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()