title: Scikit-learn get propability of a sample belonging to a category
body: <p>Hello i'm trying to classify text into 4 categories an i'd like to print as well as the prediction, the probability of text to  belong to each category.<br>
  After reading documentation of Scikit-learn i think i should use <code>predict_proba</code>,
  my code so far is this :</p>
  
  <pre><code># -*- coding: utf-8 -*-
  #!/usr/bin/env python
  import sys
  import os
  import numpy as np
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.pipeline import Pipeline
  from sklearn.metrics import confusion_matrix, f1_score
  from sklearn.datasets import load_files
  from sklearn.svm import SVC
  from sklearn.feature_extraction.text import TfidfTransformer
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.metrics import accuracy_score
  from sklearn.metrics import classification_report
  
  string = sys.argv[1] #i will pass text to predict from console
  sets = load_files('scikit') #load training set
  
  
  
  
  count_vect = CountVectorizer(analyzer='char_wb', ngram_range=(0, 3), min_df=1) 
  X_train_counts = count_vect.fit_transform(sets.data)     
  
  
  tf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts)
  X_train_tf = tf_transformer.transform(X_train_counts)
  
  
  tfidf_transformer = TfidfTransformer()
  X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
  
  
  
  clf = MultinomialNB().fit(X_train_tfidf, sets.target)
  docs_new = [string]
  X_new_counts = count_vect.transform(docs_new)
  X_new_tfidf = tfidf_transformer.transform(X_new_counts)
  predicted = clf.predict(X_new_tfidf)
  for doc, category in zip(docs_new, predicted):
       print('%r =&gt; %s' % (doc, sets.target_names[category])) #print prediction , and it is correct
       print(clf.predict_proba(sets.target_names)) #trying to get prob for al classes
  </code></pre>
  
  <p>sadly the output is this : <code>ValueError: objects are not aligned</code> , i've tried lot of different ways to achieve this and search a lot on the web but none seems working.
  Any advice would be kindly appreciated. Thanks 
  Nico.</p>