File size: 2,397 Bytes
a882958 | 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 | """Doğruluk ve kalibrasyon ölçüleri (yalnızca numpy)."""
import numpy as np
def ece(conf, correct, bins=15):
conf, correct = np.asarray(conf, float), np.asarray(correct, float)
edges = np.linspace(0, 1, bins + 1)
out = 0.0
for lo, hi in zip(edges[:-1], edges[1:]):
m = (conf > lo) & (conf <= hi) if lo > 0 else (conf >= lo) & (conf <= hi)
if m.any():
out += m.mean() * abs(conf[m].mean() - correct[m].mean())
return float(out)
def brier(conf, correct):
conf, correct = np.asarray(conf, float), np.asarray(correct, float)
return float(np.mean((conf - correct) ** 2))
def auroc(conf, correct):
"""Güvenin doğru/yanlışı ayırma gücü (Mann-Whitney)."""
conf, correct = np.asarray(conf, float), np.asarray(correct, bool)
pos, neg = conf[correct], conf[~correct]
if len(pos) == 0 or len(neg) == 0:
return float("nan")
order = np.argsort(np.concatenate([pos, neg]), kind="mergesort")
ranks = np.empty(len(order))
allv = np.concatenate([pos, neg])[order]
i = 0
r = np.empty(len(order))
while i < len(allv):
j = i
while j + 1 < len(allv) and allv[j + 1] == allv[i]:
j += 1
r[i:j + 1] = (i + j) / 2 + 1
i = j + 1
ranks[order] = r
return float((ranks[:len(pos)].sum() - len(pos) * (len(pos) + 1) / 2) / (len(pos) * len(neg)))
def risk_coverage(conf, correct, coverages=(1.0, 0.8, 0.6, 0.4, 0.2)):
"""En emin olduğu adımların %c'sinde doğruluk."""
conf, correct = np.asarray(conf, float), np.asarray(correct, float)
order = np.argsort(-conf, kind="stable")
out = {}
for c in coverages:
k = max(1, int(round(c * len(order))))
out[f"acc@{int(c * 100)}%"] = float(correct[order[:k]].mean())
return out
def bootstrap_ci(correct, n=1000, seed=0):
correct = np.asarray(correct, float)
rng = np.random.default_rng(seed)
means = [correct[rng.integers(0, len(correct), len(correct))].mean() for _ in range(n)]
return float(np.percentile(means, 2.5)), float(np.percentile(means, 97.5))
def summarize(conf, correct):
d = {"n": int(len(correct)), "acc": float(np.mean(correct)),
"acc_ci95": bootstrap_ci(correct), "ece": ece(conf, correct),
"brier": brier(conf, correct), "auroc": auroc(conf, correct)}
d.update(risk_coverage(conf, correct))
return d
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