title: tf-idf feature weights using sklearn.feature_extraction.text.TfidfVectorizer
body: <p>this page: <a href="http://scikit-learn.org/stable/modules/feature_extraction.html">http://scikit-learn.org/stable/modules/feature_extraction.html</a> mentions:</p>
  
  <blockquote>
    <p>As tf–idf is a very often used for text features, there is also another class called <strong>TfidfVectorizer</strong> that combines all the option of <strong>CountVectorizer</strong> and <strong>TfidfTransformer</strong> in a single model.</p>
  </blockquote>
  
  <p>then I followed the code and use fit_transform() on my corpus. How to get the weight of each feature computed by fit_transform()? </p>
  
  <p>I tried: </p>
  
  <pre><code>In [39]: vectorizer.idf_
  ---------------------------------------------------------------------------
  AttributeError                            Traceback (most recent call last)
  &lt;ipython-input-39-5475eefe04c0&gt; in &lt;module&gt;()
  ----&gt; 1 vectorizer.idf_
  
  AttributeError: 'TfidfVectorizer' object has no attribute 'idf_'
  </code></pre>
  
  <p>but this attribute is missing. </p>
  
  <p>Thanks</p>