Scikit学习TfidfVectorizer:如何获得tf-idf得分最高的前n个词 [英] Scikit Learn TfidfVectorizer : How to get top n terms with highest tf-idf score

问题描述

我正在研究关键字提取问题.考虑一个非常普遍的情况

I am working on keyword extraction problem. Consider the very general case

tfidf = TfidfVectorizer(tokenizer=tokenize, stop_words='english')

t = """Two Travellers, walking in the noonday sun, sought the shade of a widespreading tree to rest. As they lay looking up among the pleasant leaves, they saw that it was a Plane Tree.

"How useless is the Plane!" said one of them. "It bears no fruit whatever, and only serves to litter the ground with leaves."

"Ungrateful creatures!" said a voice from the Plane Tree. "You lie here in my cooling shade, and yet you say I am useless! Thus ungratefully, O Jupiter, do men receive their blessings!"

Our best blessings are often the least appreciated."""

tfs = tfidf.fit_transform(t.split(" "))
str = 'tree cat travellers fruit jupiter'
response = tfidf.transform([str])
feature_names = tfidf.get_feature_names()

for col in response.nonzero()[1]:
    print(feature_names[col], ' - ', response[0, col])

这给了我

  (0, 28)   0.443509712811
  (0, 27)   0.517461475101
  (0, 8)    0.517461475101
  (0, 6)    0.517461475101
tree  -  0.443509712811
travellers  -  0.517461475101
jupiter  -  0.517461475101
fruit  -  0.517461475101

这很好.对于其中出现的任何新文档，是否有办法获得tfidf得分最高的前n个术语?

which is good. For any new document that comes in, is there a way to get the top n terms with the highest tfidf score?

推荐答案

您必须做一点点的歌舞才能将矩阵转换为numpy数组，但这应该可以满足您的需求:

You have to do a little bit of a song and dance to get the matrices as numpy arrays instead, but this should do what you're looking for:

feature_array = np.array(tfidf.get_feature_names())
tfidf_sorting = np.argsort(response.toarray()).flatten()[::-1]

n = 3
top_n = feature_array[tfidf_sorting][:n]

这给了我

array([u'fruit', u'travellers', u'jupiter'], 
  dtype='<U13')

argsort调用确实非常有用，请此处是它的文档.我们必须执行[::-1]，因为argsort仅支持从小到大的排序.我们调用flatten将尺寸减小到1d，以便可以使用排序后的索引来索引1d特征数组.请注意，仅当您一次测试一个文档时，才包含对flatten的调用.

The argsort call is really the useful one, here are the docs for it. We have to do [::-1] because argsort only supports sorting small to large. We call flatten to reduce the dimensions to 1d so that the sorted indices can be used to index the 1d feature array. Note that including the call to flatten will only work if you're testing one document at at time.

另外，在另一个注释上，您是说类似tfs = tfidf.fit_transform(t.split("\n\n"))一样吗?否则，多行字符串中的每个术语都将被视为文档".相反，使用\n\n意味着我们实际上正在查看4个文档(每行一个)，这在您考虑tfidf时更有意义.

Also, on another note, did you mean something like tfs = tfidf.fit_transform(t.split("\n\n"))? Otherwise, each term in the multiline string is being treated as a "document". Using \n\n instead means that we are actually looking at 4 documents (one for each line), which makes more sense when you think about tfidf.

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