擅长:python、mysql、java
<p>使用稀疏矩阵本身的解决方案(没有<code>.toarray()</code>)!</p>
<pre><code>tfidf = TfidfVectorizer(stop_words='english')
corpus = [
'I would like to check this document',
'How about one more document',
'Aim is to capture the key words from the corpus',
'frequency of words in a document is called term frequency'
]
X = tfidf.fit_transform(corpus)
feature_names = np.array(tfidf.get_feature_names())
new_doc = ['can key words in this new document be identified?',
'idf is the inverse document frequency caculcated for each of the words']
responses = tfidf.transform(new_doc)
def get_top_tf_idf_words(response, top_n=2):
sorted_nzs = np.argsort(response.data)[:-(top_n+1):-1]
return feature_names[response.indices[sorted_nzs]]
print([get_top_tf_idf_words(response,2) for response in responses])
#[array(['key', 'words'], dtype='<U9'),
array(['frequency', 'words'], dtype='<U9')]
</code></pre>