content: """
  ====================================================================
  Plot the decision surfaces of ensembles of trees on the iris dataset
  ====================================================================
  
  Plot the decision surfaces of forests of randomized trees trained on pairs of
  features of the iris dataset.
  
  This plot compares the decision surfaces learned by a decision tree classifier
  (first column), by a random forest classifier (second column), by an extra-
  trees classifier (third column) and by an AdaBoost classifier (fourth column).
  
  In the first row, the classifiers are built using the sepal width and the sepal
  length features only, on the second row using the petal length and sepal length
  only, and on the third row using the petal width and the petal length only.
  
  In descending order of quality, when trained (outside of this example) on all
  4 features using 30 estimators and scored using 10 fold cross validation, we see::
  
      ExtraTreesClassifier()  # 0.95 score
      RandomForestClassifier()  # 0.94 score
      AdaBoost(DecisionTree(max_depth=3))  # 0.94 score
      DecisionTree(max_depth=None)  # 0.94 score
  
  Increasing `max_depth` for AdaBoost lowers the standard deviation of the scores (but
  the average score does not improve).
  
  See the console's output for further details about each model.
  
  In this example you might try to:
  
  1) vary the ``max_depth`` for the ``DecisionTreeClassifier`` and
     ``AdaBoostClassifier``, perhaps try ``max_depth=3`` for the
     ``DecisionTreeClassifier`` or ``max_depth=None`` for ``AdaBoostClassifier``
  2) vary ``n_estimators``
  
  It is worth noting that RandomForests and ExtraTrees can be fitted in parallel
  on many cores as each tree is built independently of the others. AdaBoost's
  samples are built sequentially and so do not use multiple cores.
  """
  print(__doc__)
  
  import numpy as np
  import matplotlib.pyplot as plt
  from matplotlib.colors import ListedColormap
  
  from sklearn import clone
  from sklearn.datasets import load_iris
  from sklearn.ensemble import (RandomForestClassifier, ExtraTreesClassifier,
                                AdaBoostClassifier)
  from sklearn.externals.six.moves import xrange
  from sklearn.tree import DecisionTreeClassifier
  
  # Parameters
  n_classes = 3
  n_estimators = 30
  cmap = plt.cm.RdYlBu
  plot_step = 0.02  # fine step width for decision surface contours
  plot_step_coarser = 0.5  # step widths for coarse classifier guesses
  RANDOM_SEED = 13  # fix the seed on each iteration
  
  # Load data
  iris = load_iris()
  
  plot_idx = 1
  
  models = [DecisionTreeClassifier(max_depth=None),
            RandomForestClassifier(n_estimators=n_estimators),
            ExtraTreesClassifier(n_estimators=n_estimators),
            AdaBoostClassifier(DecisionTreeClassifier(max_depth=3),
                               n_estimators=n_estimators)]
  
  for pair in ([0, 1], [0, 2], [2, 3]):
      for model in models:
          # We only take the two corresponding features
          X = iris.data[:, pair]
          y = iris.target
  
          # Shuffle
          idx = np.arange(X.shape[0])
          np.random.seed(RANDOM_SEED)
          np.random.shuffle(idx)
          X = X[idx]
          y = y[idx]
  
          # Standardize
          mean = X.mean(axis=0)
          std = X.std(axis=0)
          X = (X - mean) / std
  
          # Train
          clf = clone(model)
          clf = model.fit(X, y)
  
          scores = clf.score(X, y)
          # Create a title for each column and the console by using str() and
          # slicing away useless parts of the string
          model_title = str(type(model)).split(".")[-1][:-2][:-len("Classifier")]
          model_details = model_title
          if hasattr(model, "estimators_"):
              model_details += " with {} estimators".format(len(model.estimators_))
          print( model_details + " with features", pair, "has a score of", scores )
  
          plt.subplot(3, 4, plot_idx)
          if plot_idx <= len(models):
              # Add a title at the top of each column
              plt.title(model_title)
  
          # Now plot the decision boundary using a fine mesh as input to a
          # filled contour plot
          x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
          y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
          xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
                               np.arange(y_min, y_max, plot_step))
  
          # Plot either a single DecisionTreeClassifier or alpha blend the
          # decision surfaces of the ensemble of classifiers
          if isinstance(model, DecisionTreeClassifier):
              Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
              Z = Z.reshape(xx.shape)
              cs = plt.contourf(xx, yy, Z, cmap=cmap)
          else:
              # Choose alpha blend level with respect to the number of estimators
              # that are in use (noting that AdaBoost can use fewer estimators
              # than its maximum if it achieves a good enough fit early on)
              estimator_alpha = 1.0 / len(model.estimators_)
              for tree in model.estimators_:
                  Z = tree.predict(np.c_[xx.ravel(), yy.ravel()])
                  Z = Z.reshape(xx.shape)
                  cs = plt.contourf(xx, yy, Z, alpha=estimator_alpha, cmap=cmap)
  
          # Build a coarser grid to plot a set of ensemble classifications
          # to show how these are different to what we see in the decision
          # surfaces. These points are regularly space and do not have a black outline
          xx_coarser, yy_coarser = np.meshgrid(np.arange(x_min, x_max, plot_step_coarser),
                                               np.arange(y_min, y_max, plot_step_coarser))
          Z_points_coarser = model.predict(np.c_[xx_coarser.ravel(), yy_coarser.ravel()]).reshape(xx_coarser.shape)
          cs_points = plt.scatter(xx_coarser, yy_coarser, s=15, c=Z_points_coarser, cmap=cmap, edgecolors="none")
  
          # Plot the training points, these are clustered together and have a
          # black outline
          plt.scatter(X[:, 0], X[:, 1], c=y,
                      cmap=ListedColormap(['r', 'y', 'b']))
          plot_idx += 1  # move on to the next plot in sequence
  
  plt.suptitle("Classifiers on feature subsets of the Iris dataset")
  plt.axis("tight")
  
  plt.show()
  