content: """
  =================================================
  Pixel importances with a parallel forest of trees
  =================================================
  
  This example shows the use of forests of trees to evaluate the importance
  of the pixels in an image classification task (faces). The hotter the pixel,
  the more important.
  
  The code below also illustrates how the construction and the computation
  of the predictions can be parallelized within multiple jobs.
  """
  print(__doc__)
  
  from time import time
  import matplotlib.pyplot as plt
  
  from sklearn.datasets import fetch_olivetti_faces
  from sklearn.ensemble import ExtraTreesClassifier
  
  # Number of cores to use to perform parallel fitting of the forest model
  n_jobs = 1
  
  # Load the faces dataset
  data = fetch_olivetti_faces()
  X = data.images.reshape((len(data.images), -1))
  y = data.target
  
  mask = y < 5  # Limit to 5 classes
  X = X[mask]
  y = y[mask]
  
  # Build a forest and compute the pixel importances
  print("Fitting ExtraTreesClassifier on faces data with %d cores..." % n_jobs)
  t0 = time()
  forest = ExtraTreesClassifier(n_estimators=1000,
                                max_features=128,
                                n_jobs=n_jobs,
                                random_state=0)
  
  forest.fit(X, y)
  print("done in %0.3fs" % (time() - t0))
  importances = forest.feature_importances_
  importances = importances.reshape(data.images[0].shape)
  
  # Plot pixel importances
  plt.matshow(importances, cmap=plt.cm.hot)
  plt.title("Pixel importances with forests of trees")
  plt.show()
  