1. 小型分类模型对比，可以直接调用的

用一样的数据集做示例，简单地直接分别得到每个分类模型预测的结果。

from sklearn.datasets import load_breast_cancer
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
from sklearn.model_selection import train_test_split
import pylab as plt
import warnings; warnings.filterwarnings('ignore')

dataset = load_breast_cancer()
data = dataset.data
target = dataset.target
X_train, X_test, y_train, y_test = train_test_split(data,target,test_size=0.2)
# 模型1调用sklearn中的RandomForestClassifier
rf1 = RandomForestClassifier(n_estimators=5)
rf1.fit(X_train, y_train)
pred1 = rf1.predict_proba(X_test)[:,1]
# 模型2调用sklearn中的ExtraTreesClassifier
rf2 = ExtraTreesClassifier(n_estimators=5)
rf2.fit(X_train, y_train)
pred2 = rf2.predict_proba(X_test)[:,1]

# 画图部分
fpr1, tpr1, threshold1 = metrics.roc_curve(y_test, pred1)       # <class 'numpy.ndarray'> <class 'numpy.ndarray'>
roc_auc1 = metrics.auc(fpr1, tpr1)

fpr2, tpr2, threshold2 = metrics.roc_curve(y_test, pred2)       # <class 'numpy.ndarray'> <class 'numpy.ndarray'>
roc_auc2 = metrics.auc(fpr2, tpr2)

plt.figure(figsize=(6,6))
plt.title('Validation ROC')
plt.plot(fpr1, tpr1, 'b', label = 'RandomForestClassifier AUC = %0.3f' % roc_auc1)
plt.plot(fpr2, tpr2, 'b', label = 'ExtraTreesClassifier AUC = %0.3f' % roc_auc2)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.savefig("filename.png")
plt.show()
plt.close()

在这里插入图片描述

2. 大型的CNN模型，无法直接得到结果。

2.1 先分别运行每个分类模型，将预测的结果存入csv文件中。

from sklearn.datasets import load_breast_cancer
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
from sklearn.model_selection import train_test_split
import pylab as plt
import warnings; warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression   #线性回归
import csv

dataset = load_breast_cancer()
data = dataset.data
target = dataset.target
X_train, X_test, y_train, y_test = train_test_split(data,target,test_size=0.2)
rf1 = RandomForestClassifier(n_estimators=5)
rf1.fit(X_train, y_train)
pred1 = rf1.predict_proba(X_test)[:,1]

dataframe = pd.DataFrame({'真实标签':y_test,'预测概率':pred1})
dataframe.to_csv("test1.csv",index=False,sep=',')

rf2 = ExtraTreesClassifier(n_estimators=5)
rf2.fit(X_train, y_train)
pred2 = rf2.predict_proba(X_test)[:,1]

dataframe = pd.DataFrame({'真实标签':y_test,'预测概率':pred2})
dataframe.to_csv("test2.csv",index=False,sep=',')

2.2 从csv文件读取每个模型的预测结果，绘制AUC曲线

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc

def Draw_ROC(file1,file2):
    '''这里注意读取csv的编码方式，
    如果csv里有中文，在windows系统上可以直接用encoding='ANSI'，
    但是到了Mac或者Linux系统上会报错：`LookupError: unknown encoding: ansi`。
    解决方法：
    1. 可以改成encoding='gbk'；
    2. 或者把csv文件里的列名改成英文，就不用选择encoding的方式了。
    '''
    data1=pd.read_csv(file1, encoding='ANSI')
    data1=pd.DataFrame(data1)
    data2=pd.read_csv(file2, encoding='ANSI')
    data2=pd.DataFrame(data2)
    print(list(data1['真实标签']), list(data1['positive概率']))
    print(list(data1['真实标签']), list(data2['positive概率']))

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

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

    font = {'family': 'Times New Roman',
            'size': 12,
            }		
    '''这里很多电脑上也许默认是'DejaVu Sans'格式，但是在写论文时，
    往往需要'Times New Roman'格式，可以参考[这篇教程](https://blog.csdn.net/weixin_43543177/article/details/109723328)
    '''
    sns.set(font_scale=1.2)
    plt.rc('font',family='Times New Roman')

    plt.plot(fpr_NN,tpr_NN,'purple',label='NN_AUC = %0.2f'% roc_auc_DL)
    plt.plot(fpr_CSNN,tpr_CSNN,'blue',label='CSNN_AUC = %0.2f'% roc_auc_CSSSNN)
    plt.legend(loc='lower right',fontsize = 12)
    plt.plot([0,1],[0,1],'r--')
    plt.ylabel('True Positive Rate',fontsize = 14)
    plt.xlabel('Flase Positive Rate',fontsize = 14)
    plt.show()

if __name__=="__main__":
    Draw_ROC('./test1.csv',
             './test2.csv')