#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon May 25 20:27:38 2020@author: lg """import pandas as pd import matplotlib.pyplot as plt import numpy as np#%matplotlib inline#測(cè)試樣本的數(shù)量 parameter=100data=pd.DataFrame(index=range(0,parameter),columns=('probability','The true label')) data['The true label']=np.random.randint(0,2,size=len(data)) data['probability']=np.random.choice(np.arange(0.1,1,0.1),len(data['probability']))cm=np.arange(4).reshape(2,2)cm[0,0]=len(data[data['The true label']==0][data['probability']<0.5])#TN cm[0,1]=len(data[data['The true label']==0][data['probability']>=0.5])#FP cm[1,0]=len(data[data['The true label']==1][data['probability']<0.5]) #FN cm[1,1]=len(data[data['The true label']==1][data['probability']>=0.5])#TPimport itertools classes = [0,1] plt.figure() plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues) plt.title('Confusion matrix') tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=0) plt.yticks(tick_marks, classes) thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):plt.text(j, i, cm[i, j],horizontalalignment="center",color="white" if cm[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label')data.sort_values('probability',inplace=True,ascending=False)TPRandFPR=pd.DataFrame(index=range(len(data)),columns=('TP','FP'))for j in range(len(data)):data1=data.head(n=j+1)FP=len(data1[data1['The true label']==0] [data1['probability']>=data1.head(len(data1))['probability']])/float(len(data[data['The true label']==0]))TP=len(data1[data1['The true label']==1][data1['probability']>=data1.head(len(data1))['probability']])/float(len(data[data['The true label']==1])) TPRandFPR.iloc[j]=[TP,FP]from sklearn.metrics import auc AUC= auc(TPRandFPR['FP'],TPRandFPR['TP'])plt.scatter(x=TPRandFPR['FP'],y=TPRandFPR['TP'],label='(FPR,TPR)',color='k') plt.plot(TPRandFPR['FP'], TPRandFPR['TP'], 'k',label='AUC = %0.2f'% AUC) plt.legend(loc='lower right')plt.title('Receiver Operating Characteristic') plt.plot([(0,0),(1,1)],'r--') plt.xlim([-0.01,1.01]) plt.ylim([-0.01,01.01]) plt.ylabel('True Positive Rate') plt.xlabel('False Positive Rate') plt.show()

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