content: """
  =====================================================================
  Decision boundary of label propagation versus SVM on the Iris dataset
  =====================================================================
  
  Comparison for decision boundary generated on iris dataset
  between Label Propagation and SVM.
  
  This demonstrates Label Propagation learning a good boundary
  even with a small amount of labeled data.
  
  """
  print(__doc__)
  
  # Authors: Clay Woolam <clay@woolam.org>
  # License: BSD
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn import datasets
  from sklearn import svm
  from sklearn.semi_supervised import label_propagation
  
  rng = np.random.RandomState(0)
  
  iris = datasets.load_iris()
  
  X = iris.data[:, :2]
  y = iris.target
  
  # step size in the mesh
  h = .02
  
  y_30 = np.copy(y)
  y_30[rng.rand(len(y)) < 0.3] = -1
  y_50 = np.copy(y)
  y_50[rng.rand(len(y)) < 0.5] = -1
  # we create an instance of SVM and fit out data. We do not scale our
  # data since we want to plot the support vectors
  ls30 = (label_propagation.LabelSpreading().fit(X, y_30),
          y_30)
  ls50 = (label_propagation.LabelSpreading().fit(X, y_50),
          y_50)
  ls100 = (label_propagation.LabelSpreading().fit(X, y), y)
  rbf_svc = (svm.SVC(kernel='rbf').fit(X, y), y)
  
  # create a mesh to plot in
  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, h),
                       np.arange(y_min, y_max, h))
  
  # title for the plots
  titles = ['Label Spreading 30% data',
            'Label Spreading 50% data',
            'Label Spreading 100% data',
            'SVC with rbf kernel']
  
  color_map = {-1: (1, 1, 1), 0: (0, 0, .9), 1: (1, 0, 0), 2: (.8, .6, 0)}
  
  for i, (clf, y_train) in enumerate((ls30, ls50, ls100, rbf_svc)):
      # Plot the decision boundary. For that, we will assign a color to each
      # point in the mesh [x_min, x_max]x[y_min, y_max].
      plt.subplot(2, 2, i + 1)
      Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
  
      # Put the result into a color plot
      Z = Z.reshape(xx.shape)
      plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
      plt.axis('off')
  
      # Plot also the training points
      colors = [color_map[y] for y in y_train]
      plt.scatter(X[:, 0], X[:, 1], c=colors, cmap=plt.cm.Paired)
  
      plt.title(titles[i])
  
  plt.text(.90, 0, "Unlabeled points are colored white")
  plt.show()
  