anomaly_eval op• Data kinds: signal × signal → table
• Call: import fullseye as fs; fs.ledger.anomaly_operating_point(scores, labels, recall=0.95, confidence=0.95) (implementation को सीधे call करना हो तो import anomeval; anomeval.anomaly_operating_point(scores, labels, recall=0.95, confidence=0.95), registry से लेना हो तो opsspc.get("anomaly_operating_point"))
वह सबसे ऊँचा threshold चुनना जिसकी defect recall confidence स्तर पर कम से कम recall होने की गारंटी हो (table)।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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).
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• poc_patch_anomaly — py -3.11 examples/poc_patch_anomaly.py
table को input के रूप में लेते हैं)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 operator registry. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.