绘制ROC曲线首选肯定是查阅sklearn.metrics.roc_curve(y_true, y_score, pos_label=None, sample_weight=None, drop_intermediate=True)
但是所谓y_score到底指的是什么？查找网上相关经验时，发现几乎所有文章都用的同一个例子，但是并没有解决我的疑惑，于是多次尝试该值的计算方法，发现probas_=model.predict_proba(X[test]) 可以计算得到每个样本为正例的概率，该函数经测试多个模型发现都是适用的，虽然官方文档指出decision_function（）适用于某些分类器，但是目前我所看到的只有svc分类器使用了该函数计算y_score,遇到画roc曲线可以先尝试使用.predict_proba函数
接下来fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1]) 该函数传入的参数一定要注意是一列，否则会报错（数组类型错误）该函数则得到我们想要的roc曲线的横纵坐标数组，
另外 roc_auc = auc(fpr, tpr) 计算该model的auc值。
接下来附上完整代码：
def work1():
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc ###计算roc和auc
from sklearn import cross_validation
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB, GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn import neighbors
# Import some data to play with
data = pd.read_excel("orange_train.xls")#读取数据（'Var11', 'Var21', 'Var22','Var28','Var39','Var46','Var47','Var50'为特征名，每列为一个特征）
xdata = (data.ix[0:10000, ['Var11', 'Var21', 'Var22','Var28','Var39','Var46','Var47','Var50']])##只取了文档前9999个
# print(xdata.shape)
ydata = pd.read_csv("orange_large_train_churn.txt", header=None)#label文档（只有一列）
y = np.array(ydata[0].tolist()[:9999])#只取了文档前9999个
# print(ydata.shape)
X = np.array(xdata)
print(X.shape)
print(y.shape)
# shuffle and split training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, random_state=0)
#构建模型
# svm = svm.SVC(kernel='linear', probability=True, random_state=random_state)
clf_NB = GaussianNB()
clf = LogisticRegression(random_state=12)
clf_Tree = DecisionTreeClassifier(random_state=0)
kn_clf = neighbors.KNeighborsClassifier()
# ###通过predict_proba（） decision_function()计算得到的y_score的值，用在roc_curve()函数中
y_score = clf.fit(X_train, y_train)
y_pred = y_score.predict_proba(X_test)
y_score0 = clf_NB.fit(X_train, y_train).predict_proba(X_test)
y_score1 = clf_Tree.fit(X_train, y_train).predict_proba(X_test)
y_score2 = kn_clf.fit(X_train, y_train).predict_proba(X_test)
# Compute ROC curve and ROC area for each class
fpr, tpr, threshold = roc_curve(y_test, y_pred[:, 1]) ###计算真正率和假正率
print(y_test,y_pred)
print(fpr, tpr, threshold)
roc_auc = auc(fpr, tpr) ###计算auc的值
fpr0, tpr0, threshold0 = roc_curve(y_test, y_score0[:, 1]) ###计算真正率和假正率
roc_auc0 = auc(fpr0, tpr0)
fpr1, tpr1, threshold1 = roc_curve(y_test, y_score1[:, 1]) ###计算真正率和假正率
roc_auc1 = auc(fpr1, tpr1)
fpr2, tpr2, threshold2 = roc_curve(y_test, y_score2[:, 1]) ###计算真正率和假正率
roc_auc2 = auc(fpr2, tpr2)
#画图
plt.plot(fpr, tpr, color='darkorange',
label='LR ROC curve (area = %0.2f)' % roc_auc) ###假正率为横坐标，真正率为纵坐标做曲线
plt.plot(fpr0, tpr0, "r-",
label='NB ROC curve (area = %0.2f)' % roc_auc0)
plt.plot(fpr1, tpr1, "y-",
label='TREE ROC curve (area = %0.2f)' % roc_auc1)
plt.plot(fpr2, tpr2, "b-",
label='KNN ROC curve (area = %0.2f)' % roc_auc2)
plt.plot([0, 1], [0, 1], color='navy',linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()