sklearn画ＲＯＣ曲线

#coding:utf-8
print(__doc__)

import numpy as np
from scipy import interp
import matplotlib.pyplot as plt

from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.cross_validation import StratifiedKFold

###############################################################################
# Data IO and generation,导入iris数据，做数据准备

# import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target
X, y = X[y != 2], y[y != 2]
n_samples, n_features = X.shape

# Add noisy features
random_state = np.random.RandomState(0)
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]

###############################################################################
# Classification and ROC analysis
#分类，做ROC分析

# Run classifier with cross-validation and plot ROC curves
#使用6折交叉验证，并且画ROC曲线
cv = StratifiedKFold(y, n_folds=6)
classifier = svm.SVC(kernel='linear', probability=True,
                     random_state=random_state)

mean_tpr = 0.0
mean_fpr = np.linspace(0, 1, 100)
all_tpr = []

for i, (train, test) in enumerate(cv):
    print test
    #通过训练数据，使用svm线性核建立模型，并对测试集进行测试，求出预测得分
    probas_ = classifier.fit(X[train], y[train]).predict_proba(X[test])
    # Compute ROC curve and area the curve
    #通过roc_curve()函数，求出fpr和tpr，以及阈值
    fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])
    mean_tpr += interp(mean_fpr, fpr, tpr)            #对mean_tpr在mean_fpr处进行插值，通过scipy包调用interp()函数
    mean_tpr[0] = 0.0                                 #初始处为0
    roc_auc = auc(fpr, tpr)
    #画图，只需要plt.plot(fpr,tpr),变量roc_auc只是记录auc的值，通过auc()函数能计算出来
    plt.plot(fpr, tpr, lw=1, label='ROC fold %d (area = %0.2f)' % (i, roc_auc))

#画对角线
plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Luck')

mean_tpr /= len(cv)                     #在mean_fpr100个点，每个点处插值插值多次取平均
mean_tpr[-1] = 1.0                         #坐标最后一个点为（1,1）
mean_auc = auc(mean_fpr, mean_tpr)        #计算平均AUC值
#画平均ROC曲线
#print mean_fpr,len(mean_fpr)
#print mean_tpr
plt.plot(mean_fpr, mean_tpr, 'k--',
         label='Mean ROC (area = %0.2f)' % mean_auc, lw=2)

plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 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()