title: How to analyse the intermediate steps of sklearn-pipeline?
body: <p>I am using the sklearn to classify the text into categories. I am using CountVectorizer and TFIDFTransformer to create the sparse matrix.</p>
  
  <p>I am performing couple of pre-processing steps on string in the custom<code>tokenize_and_stem</code> function used in CountVectorizer tokenizer.</p>
  
  <pre><code>from sklearn.pipeline import Pipeline
  from sklearn.svm import LinearSVC
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.feature_extraction.text import TfidfTransformer
  
  SVM = Pipeline([('vect', CountVectorizer(max_features=100000,\
                                           ngram_range= (1, 2),stop_words='english',tokenizer=tokenize_and_stem)),\
                           ('tfidf', TfidfTransformer(use_idf= True)),\
                           ('clf-svm', LinearSVC(C=1)),])
  </code></pre>
  
  <p>my question here is, if there is any easy way available to view/store the output of step 1/2 of Pipeline to analyse what kind of array is going into svm ?</p>