from sklearn.metrics import roc_curve

labels=[1,1,0,0,1]

preds=[0.8,0.7,0.3,0.6,0.5]

fpr, tpr, thresholds = roc_curve(labels, preds)

print(fpr)
print(tpr)
print(thresholds)

auc=0

init_x=0

for x,y in zip(fpr,tpr):

    if x!=init_x:

        auc+=(x-init_x)*y

        init_x=x

print(auc)

或者直接利用下面语句直接计算即可
from sklearn.metrics import roc_auc_score
auc = roc_auc_score(labels, preds)

plt.plot(fpr,tpr)

plt.title("auc=%.4f"%(auc))

plt.xlabel("False Positive Rate")

plt.ylabel("True Positive Rate")

plt.fill_between(fpr, tpr, where=(tpr>0), color='green', alpha=0.5)

plt.show()