")
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assert res["n_applied"] == 1
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assert res["n_flagged"] == 1
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flag = db_sync.execute(
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select(PresentationReview).where(
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PresentationReview.image_record_id == img.id,
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PresentationReview.tag_id == wip.id,
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)
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).scalar_one()
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assert flag.mode == "process"
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assert flag.conflict_tag_id == content.id
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def test_process_auto_source_never_trains_head(db_sync):
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# The runaway break: provisional wip tags (process sweep 'process_auto', soft
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# title 'wip_title_soft') are NOT training positives; a HARD title-heuristic /
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# manual one IS. So the head learns only from trusted labels, never its own
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# output or the low-precision sketch/doodle tier (#1464 + #1474).
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wip = _system_tag(db_sync, "wip")
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auto_img = _img(db_sync, "f" * 64, _emb(0))
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soft_img = _img(db_sync, "9" * 64, _emb(2))
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title_img = _img(db_sync, "0" * 64, _emb(1))
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db_sync.execute(image_tag.insert().values(
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image_record_id=auto_img.id, tag_id=wip.id, source="process_auto"))
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db_sync.execute(image_tag.insert().values(
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image_record_id=soft_img.id, tag_id=wip.id, source="wip_title_soft"))
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db_sync.execute(image_tag.insert().values(
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image_record_id=title_img.id, tag_id=wip.id, source="wip_title"))
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db_sync.commit()
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positives = set(_ids_with_tag(db_sync, wip.id))
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assert title_img.id in positives # trusted HARD label trains the head
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assert auto_img.id not in positives # its own auto-applied output does NOT
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assert soft_img.id not in positives # low-precision soft tier does NOT
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def test_soft_wip_conflict_audit_flags_ring_loud(db_sync):
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# A soft-tagged image (sketch/doodle title) that ALSO scores high on a content
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# head is probably finished art mis-tagged — flagged for review; a quiet one is not.
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from backend.app.services.ml.heads import soft_wip_conflict_audit
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s = db_sync.execute(select(MLSettings).where(MLSettings.id == 1)).scalar_one()
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s.process_conflict_threshold = 0.6
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wip = _system_tag(db_sync, "wip")
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content = Tag(name="looksreal", kind=TagKind.general)
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db_sync.add(content)
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db_sync.flush()
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_head(db_sync, content.id, 0, weight=1.0) # sigmoid(1)=0.73 > 0.6 conflict
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ring = _img(db_sync, "1" * 64, _emb(0)) # scores on the content head
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quiet = _img(db_sync, "2" * 64, _emb(5)) # orthogonal → 0.5 < 0.6
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for img in (ring, quiet):
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db_sync.execute(image_tag.insert().values(
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image_record_id=img.id, tag_id=wip.id, source="wip_title_soft"))
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db_sync.commit()
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res = soft_wip_conflict_audit(db_sync)
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assert res["n_flagged"] == 1
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flag = db_sync.execute(
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select(PresentationReview).where(PresentationReview.image_record_id == ring.id)
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).scalar_one()
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assert flag.mode == "process"
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assert flag.conflict_tag_id == content.id
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assert db_sync.execute(
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select(PresentationReview).where(PresentationReview.image_record_id == quiet.id)
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).scalar_one_or_none() is None
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