如何知道單詞的特定TF-IDF值？

Question

如何使用TfidfVectorizer函數知道特定單詞的值？ 例如，我的代碼是：

docs = [] 
docs.append("this is sentence number one") 
docs.append("this is sentence number two") 
vectorizer = TfidfVectorizer(norm='l2',min_df=0, use_idf=True, smooth_idf=True, stop_words='english', sublinear_tf=True) 
sklearn_representation = vectorizer.fit_transform(docs) 

現在，我怎麼能知道 「句子」 的句子2的TF-IDF值（文檔[1]）？

Answer 1

您需要使用vectorizer的vocabulary_屬性，該屬性是項與特徵索引的映射。

>>> from sklearn.feature_extraction.text import TfidfVectorizer 
>>> docs = [] 
>>> docs.append("this is sentence number one") 
>>> docs.append("this is sentence number two") 
>>> vectorizer = TfidfVectorizer(norm='l2',min_df=0, use_idf=True, smooth_idf=True, stop_words='english', sublinear_tf=True) 
>>> x = vectorizer.fit_transform(docs) 
>>> x.todense() 
matrix([[ 0.70710678, 0.70710678], 
     [ 0.70710678, 0.70710678]]) 
>>> vectorizer.vocabulary_['sentence'] 
1 
>>> c = vectorizer.vocabulary_['sentence'] 
>>> x[:,c] 
<2x1 sparse matrix of type '<class 'numpy.float64'>' 
    with 2 stored elements in Compressed Sparse Row format> 
>>> x[:,c].todense() 
matrix([[ 0.70710678], 
     [ 0.70710678]])