text: #!/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
  
  X, y = datasets.load_diabetes(return_X_y=True)
  
  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()
  