import numpy as np
 from sklearn.metrics import roc_auc_score
 y_true = np.array([0, 0, 1, 1])
 y_scores = np.array([0.1, 0.4, 0.35, 0.8])
 roc_auc_score(y_true, y_scores)

输出：0.75

 import numpy as np
 from sklearn import metrics
 y = np.array([1, 1, 2, 2])  #实际值
 scores = np.array([0.1, 0.4, 0.35, 0.8]) #预测值
 fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=2) #pos_label=2，表示值为2的实际值为正样本
 print fpr
 print tpr
 print thresholds

输出：
array([ 0. , 0.5, 0.5, 1. ])
array([ 0.5, 0.5, 1. , 1. ])
array([ 0.8 , 0.4 , 0.35, 0.1 ])