File size: 2,094 Bytes
69def8e | 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 | import numpy as np
def compute_metrics(scores, labels, threshold, score_is_distance=False):
"""
If score_is_distance, lower score means more similar. (e.g. Euclidean)
If not, higher score means more similar. (e.g. Cosine)
"""
scores = np.asarray(scores)
labels = np.asarray(labels)
if len(scores) == 0:
return {}
if score_is_distance:
preds = (scores < threshold).astype(int)
else:
preds = (scores >= threshold).astype(int)
tp = np.sum((preds == 1) & (labels == 1))
fp = np.sum((preds == 1) & (labels == 0))
tn = np.sum((preds == 0) & (labels == 0))
fn = np.sum((preds == 0) & (labels == 1))
acc = (tp + tn) / len(labels)
tpr = tp / (tp + fn) if (tp + fn) > 0 else 0
fpr = fp / (fp + tn) if (fp + tn) > 0 else 0
precision = tp / (tp + fp) if (tp + fp) > 0 else 0
recall = tpr
f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
return {
"accuracy": float(acc),
"tpr": float(tpr),
"fpr": float(fpr),
"precision": float(precision),
"recall": float(recall),
"f1": float(f1),
"tp": int(tp),
"fp": int(fp),
"tn": int(tn),
"fn": int(fn)
}
def get_confusion_matrix(scores, labels, threshold, score_is_distance=False):
m = compute_metrics(scores, labels, threshold, score_is_distance)
if not m:
return {}
return {
"tp": m["tp"],
"fp": m["fp"],
"tn": m["tn"],
"fn": m["fn"]
}
def compute_confidence(score, threshold, k=10, score_is_distance=False):
"""
Computes a calibrated confidence score in [0.5, 1.0] for the decision.
Uses a sigmoid transformation centered at the threshold.
"""
if score_is_distance:
val = threshold - score
else:
val = score - threshold
# prob in [0, 1], 0.5 at val=0 (score=threshold)
prob = 1.0 / (1.0 + np.exp(-k * val))
confidence = prob if prob >= 0.5 else 1.0 - prob
return float(confidence)
|