title: Informative Features Code not Working
body: <p>I want to implement a most informative features function for binary NB in SciKit Learn. I am using Python3. </p>
  
  <p>First off, I understand that the question of implementing some sort of 'informative features' function for SciKit's multinomial NB has been asked. However, I have tried the responses and have had no luck - so I think either SciKit updated, or I am doing something very wrong. I am using 
  tobigue's <a href="https://stackoverflow.com/questions/11116697/how-to-get-most-informative-features-for-scikit-learn-classifiers">answer here</a> for a function. </p>
  
  <pre><code>from nltk.corpus import stopwords
  import numpy as np
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.pipeline import Pipeline
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.feature_extraction.text import TfidfTransformer
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.model_selection import GridSearchCV
  from sklearn.model_selection import train_test_split
  
  
  
  #Array contains a list of (headline, source) tupples where there are two sources. 
  #I want to classify each headline as belonging to a given source. 
  array = [('toyota showcases humanoid that mirrors user', 'drudge'), ('virginia again delays vote certification after error in ballot distribution', 'npr'), ("do doctors need to use computers? one physician's case highlights the quandary", 'npr'), ('office sex summons', 'drudge'), ('launch calibrated to avoid military response?', 'drudge'), ('snl skewers alum al franken, trump sons', 'npr'), ('mulvaney shows up for work at consumer watchdog group, as leadership feud deepens', 'npr'), ('indonesia tries to evacuate 100,000 people away from erupting volcano on bali', 'npr'), ('downing street blasts', 'drudge'), ('stocks soar more; records smashed', 'drudge'), ('aid begins to filter back into yemen, as saudi-led blockade eases', 'npr'), ('just look at these fancy port-a-potties', 'npr'), ('nyt turns to twitter activism to thwart', 'drudge'), ('uncertainty reigns in battle for virginia house of delegates', 'npr'), ('u.s. reverses its decision to close palestinian office in d.c.', 'npr'), ("'i don't believe in science,' says flat-earther set to launch himself in own rocket", 'npr'), ("bosnian war chief 'dies' after being filmed 'drinking poison' at the hague", 'drudge'), ('federal judge blocks new texas anti-abortion law', 'npr'), ('gm unveils driverless cars, aiming to lead pack', 'drudge'), ('in japan, a growing scandal over companies faking product-quality data', 'npr')]
  
  
  #I want to classify each headline as belonging to a given source. 
  def scikit_naivebayes(data_array):
      headlines = [element[0] for element in data_array]
      sources = [element[1] for element in data_array]
      text_clf = Pipeline([('vect', CountVectorizer(stop_words='english')), ('tfidf', TfidfTransformer()),('clf', MultinomialNB())])
      cf1 = text_clf.fit(headlines, sources)
      train(cf1,headlines,sources)
  
      #Call most_informative_features function on CountVectorizer and classifier
      show_most_informative_features(CountVectorizer, cf1)
  
  
  def train(classifier, X, y):
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=33)
      classifier.fit(X_train, y_train)
      print ("Accuracy: {}".format(classifier.score(X_test, y_test)))
  
  
  #tobigue's code: 
  def show_most_informative_features(vectorizer, clf, n=20):
      feature_names = vectorizer.get_feature_names()
      coefs_with_fns = sorted(zip(clf.coef_[0], feature_names))
      top = zip(coefs_with_fns[:n], coefs_with_fns[:-(n + 1):-1])
      for (coef_1, fn_1), (coef_2, fn_2) in top:
      print ("\t%.4f\t%-15s\t\t%.4f\t%-15s" % (coef_1, fn_1, coef_2, fn_2))
  
  
  def main():
      scikit_naivebayes(array)
  
  
  main()
  
  #ERROR: 
  # File "file_path_here", line 34, in program_name
  # feature_names = vectorizer.get_feature_names()
  # TypeError: get_feature_names() missing 1 required positional argument: 'self'
  </code></pre>