""" ======================== Lasso regression example ======================== """ print __doc__ import numpy as np ################################################################################ # generate some sparse data to play with n_samples, n_features = 50, 200 X = np.random.randn(n_samples, n_features) coef = 3*np.random.randn(n_features) coef[10:] = 0 # sparsify coef y = np.dot(X, coef) # add noise y += 0.01*np.random.normal((n_samples,)) # Split data in train set and test set n_samples = X.shape[0] X_train, y_train = X[:n_samples/2], y[:n_samples/2] X_test, y_test = X[n_samples/2:], y[n_samples/2:] ################################################################################ # Lasso from scikits.learn.linear_model import Lasso alpha = 0.1 lasso = Lasso(alpha=alpha) y_pred_lasso = lasso.fit(X_train, y_train).predict(X_test) print lasso print "r^2 on test data : %f" % (1 - np.linalg.norm(y_test - y_pred_lasso)**2 / np.linalg.norm(y_test)**2) ################################################################################ # ElasticNet from scikits.learn.linear_model import ElasticNet enet = ElasticNet(alpha=alpha, rho=0.7) y_pred_enet = enet.fit(X_train, y_train).predict(X_test) print enet print "r^2 on test data : %f" % (1 - np.linalg.norm(y_test - y_pred_enet)**2 / np.linalg.norm(y_test)**2)