lasso_cv = LassoCV(fit_intercept=True, n_alphas=100, normalize=False)
lasso_cv.fit(x_train, y_train)
p_lasso_cv = lasso_cv.predict(x_test)
print("#--------------------------------------")
print("LassoCV Regression Result")
print("r2_score = %.4f" % (r2_score(y_test, p_lasso_cv)))
print("mean squared error = %.4f" % (mean_squared_error(y_test, p_lasso_cv)))
print(sum(lasso_cv.coef_ == 0))
print("#--------------------------------------")
print("\n")
# Let's visualize the solution path
lassop = lasso_cv.path(x_train, y_train)
lassop_fig = plt.figure()
lassop_plot = lassop_fig.add_subplot(1, 1, 1)
lassop_plot.plot(np.log(lassop[0]), lassop[1].T)
lassop_plot.set_xlabel("lambda value (log scale)")
lassop_plot.set_ylabel("Coefficient estimate value")
lassop_plot.set_title("lasso solution path")
plt.savefig("lasso_path.jpg", dpi=300)
plt.show()
# ElasticNetCV: contained L1 and L2 regularization
# CV == cross-validation
en_cv = ElasticNetCV(fit_intercept=True,
n_alphas=100,
normalize=False,
