列出测试数据中的错误预测！ - Python

如何解决列出测试数据中的错误预测！ - Python

我试图在测试集中列出所有错误的预测，但不确定如何去做。我尝试过 Stackoverflow，但可能搜索了错误的“问题”。所以我从一个文件夹中获得了这些文本文件，其中包含电子邮件。问题是我的预测效果不佳，我想检查预测错误的电子邮件。目前我的代码片段看起来像这样：

 no_head_train_path_0 = 'folder_name' 
 no_head_train_path_1 = 'folder_name'

def get_data(path):
text_list = list()
files = os.listdir(path)
for text_file in files:
    file_path = os.path.join(path,text_file)
    read_file = open(file_path,'r+')
    read_text = read_file.read()
    read_file.close()
    cleaned_text = clean_text(read_text)
    text_list.append(cleaned_text)
return text_list,files

no_head_train_0,temp = get_data(no_head_train_path_0)
no_head_train_1,temp1 = get_data(no_head_train_path_1)
no_head_train = no_head_train_0 + no_head_train_1
no_head_labels_train = ([0] * len(no_head_train_0)) + ([1] * len(no_head_train_1))

def vocabularymat(TEXTFILES,VOC,PLAY,METHOD):
from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer

 if (METHOD == "TDM"):
    voc = CountVectorizer()
    voc.fit(VOC)

    if (PLAY == "TRAIN"):
        TrainMat = voc.transform(TEXTFILES)
        return TrainMat

    if (PLAY =="TEST"):
        TestMat = voc.transform(TEXTFILES)
        return TestMat
TrainMat = vocabularymat(no_head_train,no_head_train,PLAY= "TRAIN",METHOD="TDM")



X_train = Featurelearning(Traindata,Method="NMF")
y_train = datalabel 
X_train,X_test,y_train,y_test = train_test_split(data,datalabel,test_size=0.33,random_state=42

model = LogisticRegression() 
model.fit(X_train,y_train)


expected = y_test
predicted = model.predict(X_test)
proba = model.predict_proba(X_test)

accuracy = accuracy_score(expected,predicted)
recall = recall_score(expected,predicted,average="binary")
precision = precision_score(expected,average="binary")
f1 = f1_score(expected,average="binary")

是否可以找到预测错误的电子邮件/文件名，以便我可以手动检查它们？（抱歉代码太长）

解决方法

您可以使用 NumPy 创建一个布尔向量，指示哪些预测是错误的，然后使用该向量来索引您的文件名数组。例如：

import numpy as np

# mock data
files = np.array(['mail1.txt','mail2.txt','mail3.txt','mail4.txt'])
y_test = np.array([0,1,1])
predicted = np.array([0,1])

# create a Boolean index for the wrong classifications
classification_is_wrong = y_test != predicted

# print the file names of the wrongly classified mails
print(files[classification_is_wrong])

输出： ['mail2.txt' 'mail3.txt']