# -*- coding: utf-8 -*- ''' Created on 2018年1月19日 @author: Jason.F @summary: ROC(receiver operator characteristic，基于模型真正率和假正率等性能指標評估分類模型 ''' import pandas as pd from sklearn.preprocessing import LabelEncoder from sklearn.cross_validation import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline from sklearn.svm import SVC import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LogisticRegression from sklearn.metrics import roc_curve,auc,roc_auc_score,accuracy_score from scipy import interp from sklearn.cross_validation import StratifiedKFold from sklearn.svm import SVC #導入數據 df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data',header=None) X=df.loc[:,2:].values y=df.loc[:,1].values le=LabelEncoder() y=le.fit_transform(y)#類標整數化 print (le.transform(['M','B'])) #劃分訓練集合測試集 X_train,X_test,y_train,y_test = train_test_split (X,y,test_size=0.20,random_state=1) #建立pipeline pipe_lr=Pipeline([('scl',StandardScaler()),('clf',LogisticRegression(random_state=1))]) #ROC計算 X_train2=X_train[:,[4,14]] cv=StratifiedKFold(y_train,n_folds=3,random_state=1) fig=plt.figure(figsize=(7,5)) mean_tpr=0.0 mean_fpr=np.linspace(0,1,100) all_tpr=[]for i,(train,test) in enumerate(cv):probas=pipe_lr.fit(X_train2[train],y_train[train]).predict_proba(X_train2[test])fpr,tpr,thresholds = roc_curve(y_train[test],probas[:,1],pos_label=1)mean_tpr+=interp(mean_fpr,fpr,tpr)mean_tpr[0]=0.0roc_auc = auc(fpr,tpr)plt.plot(fpr,tpr,lw=1,label='ROC fold %d (area = %0.2f)'%(i+1,roc_auc))plt.plot([0,1],[0,1],linestyle='--',color=(0.6,0.6,0.6),label='random guessing') mean_tpr/=len(cv) mean_tpr[-1]=1.0 mean_auc=auc(mean_fpr,mean_tpr) plt.plot(mean_fpr,mean_tpr,'k--',label='mean ROC (area=%0.2f)'%mean_auc,lw=2) plt.plot([0,0,1],[0,1,1],lw=2,linestyle=':',color='black',label='perfect performance') 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 Operator Characteristic') plt.legend(loc='lower right') plt.show()#直接使用roc_auc_curve函數 pipe_svc=Pipeline([('scl',StandardScaler()),('clf',SVC(random_state=1))]) pipe_svc=pipe_svc.fit(X_train2,y_train) y_pred2=pipe_svc.predict(X_test[:,[4,14]]) print ('ROC AUC:%.3f'%roc_auc_score(y_true=y_test,y_score=y_pred2)) print ('Accuracy:%.3f'%accuracy_score(y_true=y_test,y_pred=y_pred2)) 結果：

[1 0] ROC AUC:0.671 Accuracy:0.728

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