anomaly_eval op• 데이터 종류: signal × signal → table
• 호출: import fullseye as fs; fs.ledger.anomaly_operating_point(scores, labels, recall=0.95, confidence=0.95)(구현을 직접 호출하려면 import anomeval; anomeval.anomaly_operating_point(scores, labels, recall=0.95, confidence=0.95), 원장에서 가져오려면 opsspc.get("anomaly_operating_point"))
결함 재현율이 confidence 수준에서 recall 이상임을 보장할 수 있는 가장 높은 임계값을 고른다 (table).
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
scores (1-D, higher = more anomalous) and labels (1 / True = defective) are image scores of a held-out set that was NOT used to fit the detector. Detecting k of the n defective images, the exact one-sided Clopper–Pearson lower limit of the recall at level confidence is computed; the smallest k whose limit reaches recall fixes the threshold at the k-th highest defect score (>= rule: the highest threshold that still carries the guarantee, so the fewest false alarms). The guarantee holds for defects drawn like the held-out ones; it is a statement about a sample, not about every future defect. Returns threshold, recall_observed, recall_lower (the limit), detected, n_pos, n_neg, fpr (fraction of good images at or above the threshold), fpr_upper (its one-sided Clopper–Pearson upper limit at the same level) and n_required (the smallest number of defective images for which the guarantee is attainable at all:
ceil(ln(1 - confidence) / ln(recall))).
Raises ValueError when the guarantee is unattainable with so few defective images (n_pos < n_required): it never returns a threshold it cannot back. Also on empty / mismatched / non-finite input, non-binary labels, no good or no defective image, or recall / confidence outside (0, 1).
• 샘플 데이터 카탈로그(DL URL / 라이선스) —— 2-D 는 skimage.data(BSD/public)+ 합성, 3-D 는 실데이터 소스(Stanford/PDS 등)의 DL URL.
• 연산자의 내력·참고문헌 —— 이 연산자 족의 바탕이 된 연구/기법의 출처.
• 알고리즘의 정전(저자·연도)과 용도는 위의 패밀리 사용 가이드에 적혀 있습니다.
• poc_patch_anomaly — py -3.11 examples/poc_patch_anomaly.py
table 를 입력으로 받는 것)msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo
anomaly_eval)anomaly_image_auroc · anomaly_pixel_auroc · anomaly_pro · anomaly_region_overlap · anomaly_pixel_f1_max · anomaly_image_f1_max
*Provenance: anomeval.py — SPC 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.