如何解决 svm fit 错误？

Question

我必须将可执行文件分类为恶意文件和非恶意文件。 我创建了自己的语料库火车。我已经解释了下面的错误。输入文件格式也在下面给出。如何在相应文件中获取具有名称的特征并将其作为数据集保存到文本文件中？如何同时测试多个文件？我是 n-gram 分类的新手，请帮我解决所有这些问题。提前致谢。

from sklearn.datasets import load_files
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_selection import chi2 
from sklearn.feature_selection import SelectKBest
from sklearn import svm   
a = load_files('D:\Train') #contains two folders true(non malicious) and false(malicious). that is targets
vectorizer = CountVectorizer(ngram_range=(4,4))
X = vectorizer.fit_transform(a.data) 
B,c = X, a.target
b_new = SelectKBest(chi2, k=1000).fit_transform(B, c)
clf = svm.SVC(gamma="auto", C=1.)
clf.fit(b_new,a.target)
y = vectorizer.transform(open('D:/data/PRE/chrome.txt'))
le = preprocessing.LabelEncoder()
data = le.fit_transform(matrix)
data = data.reshape(1,-1)
print(clf.predict(data))

错误：

File "D:/spyder/corpus.py", line 59, in <module>
print(clf.predict(data))

ValueError: X.shape[1] = 482180 should be equal to 1000, the number of features at training time

输入文件格式（十六进制文件）

90 00 03 00 00 00 04 00 00 00 FF FF 00 00
00 00 00 00 00 00 40 00 00 00 00 00 00 00
00 00 00 00 00 00 00 00 00 00 00 00 00 00
00 00 00 00 00 00 00 00 00 00 F0 00 00 00
BA 0E 00 B4 09 CD 21 B8 01 4C CD 21 54 68
20 70 72 6F 67 72 61 6D 20 63 61 6E 6E 6F
62 65 20 72 75 6E 20 69 6E 20 44 4F 53 20
64 65 2E 0D 0D 0A 24 00 00 00 00 00 00 00
94 01 36 82 FA 52 36 82 FA 52 36 82 FA 52

更新代码

from sklearn.datasets import load_files
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_selection import chi2 
from sklearn.feature_selection import SelectKBest
from sklearn import svm   
a = load_files('D:\Train') #contains two folders true(non malicious) and false(malicious). that is targets
vectorizer = CountVectorizer(ngram_range=(4,4))
X = vectorizer.fit_transform(a.data) 
B,c = X, a.target
ch2 = SelectKBest(chi2, k=1000)
X_train = ch2.fit_transform(B,c)
clf = svm.SVC()
clf.fit(X_train,a.target) 
y = vectorizer.transform(open('D:/data/PRE/chrome.txt')))
X_test = ch2.transform(y)
print(clf.predict(X_test))

输出

[1 1 1 ..., 1 1 1]

问题 我只给了一个文件作为测试集。也就是说，所有内容都存储在单个数组中。那么它是如何在输出中给出几个 1 的。应该只有一个 1。另一个问题是，对于任何测试数据，它总是给出一个 1 的数组作为输出。它是二进制类，它不返回另一个类。怎么办？

Answer 1

我解决了一个问题。为输出中的单个文件生成单个 1。我只是在下面更改以下代码行。

docs=['D:/data/PRE/office.txt']
y = vectorizer.transform(docs)