在Python中手工计算AUC

import numpy as np
from sklearn.metrics import roc_auc_score

y_true = np.array([0, 1, 1, 0, 1])
y_pred = np.array([0.2, 0.6, 0.8, 0.3, 0.9])

sorted_indices = np.argsort(y_pred)

positive_ranks = np.sum(y_true[sorted_indices] == 1)
negative_ranks = np.sum(y_true[sorted_indices] == 0)

auc = (positive_ranks - (positive_ranks * (positive_ranks + 1)) / 2) / (positive_ranks * negative_ranks)

print("手工计算的AUC值为:", auc)

import numpy as np
from sklearn.metrics import roc_auc_score

y_true = np.array([0, 1, 1, 0, 1])
y_pred = np.array([0.2, 0.6, 0.8, 0.3, 0.9])

sorted_indices = np.argsort(y_pred)
positive_ranks = np.sum(y_true[sorted_indices] == 1)
negative_ranks = np.sum(y_true[sorted_indices] == 0)

auc = (positive_ranks - (positive_ranks * (positive_ranks + 1)) / 2) / (positive_ranks * negative_ranks)

print("手工计算的AUC值为:", auc)