title: sklearn GridSearchCV: how to get classification report?
body: <p>I am using GridSearchCV like this:</p>
  
  <pre><code>corpus = load_files('corpus')
  
  with open('stopwords.txt', 'r') as f:
      stop_words = [y for x in f.read().split('\n') for y in (x, x.title())]
  
  x = corpus.data
  
  y = corpus.target
  
  pipeline = Pipeline([
      ('vec', CountVectorizer(stop_words=stop_words)),
      ('classifier', MultinomialNB())])
  
  parameters = {'vec__ngram_range': [(1, 1), (1, 2)],
                'classifier__alpha': [1e-2, 1e-3],
                'classifier__fit_prior': [True, False]}
  
  gs_clf = GridSearchCV(pipeline, parameters, n_jobs=-1, cv=5, scoring="f1", verbose=10)
  
  gs_clf = gs_clf.fit(x, y)
  
  joblib.dump(gs_clf.best_estimator_, 'MultinomialNB.pkl', compress=1)
  </code></pre>
  
  <p>Then, in another file, to classify new documents (not from the corpus), I do this:</p>
  
  <pre><code>  classifier = joblib.load(filepath) # path to .pkl file
    result = classifier.predict(tokenlist)
  </code></pre>
  
  <p>My question is: Where do I get the values needed for the <code>classification_report</code>?</p>
  
  <p>In many other examples, I see people split the corpus into traing set and test set.
  However, since I am using <code>GridSearchCV</code> with kfold-cross-validation, I don't need to do that.
  So how can I get those values from <code>GridSearchCV</code>?</p>