在不同的数据预处理情况下训练得到了三个SVM模型，结果都差不多，对这三个模型的分类结果进行投票

1、三个模型的model_path

# 最终model的path
self.model_path = log_path+'/'+model_name+'_model.gz'
self.time_log = log_path+'/'+model_name+'_time_log.csv'

# 模型1
self.model1_path = 'F:/20230911_bcic_windowlenth/4/subject_dependent_2_classes' + '/' + model_name + '_model.gz'
# 模型2
self.model2_path = 'F:/20230911_bcic_windowlenth/2_1/subject_dependent_2_classes' + '/' + model_name + '_model.gz'
# 模型3
self.model3_path = 'F:/20230911_bcic_windowlenth/2_2/subject_dependent_2_classes' + '/' + model_name + '_model.gz'

2、导入、拟合、保存

# model1
clf1 = load(self.model1_path)
# model2
clf2 = load(self.model2_path)
# model3
clf3 = load(self.model3_path)

estimators = [('svm', clf1), ('svm1', clf2), ('svm2', clf3)]

# soft/hard
classifier = VotingClassifier(estimators, voting="soft")

classifier.fit(X_train, y_train)
# save
dump(classifier, self.model_path)

3、predict

classifier = load(self.model_path)
vote_acc = classifier.score(X_test, y_test)

遇到的问题：

AttributeError: predict_proba is not available when  probability=False

分别在model1、model2、model3训练svm的时候按照上述设置