python 中 CountVectorier 稀疏矩阵中的列名称

Question

当我使用下面的代码时：

from sklearn.feature_extraction.text import CountVectorizer
X = dataset.Tweet
y = dataset.Type

count_vect = CountVectorizer()
BoW = count_vect.fit_transform(X)

它将returns词频文档作为一个稀疏矩阵。

我找到了如何获取稀疏矩阵的数据、索引和 indptr。

我的问题是如何获取列的名称（应该是特征或单词）？

Answer 1

您要使用的是vectorizer.get_feature_names()。这是文档中的示例：

from sklearn.feature_extraction.text import CountVectorizer
corpus = [
    'This is the first document.',
    'This document is the second document.',
    'And this is the third one.',
    'Is this the first document?',
]
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())
# ['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']
print(X.toarray())  
# [[0 1 1 1 0 0 1 0 1]
#  [0 2 0 1 0 1 1 0 1]
#  [1 0 0 1 1 0 1 1 1]
#  [0 1 1 1 0 0 1 0 1]]

文档 link：https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html

python 中 CountVectorier 稀疏矩阵中的列名称

The names of the columns in CountVectorier sparse matrix in python

python

sparse-matrix

text-classification

countvectorizer