.. _sphx_glr_auto_examples_linear_model_lasso_dense_vs_sparse_data.py: ============================== 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. .. code-block:: python 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 .. code-block:: python 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 .. code-block:: python 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_)) **Total running time of the script:** (0 minutes 0.000 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`lasso_dense_vs_sparse_data.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`lasso_dense_vs_sparse_data.ipynb `