content: """
  ==============================
  Lasso on dense and sparse data
  ==============================
  
  We show that linear_model.Lasso provides the same results for dense and sparse
  data and that in the case of sparse data the speed is improved.
  
  """
  print(__doc__)
  
  from time import time
  from scipy import sparse
  from scipy import linalg
  
  from sklearn.datasets.samples_generator import make_regression
  from sklearn.linear_model import Lasso
  
  
  ###############################################################################
  # The two Lasso implementations on Dense data
  print("--- Dense matrices")
  
  X, y = make_regression(n_samples=200, n_features=5000, random_state=0)
  X_sp = sparse.coo_matrix(X)
  
  alpha = 1
  sparse_lasso = Lasso(alpha=alpha, fit_intercept=False, max_iter=1000)
  dense_lasso = Lasso(alpha=alpha, fit_intercept=False, max_iter=1000)
  
  t0 = time()
  sparse_lasso.fit(X_sp, y)
  print("Sparse Lasso done in %fs" % (time() - t0))
  
  t0 = time()
  dense_lasso.fit(X, y)
  print("Dense Lasso done in %fs" % (time() - t0))
  
  print("Distance between coefficients : %s"
        % linalg.norm(sparse_lasso.coef_ - dense_lasso.coef_))
  
  ###############################################################################
  # The two Lasso implementations on Sparse data
  print("--- Sparse matrices")
  
  Xs = X.copy()
  Xs[Xs < 2.5] = 0.0
  Xs = sparse.coo_matrix(Xs)
  Xs = Xs.tocsc()
  
  print("Matrix density : %s %%" % (Xs.nnz / float(X.size) * 100))
  
  alpha = 0.1
  sparse_lasso = Lasso(alpha=alpha, fit_intercept=False, max_iter=10000)
  dense_lasso = Lasso(alpha=alpha, fit_intercept=False, max_iter=10000)
  
  t0 = time()
  sparse_lasso.fit(Xs, y)
  print("Sparse Lasso done in %fs" % (time() - t0))
  
  t0 = time()
  dense_lasso.fit(Xs.toarray(), y)
  print("Dense Lasso done in %fs" % (time() - t0))
  
  print("Distance between coefficients : %s"
        % linalg.norm(sparse_lasso.coef_ - dense_lasso.coef_))
  