python实现绘制多个模型的ROC曲线

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import pandas as pd

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import seaborn as sns

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import matplotlib.pyplot as plt

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from sklearn.metrics import roc_curve, auc

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def Draw_ROC(file1,file2):

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data1=pd.read_excel(file1)

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data1=pd.DataFrame(data1)

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data2=pd.read_excel(file2)

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data2=pd.DataFrame(data2)

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fpr_CSNN,tpr_CSNN,thresholds=roc_curve(list(data1['真实标签']),

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list(data1['positive概率']))

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roc_auc_CSSSNN=auc(fpr_CSNN,tpr_CSNN)

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fpr_NN,tpr_NN,thresholds=roc_curve(list(data2['真实标签']),

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list(data2['positive概率']))

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roc_auc_DL=auc(fpr_NN,tpr_NN)

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font = {'family': 'Times New Roman',

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'size': 12,

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}

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sns.set(font_scale=1.2)

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plt.rc('font',family='Times New Roman')

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plt.plot(fpr_NN,tpr_NN,'purple',label='NN_AUC = %0.2f'% roc_auc_DL)

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plt.plot(fpr_CSNN,tpr_CSNN,'blue',label='CSNN_AUC = %0.2f'% roc_auc_CSSSNN)

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plt.legend(loc='lower right',fontsize = 12)

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plt.plot([0,1],[0,1],'r--')

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plt.ylabel('True Positive Rate',fontsize = 14)

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plt.xlabel('Flase Positive Rate',fontsize = 14)

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plt.show()

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if __name__=="__main__":

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Draw_ROC('F:医学大数据课题RA预测RA预测CSSSDL_0.92-0.86-0.90.xlsx',

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'F:医学大数据课题RA预测RA预测DL_0.83-0.83-0.83.xlsx')