text: """
  ===================================================
  Feature selection using SelectFromModel and LassoCV
  ===================================================
  
  Use SelectFromModel meta-transformer along with Lasso to select the best
  couple of features from the Boston dataset.
  """
  # Author: Manoj Kumar <mks542@nyu.edu>
  # License: BSD 3 clause
  
  print(__doc__)
  
  import matplotlib.pyplot as plt
  import numpy as np
  
  from sklearn.datasets import load_boston
  from sklearn.feature_selection import SelectFromModel
  from sklearn.linear_model import LassoCV
  
  # Load the boston dataset.
  boston = load_boston()
  X, y = boston['data'], boston['target']
  
  # We use the base estimator LassoCV since the L1 norm promotes sparsity of features.
  clf = LassoCV(cv=5)
  
  # Set a minimum threshold of 0.25
  sfm = SelectFromModel(clf, threshold=0.25)
  sfm.fit(X, y)
  n_features = sfm.transform(X).shape[1]
  
  # Reset the threshold till the number of features equals two.
  # Note that the attribute can be set directly instead of repeatedly
  # fitting the metatransformer.
  while n_features > 2:
      sfm.threshold += 0.1
      X_transform = sfm.transform(X)
      n_features = X_transform.shape[1]
  
  # Plot the selected two features from X.
  plt.title(
      "Features selected from Boston using SelectFromModel with "
      "threshold %0.3f." % sfm.threshold)
  feature1 = X_transform[:, 0]
  feature2 = X_transform[:, 1] 
  plt.plot(feature1, feature2, 'r.')
  plt.xlabel("Feature number 1")
  plt.ylabel("Feature number 2")
  plt.ylim([np.min(feature2), np.max(feature2)])
  plt.show()
  