Python中的TF-IDF矩阵

Question

我为语料库计算TF-IDF的代码如下：

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer

train_set = "i have a ball", "he is good", "she played well" 
vectorizer = TfidfVectorizer(min_df=1)

train_array = vectorizer.fit_transform(train_set).toarray()
print(vectorizer.get_feature_names())
print(train_array)

我收到的输出是：

['ball', 'good', 'have', 'he', 'is', 'played', 'she', 'well']

[[0.70710678, 0., 0.70710678, 0., 0., 0., 0., 0.],
 [0., 0.57735027, 0., 0.57735027, 0.57735027, 0., 0., 0.],
 [0., 0., 0., 0., 0., 0.57735027, 0.57735027, 0.57735027]]

问题是如何计算句子的TF-IDF："she is good"？语料库是上述代码中的train_set。

Answer 1

您只需使用TF-IDF方法将新的数据应用于.transform矢量图：

In [16]: test = ["she is good"]

In [17]: test_array = vectorizer.transform(test)

In [18]: test_array.A
Out[18]: array([[0., 0.57735027, 0., 0., 0.57735027, 0., 0.57735027, 0.]])

In [19]: vectorizer.get_feature_names()
Out[19]: ['ball', 'good', 'have', 'he', 'is', 'played', 'she', 'well']