text: """
  ===============================================================================
  Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset
  ===============================================================================
  
  A comparison for the decision boundaries generated on the iris dataset
  by Label Spreading, Self-training and SVM.
  
  This example demonstrates that Label Spreading and Self-training can learn
  good boundaries even when small amounts of labeled data are available.
  
  Note that Self-training with 100% of the data is omitted as it is functionally
  identical to training the SVC on 100% of the data.
  
  """
  
  # Authors: Clay Woolam   <clay@woolam.org>
  #          Oliver Rausch <rauscho@ethz.ch>
  # License: BSD
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn import datasets
  from sklearn.svm import SVC
  from sklearn.semi_supervised import LabelSpreading
  from sklearn.semi_supervised import SelfTrainingClassifier
  
  
  iris = datasets.load_iris()
  
  X = iris.data[:, :2]
  y = iris.target
  
  # step size in the mesh
  h = 0.02
  
  rng = np.random.RandomState(0)
  y_rand = rng.rand(y.shape[0])
  y_30 = np.copy(y)
  y_30[y_rand < 0.3] = -1  # set random samples to be unlabeled
  y_50 = np.copy(y)
  y_50[y_rand < 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 = (LabelSpreading().fit(X, y_30), y_30, "Label Spreading 30% data")
  ls50 = (LabelSpreading().fit(X, y_50), y_50, "Label Spreading 50% data")
  ls100 = (LabelSpreading().fit(X, y), y, "Label Spreading 100% data")
  
  # the base classifier for self-training is identical to the SVC
  base_classifier = SVC(kernel="rbf", gamma=0.5, probability=True)
  st30 = (
      SelfTrainingClassifier(base_classifier).fit(X, y_30),
      y_30,
      "Self-training 30% data",
  )
  st50 = (
      SelfTrainingClassifier(base_classifier).fit(X, y_50),
      y_50,
      "Self-training 50% data",
  )
  
  rbf_svc = (SVC(kernel="rbf", gamma=0.5).fit(X, y), y, "SVC with rbf kernel")
  
  # 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))
  
  color_map = {-1: (1, 1, 1), 0: (0, 0, 0.9), 1: (1, 0, 0), 2: (0.8, 0.6, 0)}
  
  classifiers = (ls30, st30, ls50, st50, ls100, rbf_svc)
  for i, (clf, y_train, title) in enumerate(classifiers):
      # 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(3, 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, edgecolors="black")
  
      plt.title(title)
  
  plt.suptitle("Unlabeled points are colored white", y=0.1)
  plt.show()
  