Consolidate duplication accrued across the ML tagging + settings backend,
behavior-preserving (over-DRY guard applied — the three auto-apply sweep
BODIES stay separate; only their shared inner helpers are extracted).
- _sigmoid / _conflict_scores / _insert_presentation_review (heads.py): the
score→prob transform (6 inlined sites), the presentation conflict signal
(2 sites), and the ring-loud PresentationReview insert (2 sites, single-
sourced so the mode column can't drift on the shared composite PK).
- _applied_or_rejected (training_data.py): the per-tag "applied ∪ rejected"
skip-set, byte-identical at 3 sweep sites (heads.py x2, tasks/ml.py ccip).
- ccip sweep divergence fixes: import ccip._FIGURE_KINDS + training_data._l2norm
instead of local copies that silently drift when the canonical changes.
- MLSettings.load / .load_sync classmethods (mirror ImportSettings); route all
8 scalar_one singleton reads through them (the session.get None-path stays).
- GET serializers for MLSettings + ImportSettings are now table-driven off the
same _EDITABLE tuples PATCH writes, so a new field can't be silently absent
from GET (the split that historically dropped fields).
- AUTO_APPLY_THRESHOLD_MIN/MAX constant single-sources the [0.5,0.999] operating
range across the service clamp + the 5 API validators.
- test_ml_dry_helpers.py pins _applied_or_rejected + _sigmoid.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NsmJSQxnNxGgtM5Yz4GAqi
Extends WIP title-tagging to lower-precision cues (sketch/doodle/scribble) safely.
- wip_title.py: soft matcher (word-anchored; sketchbook/kadoodle don't trip it);
WIP_TITLE_SOFT_SOURCE + soft SQL prefilter; apply_wip_image_tags takes a source arg.
- training_data._AUTO_SOURCES += 'wip_title_soft' → the soft tier is PROVISIONAL and
never trains the wip head (a finished "sketch" can't pollute it). Only the hard
tier (wip_title) + manual train.
- ImportSettings.wip_soft_title_tagging_enabled (OFF by default, opt-in). Migration 0087.
- importer: hard tier wins, soft is the fallback (source wip_title_soft).
- backfill: refactored into a shared _backfill_wip_tier; hard always, soft when enabled.
- heads.soft_wip_conflict_audit + daily beat: score soft-tagged images against content
heads, flag ring-loud ones (PresentationReview mode=process) for the review strip —
the operator's "measure if they got falsely tagged" safety.
- api settings toggle; ImportFiltersForm soft toggle.
- tests: soft matcher pos/neg; soft source not a training positive; audit flags
ring-loud + spares quiet.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Backend for the system-tag behavior refactor (milestone #157). editor screenshot
moves from chrome (hidden) to the PROCESS group (shown, like wip); wip+editor gain
provisional auto-apply so they stop needing endless manual identification —
without a runaway loop.
- tag.py: split PRESENTATION_SYSTEM_TAGS → CHROME_SYSTEM_TAGS (banner) +
PROCESS_SYSTEM_TAGS (wip, editor screenshot).
- heads.py: generalize presentation_auto_apply_sweep → system_tag_auto_apply_sweep
(mode chrome|process). Same Guard 1 (skip human/confirmed) + Guard 2 (ring-loud
conflict → PresentationReview). process mode uses source 'process_auto' and does
NOT hide (hide is a gallery-query effect of group membership).
- training_data._AUTO_SOURCES += 'process_auto' → the head never trains on its own
auto-applied output; only wip_title/manual train it (the runaway break).
- ml_settings: process_auto_apply_enabled (OFF, opt-in) + threshold + conflict
threshold. presentation_review.mode ('chrome'|'process'). Migration 0086.
- gallery_service: default-hide reads CHROME only (editor now shows); Explore
neighbors exclude the whole PROCESS group.
- tasks/ml + celery beat: scheduled_process_auto_apply (daily, opt-in); prune
covers both modes.
- api: ml_admin process_* CRUD+validation; hidden-review returns mode.
- tests: rename chrome sweep calls; new test_process_auto_apply (apply, guards,
mode flag, no-self-train); gallery test asserts editor now visible.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
presentation_auto_apply_sweep fires banner/editor-screenshot heads at the FLAT
presentation threshold (source=presentation_auto). Two guards: (1) hard-skip any
image already carrying a human/confirmed content tag — you valued it, so the model
can't bury it; (2) if an auto-hide ALSO scores >= presentation_conflict_threshold
on a content head, hide it but record a PresentationReview row (conflict tag +
score) for the Hidden view.
_auto_apply_heads now excludes system tags, so a graduated wip/banner can't fire
via the content path (and wip never auto-applies at all). presentation_auto added
to _AUTO_SOURCES so auto-hidden chrome never self-trains. Tests: applies,
hard-skip valued, conflict-flag, disabled no-op, ignores wip, content-path
excludes system. Settings UI + scheduling land next.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
Makes auto-apply truly "soft" for heads: _ids_with_tag (head positives) and
_eligible_tag_ids (graduation count) now count human-applied + operator-confirmed
tags only, via a shared _AUTO_SOURCES (head_auto/ccip_auto/ml_auto) exclusion.
Unconfirmed auto-applied tags no longer train the head that judges them, so a
misfire can't reinforce itself and the retraction sweep can actually drop it.
Confirming a tag (TagPositiveConfirmation) promotes it to a positive AND protects
it from retraction. sklearn-free tests. CCIP reference exclusion is the companion
piece, next.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
Step 2 of milestone #128. _hygiene_excluded_ids (training_data.py) is the
one shared predicate: images carrying any system tag are dropped from
every OTHER concepts head training — not positives (a rough wip tagged
as a character drags the head toward generic-sketch) and not rejection
or sampled negatives (a wip OF character X is not evidence against X).
A system tags own head trains on them unfiltered; that is what makes
auto-flagging banners work. Selection is split out of train_head as the
sklearn-free head_training_ids so CI (no sklearn) can pin the behavior.
CCIP: reference prototypes skip hygiene-tagged images — a faceless wip
figure region must never become an identity reference — and the ref
cache signature now counts hygiene applications, since tagging an image
wip changes the reference set without touching character/region counts.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
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
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