title: My PipeLine Configuration for Text Classification using sklearn in python
body: <p>I have done the following pipeline:</p>
  
  <p>max_features=None, min_df=2,ngram_range=(1, 3)</p>
  
  <p><strong>1-</strong> <strong>How can I print the output of this pipeline? I mean (the 1-3 grams)
  and if I want to generate my bigram by my self what is the best solution ?</strong></p>
  
  <p><strong>2-If I want to add a constraint like min-TF >1 ?</strong></p>
  
  <p>here is my configuration:</p>
  
  <pre><code>from sklearn.naive_bayes import MultinomialNB,BernoulliNB,GaussianNB
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.svm import SVC
  from sklearn.feature_extraction.text import TfidfTransformer
  from sklearn.pipeline import Pipeline
  
  pipeline = Pipeline([   
      ('count_vectorizer',   TfidfVectorizer(max_features=None, min_df=2,ngram_range=(1, 3),token_pattern=r'\s',analyzer = 'word' ,lowercase=False, stop_words=StopWordsList)),
      ('tfidf_transformer',  TfidfTransformer(norm='l2', smooth_idf=False, sublinear_tf=False, use_idf=True)),    ('classifier', MultinomialNB()  )# SVC(kernel='rbf', probability=True) )
  ])
  </code></pre>