old_contents: #!/usr/bin/env python
  """
  =====================
  Lasso path using LARS
  =====================
  
  Computes Lasso Path along the regularization parameter using the LARS
  algorithm on the diabetes dataset. Each color represents a different
  feature of the coefficient vector, and this is displayed as a function
  of the regularization parameter.
  
  """
  print(__doc__)
  
  # Author: Fabian Pedregosa <fabian.pedregosa@inria.fr>
  #         Alexandre Gramfort <alexandre.gramfort@inria.fr>
  # License: BSD 3 clause
  
  import numpy as np
  import matplotlib.pyplot as plt
  
  from sklearn import linear_model
  from sklearn import datasets
  
  diabetes = datasets.load_diabetes()
  X = diabetes.data
  y = diabetes.target
  
  print("Computing regularization path using the LARS ...")
  alphas, _, coefs = linear_model.lars_path(X, y, method='lasso', verbose=True)
  
  xx = np.sum(np.abs(coefs.T), axis=1)
  xx /= xx[-1]
  
  plt.plot(xx, coefs.T)
  ymin, ymax = plt.ylim()
  plt.vlines(xx, ymin, ymax, linestyle='dashed')
  plt.xlabel('|coef| / max|coef|')
  plt.ylabel('Coefficients')
  plt.title('LASSO Path')
  plt.axis('tight')
  plt.show()
  
new_contents: #!/usr/bin/env python
  """
  =====================
  Lasso path using LARS
  =====================
  
  Computes Lasso Path along the regularization parameter using the LARS
  algorithm on the diabetes dataset. Each color represents a different
  feature of the coefficient vector, and this is displayed as a function
  of the regularization parameter.
  
  """
  print(__doc__)
  
  # Author: Fabian Pedregosa <fabian.pedregosa@inria.fr>
  #         Alexandre Gramfort <alexandre.gramfort@inria.fr>
  # License: BSD 3 clause
  
  import numpy as np
  import matplotlib.pyplot as plt
  
  from sklearn import linear_model
  from sklearn import datasets
  
  diabetes = datasets.load_diabetes()
  X = diabetes.data
  y = diabetes.target
  
  print("Computing regularization path using the LARS ...")
  _, _, coefs = linear_model.lars_path(X, y, method='lasso', verbose=True)
  
  xx = np.sum(np.abs(coefs.T), axis=1)
  xx /= xx[-1]
  
  plt.plot(xx, coefs.T)
  ymin, ymax = plt.ylim()
  plt.vlines(xx, ymin, ymax, linestyle='dashed')
  plt.xlabel('|coef| / max|coef|')
  plt.ylabel('Coefficients')
  plt.title('LASSO Path')
  plt.axis('tight')
  plt.show()
  
subject: Remove unused variable alphas from the LARS example. (#9882)