bvandeusen
48c8811d69
Auto-apply is now ON by default (operator-asked: opt-OUT, not opt-in) — migration 0059 + model default flipped. The support (>=30) + measured-precision gates keep it safe and every auto-tag is reversible. Observability so the operator can tune from real data: - MISFIRE = an auto-applied (source='head_auto') tag the operator later removes. UNDER-FIRE = a tag with a head the operator adds by hand (the head missed it). Both captured at correction time in TagService.add_to_image/remove_from_image (source is lost on delete) into durable per-tag counters (head_metric), keyed by tag so they survive head retrain/prune. - Daily snapshot_head_metrics writes a per-concept time-series point (head_metrics_snapshot): auto-applied volume + cumulative misfires/under-fires + head quality; 180-day retention; daily beat. - GET /api/heads/metrics: per-concept current counts + realized misfire rate + head quality, plus the snapshot time-series — the report to tune the precision target + support floor. Migration 0060. Tests: misfire/under-fire counting (and the negatives — manual removal isn't a misfire, headless manual add isn't an under-fire), snapshot time-series, metrics API. What's the autofire threshold? There's no single number — each graduated head derives its OWN probability cutoff from its PR curve: the operating point that holds precision >= head_auto_apply_precision (0.97) at max recall. The global knobs are that target + the >=30 support floor. NEXT (slice 3): UI — enable toggle, dry-run preview, per-concept trends. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
98 lines
4.3 KiB
Python
98 lines
4.3 KiB
Python
"""MLSettings — single-row table holding ML pipeline tunables."""
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from datetime import datetime
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from sqlalchemy import (
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Boolean,
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CheckConstraint,
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DateTime,
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Float,
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Integer,
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String,
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func,
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)
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from sqlalchemy.orm import Mapped, mapped_column
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from .base import Base
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class MLSettings(Base):
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__tablename__ = "ml_settings"
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# Bare name — Base.metadata's naming convention prepends ck_<table>_,
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# producing the final ck_ml_settings_singleton (matches migration 0003).
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__table_args__ = (CheckConstraint("id = 1", name="singleton"),)
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id: Mapped[int] = mapped_column(Integer, primary_key=True)
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suggestion_threshold_character: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.70
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)
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# Default raised 0.50 → 0.70 on 2026-06-02 — operator-flagged 0.50
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# surfaced too many low-confidence picks; 0.70 keeps the rail
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# signal-rich while still surfacing more than the original 0.95
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# which hid almost everything. Operator-tunable via Settings → ML.
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suggestion_threshold_general: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.70
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)
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centroid_similarity_threshold: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.55
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)
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# Ingest floor: tagger predictions below this confidence are not stored
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# (tagger.Tagger.infer). Default 0.70 — the suggestion path already
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# filters at 0.70 and the centroid/learned path covers low-confidence
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# preferred tags, so the sub-0.70 tail is redundant weight (it had
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# bloated image_record's TOAST to ~100 GB; plan-task #764). Operator-
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# tunable via Settings → ML; must stay ≤ the suggestion thresholds.
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tagger_store_floor: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.70
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)
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min_reference_images: Mapped[int] = mapped_column(
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Integer, nullable=False, default=5
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)
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# Video tagging (#747). Sample one frame every N seconds (fixed CADENCE, not a
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# fixed count) so a tag's frame-presence reflects real screen time regardless
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# of video length; cap the total so a long video can't explode into hundreds
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# of inferences (the cadence stretches past the cap). A tag is kept only if it
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# appears in >= video_min_tag_frames sampled frames (≈ that many × interval
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# seconds on screen) — duration-independent noise rejection. Operator-tunable.
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video_frame_interval_seconds: Mapped[float] = mapped_column(
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Float, nullable=False, default=4.0
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)
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video_max_frames: Mapped[int] = mapped_column(
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Integer, nullable=False, default=64
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)
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video_min_tag_frames: Mapped[int] = mapped_column(
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Integer, nullable=False, default=3
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)
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# Tagging-v2 head training (#114). The head is the suggestion source that
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# LEARNS from the operator's tags (replacing Camie + centroid). A concept
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# needs >= head_min_positives labelled images before a head is trained;
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# head_auto_apply_precision is the precision bar a head must clear (at some
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# operating point) to "graduate" into earned auto-apply. Operator-tunable.
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head_min_positives: Mapped[int] = mapped_column(
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Integer, nullable=False, default=8
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)
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head_auto_apply_precision: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.97
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)
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# Earned auto-apply (#114). A graduated head fires (tags images without a
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# human) when this master switch is on AND the head has at least
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# head_auto_apply_min_positives clean labels — so a precise-looking but
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# under-supported low-N head can't spray tags across the library. ON by
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# default (operator-asked 2026-06-29: opt-OUT, not opt-in); the support +
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# measured-precision gates keep it safe, and every auto-tag is reversible.
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head_auto_apply_enabled: Mapped[bool] = mapped_column(
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Boolean, nullable=False, default=True
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)
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head_auto_apply_min_positives: Mapped[int] = mapped_column(
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Integer, nullable=False, default=30
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)
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tagger_model_version: Mapped[str] = mapped_column(
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String(128), nullable=False, default="camie-tagger-v2"
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)
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embedder_model_version: Mapped[str] = mapped_column(
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String(128), nullable=False, default="siglip-so400m-patch14-384"
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)
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updated_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), nullable=False, server_default=func.now()
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)
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