cv_model = ElasticNetCV(l1_ratio=[.1, .5, .7, .9, .95, .99, 1], eps=1e-3, n_alphas=100, fit_intercept=True,
                        normalize=True, precompute='auto', max_iter=2000, tol=0.0001, cv=6,
                        copy_X=True, verbose=0, n_jobs=-1, positive=False, random_state=0)

cv_model.fit(X_train, y_train)
print('Optimal alpha: %.8f'%cv_model.alpha_)
print('Optimal l1_ratio: %.3f'%cv_model.l1_ratio_)
print('Number of iterations %d'%cv_model.n_iter_)