来自 sklearn 的 Tfidfvectorizer - 如何获取矩阵

Question

我想从 sklearn 的 Tfidfvectorizer 对象中获取矩阵。这是我的代码：

from sklearn.feature_extraction.text import TfidfVectorizer
text = ["The quick brown fox jumped over the lazy dog.",
        "The dog.",
        "The fox"]

vectorizer = TfidfVectorizer()
vectorizer.fit_transform(text)

这是我尝试过但返回的错误：

vectorizer.toarray()

--------------------------------------------------------------------------- 
AttributeError                            Traceback (most recent call last) <ipython-input-117-76146e626284> in <module>()   
----> 1 vectorizer.toarray()

AttributeError: 'TfidfVectorizer' object has no attribute 'toarray'

再次尝试

vectorizer.todense()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-118-6386ee121184> in <module>()
----> 1 vectorizer.todense()

AttributeError: 'TfidfVectorizer' object has no attribute 'todense'

Answer 1

.fit_transform本身returns一个文档术语矩阵。所以，你这样做：

matrix = vectorizer.fit_transform(text)

matrix.todense() 用于将稀疏矩阵转换为密集矩阵。
matrix.shape 会给你矩阵的形状。

Answer 2

请注意，vectorizer.fit_transform returns 您要获取的术语文档矩阵。所以保存它 returns，并使用 todense，因为它将是稀疏格式：

Returns: X : sparse matrix, [n_samples, n_features]. Tf-idf-weighted document-term matrix.

a = vectorizer.fit_transform(text)
a.todense()

matrix([[0.36388646, 0.27674503, 0.27674503, 0.36388646, 0.36388646,
         0.36388646, 0.36388646, 0.42983441],
        [0.        , 0.78980693, 0.        , 0.        , 0.        ,
         0.        , 0.        , 0.61335554],
        [0.        , 0.        , 0.78980693, 0.        , 0.        ,
         0.        , 0.        , 0.61335554]])

来自 sklearn 的 Tfidfvectorizer - 如何获取矩阵

Tfidfvectorizer from sklearn - how to get matrix

python

tf-idf

scikit-learn

tfidfvectorizer