content: """
  ===================================================
  Faces recognition example using eigenfaces and SVMs
  ===================================================
  
  The dataset used in this example is a preprocessed excerpt of the
  "Labeled Faces in the Wild", aka LFW_:
  
    http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)
  
  .. _LFW: http://vis-www.cs.umass.edu/lfw/
  
  Expected results for the top 5 most represented people in the dataset::
  
                     precision    recall  f1-score   support
  
       Ariel Sharon       0.67      0.92      0.77        13
       Colin Powell       0.75      0.78      0.76        60
    Donald Rumsfeld       0.78      0.67      0.72        27
      George W Bush       0.86      0.86      0.86       146
  Gerhard Schroeder       0.76      0.76      0.76        25
        Hugo Chavez       0.67      0.67      0.67        15
         Tony Blair       0.81      0.69      0.75        36
  
        avg / total       0.80      0.80      0.80       322
  
  """
  from __future__ import print_function
  
  from time import time
  import logging
  import matplotlib.pyplot as plt
  
  from sklearn.cross_validation import train_test_split
  from sklearn.datasets import fetch_lfw_people
  from sklearn.grid_search import GridSearchCV
  from sklearn.metrics import classification_report
  from sklearn.metrics import confusion_matrix
  from sklearn.decomposition import RandomizedPCA
  from sklearn.svm import SVC
  
  
  print(__doc__)
  
  # Display progress logs on stdout
  logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
  
  
  ###############################################################################
  # Download the data, if not already on disk and load it as numpy arrays
  
  lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
  
  # introspect the images arrays to find the shapes (for plotting)
  n_samples, h, w = lfw_people.images.shape
  
  # for machine learning we use the 2 data directly (as relative pixel
  # positions info is ignored by this model)
  X = lfw_people.data
  n_features = X.shape[1]
  
  # the label to predict is the id of the person
  y = lfw_people.target
  target_names = lfw_people.target_names
  n_classes = target_names.shape[0]
  
  print("Total dataset size:")
  print("n_samples: %d" % n_samples)
  print("n_features: %d" % n_features)
  print("n_classes: %d" % n_classes)
  
  
  ###############################################################################
  # Split into a training set and a test set using a stratified k fold
  
  # split into a training and testing set
  X_train, X_test, y_train, y_test = train_test_split(
      X, y, test_size=0.25, random_state=42)
  
  
  ###############################################################################
  # Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
  # dataset): unsupervised feature extraction / dimensionality reduction
  n_components = 150
  
  print("Extracting the top %d eigenfaces from %d faces"
        % (n_components, X_train.shape[0]))
  t0 = time()
  pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
  print("done in %0.3fs" % (time() - t0))
  
  eigenfaces = pca.components_.reshape((n_components, h, w))
  
  print("Projecting the input data on the eigenfaces orthonormal basis")
  t0 = time()
  X_train_pca = pca.transform(X_train)
  X_test_pca = pca.transform(X_test)
  print("done in %0.3fs" % (time() - t0))
  
  
  ###############################################################################
  # Train a SVM classification model
  
  print("Fitting the classifier to the training set")
  t0 = time()
  param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
                'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
  clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
  clf = clf.fit(X_train_pca, y_train)
  print("done in %0.3fs" % (time() - t0))
  print("Best estimator found by grid search:")
  print(clf.best_estimator_)
  
  
  ###############################################################################
  # Quantitative evaluation of the model quality on the test set
  
  print("Predicting people's names on the test set")
  t0 = time()
  y_pred = clf.predict(X_test_pca)
  print("done in %0.3fs" % (time() - t0))
  
  print(classification_report(y_test, y_pred, target_names=target_names))
  print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))
  
  
  ###############################################################################
  # Qualitative evaluation of the predictions using matplotlib
  
  def plot_gallery(images, titles, h, w, n_row=3, n_col=4):
      """Helper function to plot a gallery of portraits"""
      plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))
      plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)
      for i in range(n_row * n_col):
          plt.subplot(n_row, n_col, i + 1)
          plt.imshow(images[i].reshape((h, w)), cmap=plt.cm.gray)
          plt.title(titles[i], size=12)
          plt.xticks(())
          plt.yticks(())
  
  
  # plot the result of the prediction on a portion of the test set
  
  def title(y_pred, y_test, target_names, i):
      pred_name = target_names[y_pred[i]].rsplit(' ', 1)[-1]
      true_name = target_names[y_test[i]].rsplit(' ', 1)[-1]
      return 'predicted: %s\ntrue:      %s' % (pred_name, true_name)
  
  prediction_titles = [title(y_pred, y_test, target_names, i)
                       for i in range(y_pred.shape[0])]
  
  plot_gallery(X_test, prediction_titles, h, w)
  
  # plot the gallery of the most significative eigenfaces
  
  eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
  plot_gallery(eigenfaces, eigenface_titles, h, w)
  
  plt.show()
  