old_contents: """
  =================================================================
  Test with permutations the significance of a classification score
  =================================================================
  
  In order to test if a classification score is significative a technique
  in repeating the classification procedure after randomizing, permuting,
  the labels. The p-value is then given by the percentage of runs for
  which the score obtained is greater than the classification score
  obtained in the first place.
  
  """
  
  # Author:  Alexandre Gramfort <alexandre.gramfort@inria.fr>
  # License: BSD
  
  print(__doc__)
  
  import numpy as np
  import pylab as pl
  
  from sklearn.svm import SVC
  from sklearn.cross_validation import StratifiedKFold, permutation_test_score
  from sklearn import datasets
  from sklearn.metrics import accuracy_score
  
  
  ##############################################################################
  # Loading a dataset
  iris = datasets.load_iris()
  X = iris.data
  y = iris.target
  n_classes = np.unique(y).size
  
  # Some noisy data not correlated
  random = np.random.RandomState(seed=0)
  E = random.normal(size=(len(X), 2200))
  
  # Add noisy data to the informative features for make the task harder
  X = np.c_[X, E]
  
  svm = SVC(kernel='linear')
  cv = StratifiedKFold(y, 2)
  
  score, permutation_scores, pvalue = permutation_test_score(
      svm, X, y, accuracy_score, cv=cv, n_permutations=100, n_jobs=1)
  
  print("Classification score %s (pvalue : %s)" % (score, pvalue))
  
  ###############################################################################
  # View histogram of permutation scores
  pl.hist(permutation_scores, 20, label='Permutation scores')
  ylim = pl.ylim()
  # BUG: vlines(..., linestyle='--') fails on older versions of matplotlib
  #pl.vlines(score, ylim[0], ylim[1], linestyle='--',
  #          color='g', linewidth=3, label='Classification Score'
  #          ' (pvalue %s)' % pvalue)
  #pl.vlines(1.0 / n_classes, ylim[0], ylim[1], linestyle='--',
  #          color='k', linewidth=3, label='Luck')
  pl.plot(2 * [score], ylim, '--g', linewidth=3,
          label='Classification Score'
          ' (pvalue %s)' % pvalue)
  pl.plot(2 * [1. / n_classes], ylim, '--k', linewidth=3, label='Luck')
  
  pl.ylim(ylim)
  pl.legend()
  pl.xlabel('Score')
  pl.show()
  
new_contents: """
  =================================================================
  Test with permutations the significance of a classification score
  =================================================================
  
  In order to test if a classification score is significative a technique
  in repeating the classification procedure after randomizing, permuting,
  the labels. The p-value is then given by the percentage of runs for
  which the score obtained is greater than the classification score
  obtained in the first place.
  
  """
  
  # Author:  Alexandre Gramfort <alexandre.gramfort@inria.fr>
  # License: BSD
  
  print(__doc__)
  
  import numpy as np
  import pylab as pl
  
  from sklearn.svm import SVC
  from sklearn.cross_validation import StratifiedKFold, permutation_test_score
  from sklearn import datasets
  
  
  ##############################################################################
  # Loading a dataset
  iris = datasets.load_iris()
  X = iris.data
  y = iris.target
  n_classes = np.unique(y).size
  
  # Some noisy data not correlated
  random = np.random.RandomState(seed=0)
  E = random.normal(size=(len(X), 2200))
  
  # Add noisy data to the informative features for make the task harder
  X = np.c_[X, E]
  
  svm = SVC(kernel='linear')
  cv = StratifiedKFold(y, 2)
  
  score, permutation_scores, pvalue = permutation_test_score(
      svm, X, y, scoring="accuracy", cv=cv, n_permutations=100, n_jobs=1)
  
  print("Classification score %s (pvalue : %s)" % (score, pvalue))
  
  ###############################################################################
  # View histogram of permutation scores
  pl.hist(permutation_scores, 20, label='Permutation scores')
  ylim = pl.ylim()
  # BUG: vlines(..., linestyle='--') fails on older versions of matplotlib
  #pl.vlines(score, ylim[0], ylim[1], linestyle='--',
  #          color='g', linewidth=3, label='Classification Score'
  #          ' (pvalue %s)' % pvalue)
  #pl.vlines(1.0 / n_classes, ylim[0], ylim[1], linestyle='--',
  #          color='k', linewidth=3, label='Luck')
  pl.plot(2 * [score], ylim, '--g', linewidth=3,
          label='Classification Score'
          ' (pvalue %s)' % pvalue)
  pl.plot(2 * [1. / n_classes], ylim, '--k', linewidth=3, label='Luck')
  
  pl.ylim(ylim)
  pl.legend()
  pl.xlabel('Score')
  pl.show()
  
subject: FIX example