为什么我会收到一条错误消息，指出测试数据的特征数量较少？

Question

我正在尝试在包含 25000 条电影评论的数据集上实施 LinearSVC 模型。 12,500 条是正面标记的评论，其余是负面的。我正在尝试使用 TfidfVectorizer.

对数据进行矢量化

这是我的代码：

import pandas as pd
from sklearn.svm import LinearSVC
from sklearn.feature_extraction.text import TfidfVectorizer

k=0

# reading training data from data-set and taking test data as input

rev=input('Enter:')
rev=rev.replace("<br />", " ")
data_folder= "C:/Users/Files/Desktop/Dataset/train"
for sentiment in (["/neg/","/pos/"]):
    #path would be the directory path 
    path=data_folder+sentiment
    #filename will store the NAME of the files that you want to access
    for filename in sorted(os.listdir(path)):
        #open file
        with open(path+ filename,"r",encoding= 'utf-8') as f:
                review=f.read()
                review=review.replace("<br />", " ")
                total.append(review)

# removing stop-words from data

for i in total:
    for j in stop_words:
        if j in i:
            i.replace(j,'')

for i in [rev]:
    for j in stop_words:
        if j in i:
            i.replace(j,'')

c=TfidfVectorizer()
f=c.fit_transform(total).toarray()
tst=c.fit_transform([rev]).toarray()


# 0 for negative data and 1 for positive data

while k!=12500:
    l.append(0)
    k+=1
while k!=25000:
    l.append(1)
    k+=1
m=LinearSVC(random_state=0,tol=1e-5)
m.fit(f,l)

if(m.predict(tst).tolist().count(1)>m.predict(tst).tolist().count(0)):
    print('Positive')
else:
    print('Negative')

每次我运行这段代码，我都会收到这个错误

ValueError: X has 139 features per sample; expecting 79897

这个错误是什么意思，我该如何解决？

Answer 1

好的，我找到了解决方案，只需要使用 transform() 代替 fit_transform() 来向量化火车数据，即

c=TfidfVectorizer()
f=c.fit_transform(total).toarray()
tst=c.transform([rev]).toarray()

为什么我会收到一条错误消息，指出测试数据的特征数量较少？

Why am I getting an error saying that test data has lesser number of features?

python

libsvm

scikit-learn