CHB-MIT数据集.rar

# -*- coding: utf-8 -*- """ Created on Sat May 2 15:45:36 2020 @author: ILQMN """ import pandas as pd from sklearn.model_selection import train_test_split import numpy as np from sklearn.metrics import accuracy_score from sklearn.metrics import confusion_matrix from sklearn.neighbors import KNeighborsClassifier #导入KNN from sklearn.svm import SVC #导入SVC from sklearn.tree import DecisionTreeClassifier #导入DT from vecstack import stacking #种类，特征向量 names = ['class','chb03_biaozhuncha','chb03_biaozhuncha_d1','chb03_biaozhuncha_d2','chb03_biaozhuncha_d3','chb03_biaozhuncha_d4','chb03_biaozhuncha_d5'] data = pd.read_csv(r'E:\大学\大四学年（2019-2020）\下学期\毕设\数据\CHB-MIT数据集\stacking\标准差\chb03_biaozhuncha.csv',header=None,names=names) label = data[['class']] sub_data = data.iloc[:,1:] sub_data_train,sub_data_test,label_train,label_test = train_test_split(sub_data,label,test_size = 0.2,random_state = 0) models = [ SVC(C=2.8284,kernel='rbf',degree=3,gamma=22.6274,coef0=0.0,shrinking=True,probability=False,tol=0.001), KNeighborsClassifier(n_neighbors=9,weights='uniform',algorithm='auto',p=2,metric='minkowski',metric_params=None,radius=None)] S_train,S_test = stacking(models, sub_data_train,label_train,sub_data_test, regression=False, mode='oof_pred_bag', needs_proba=False, save_dir=None, metric=accuracy_score, n_folds=5, stratified=True, shuffle=True, random_state=0, verbose=2 ) #cart算法采用的是 GINI 指数 model = DecisionTreeClassifier(criterion="gini",splitter="best",max_depth=None) model = model.fit(S_train,label_train) y_pred = model.predict(S_test) #计算混淆矩阵的值 senpe = confusion_matrix(label_test,y_pred) #打印准确度 print('预测结果：[%.8f]' % accuracy_score(label_test,y_pred)) #打印混淆矩阵 print(senpe)