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f271df0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 | """Metric engine: precision/recall/F1/IoU + verification rates.
Pure functions — no I/O. Callers feed counts from MegaEval containers or
LabelEval match stats.
"""
from __future__ import annotations
from typing import Any, Dict, Iterable, List, Optional, Sequence
def safe_div(num: float, den: float) -> Optional[float]:
if den <= 0:
return None
return num / den
def round4(v: Optional[float]) -> Optional[float]:
return None if v is None else round(float(v), 4)
def precision_recall_f1(tp: int, fp: int, fn: int) -> Dict[str, Optional[float]]:
precision = safe_div(tp, tp + fp)
recall = safe_div(tp, tp + fn)
f1 = None
if precision is not None and recall is not None and (precision + recall) > 0:
f1 = 2 * precision * recall / (precision + recall)
return {
"precision": round4(precision),
"recall": round4(recall),
"f1": round4(f1),
"tp": tp,
"fp": fp,
"fn": fn,
}
def mean_iou(sum_iou: float, tp: int) -> Optional[float]:
return round4(safe_div(sum_iou, tp))
def verification_rates(
*,
n_objects: int,
verified: int = 0,
reviewed: int = 0,
disputed: int = 0,
auto_accepted: int = 0,
hitl: int = 0,
unverified: Optional[int] = None,
) -> Dict[str, Any]:
"""Verification / HITL funnel rates at container (or dataset) scope.
unverified defaults to n_objects - verified when not provided.
"""
n = max(0, int(n_objects))
verified = max(0, int(verified))
reviewed = max(0, int(reviewed))
disputed = max(0, int(disputed))
auto_accepted = max(0, int(auto_accepted))
hitl = max(0, int(hitl))
if unverified is None:
unverified = max(0, n - verified)
def rate(count: int) -> Optional[float]:
return round4(safe_div(count, n)) if n > 0 else None
return {
"n_objects": n,
"verified": verified,
"unverified": unverified,
"disputed": disputed,
"auto_accepted": auto_accepted,
"hitl": hitl,
"reviewed": reviewed,
"verified_rate": rate(verified),
"unverified_rate": rate(unverified),
"disputed_rate": rate(disputed),
"auto_accept_rate": rate(auto_accepted),
"hitl_rate": rate(hitl),
"review_coverage": rate(reviewed),
}
def container_quality_metrics(
*,
tp: int,
fp: int,
fn: int,
sum_iou: float = 0.0,
n_objects: int = 0,
verified: int = 0,
reviewed: int = 0,
disputed: int = 0,
auto_accepted: int = 0,
hitl: int = 0,
anomalies: int = 0,
) -> Dict[str, Any]:
prf = precision_recall_f1(tp, fp, fn)
n = n_objects or (tp + fp)
ver = verification_rates(
n_objects=n,
verified=verified,
reviewed=reviewed,
disputed=disputed,
auto_accepted=auto_accepted,
hitl=hitl,
)
return {
**prf,
"mean_iou": mean_iou(sum_iou, tp),
"anomalies": anomalies,
"anomaly_rate": round4(safe_div(anomalies, n)) if n else None,
"verification": ver,
}
def aggregate_container_rows(rows: Sequence[Dict[str, Any]]) -> Dict[str, Any]:
"""Roll up a page of container metric dicts into one profile summary."""
tp = fp = fn = verified = reviewed = disputed = auto_acc = hitl = anomalies = n = 0
sum_iou = 0.0
for r in rows:
tp += int(r.get("tp") or 0)
fp += int(r.get("fp") or 0)
fn += int(r.get("fn") or 0)
verified += int(r.get("verified") or 0)
reviewed += int(r.get("reviewed") or 0)
disputed += int(r.get("disputed") or 0)
auto_acc += int(r.get("auto_accepted") or 0)
hitl += int(r.get("hitl") or 0)
anomalies += int(r.get("anomalies") or 0)
n += int(r.get("n_objects") or 0)
mi = r.get("mean_iou")
tpi = int(r.get("tp") or 0)
if mi is not None and tpi > 0:
sum_iou += float(mi) * tpi
elif r.get("sum_iou") is not None:
sum_iou += float(r["sum_iou"])
return container_quality_metrics(
tp=tp, fp=fp, fn=fn, sum_iou=sum_iou, n_objects=n,
verified=verified, reviewed=reviewed, disputed=disputed,
auto_accepted=auto_acc, hitl=hitl, anomalies=anomalies,
)
def delta_metrics(
baseline: Dict[str, Optional[float]],
candidate: Dict[str, Optional[float]],
keys: Optional[Iterable[str]] = None,
) -> List[Dict[str, Any]]:
keys = list(keys or ("precision", "recall", "f1", "mean_iou", "safety_recall", "anomaly_rate"))
out = []
for k in keys:
b, c = baseline.get(k), candidate.get(k)
if b is None or c is None:
continue
out.append({
"metric": k,
"baseline": round4(float(b)),
"candidate": round4(float(c)),
"delta": round4(float(c) - float(b)),
})
return out
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