content: """
  ==========================================
  IsolationForest example
  ==========================================
  
  An example using IsolationForest for anomaly detection.
  
  The IsolationForest 'isolates' observations by randomly selecting a feature
  and then randomly selecting a split value between the maximum and minimum
  values of the selected feature.
  
  Since recursive partitioning can be represented by a tree structure, the
  number of splittings required to isolate a sample is equivalent to the path
  length from the root node to the terminating node.
  
  This path length, averaged over a forest of such random trees, is a measure
  of normality and our decision function.
  
  Random partitioning produces noticeable shorter paths for anomalies.
  Hence, when a forest of random trees collectively produce shorter path lengths
  for particular samples, they are highly likely to be anomalies.
  
  .. [1] Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation forest."
      Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on.
  
  """
  print(__doc__)
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn.ensemble import IsolationForest
  
  rng = np.random.RandomState(42)
  
  # Generate train data
  X = 0.3 * rng.randn(100, 2)
  X_train = np.r_[X + 2, X - 2]
  # Generate some regular novel observations
  X = 0.3 * rng.randn(20, 2)
  X_test = np.r_[X + 2, X - 2]
  # Generate some abnormal novel observations
  X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))
  
  # fit the model
  clf = IsolationForest(max_samples=100, random_state=rng)
  clf.fit(X_train)
  y_pred_train = clf.predict(X_train)
  y_pred_test = clf.predict(X_test)
  y_pred_outliers = clf.predict(X_outliers)
  
  # plot the line, the samples, and the nearest vectors to the plane
  xx, yy = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50))
  Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
  Z = Z.reshape(xx.shape)
  
  plt.title("IsolationForest")
  plt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r)
  
  b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white')
  b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='green')
  c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red')
  plt.axis('tight')
  plt.xlim((-5, 5))
  plt.ylim((-5, 5))
  plt.legend([b1, b2, c],
             ["training observations",
              "new regular observations", "new abnormal observations"],
             loc="upper left")
  plt.show()
  