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)

这是我尝试并得到的错误：

vectorizer.toarray()

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

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

另一种尝试

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本身返回文档术语矩阵。因此，您可以这样做：

matrix = vectorizer.fit_transform(text)

matrix.todense()用于将稀疏矩阵转换为密集矩阵。
matrix.shape将为您提供矩阵形状。

Answer 2

请注意，vectorizer.fit_transform返回要获取的术语文档矩阵。因此，请保存返回的内容，并使用todense，因为它将采用稀疏格式：

返回：X：稀疏矩阵，[n_samples，n_features]。 TF-IDF加权文档期限矩阵。

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]])

sklearn的Tfidfvectorizer-如何获取矩阵

2 个答案: