test_least_angle.py 文件源码

def test_lasso_lars_vs_lasso_cd_ill_conditioned2():
    # Create an ill-conditioned situation in which the LARS has to go
    # far in the path to converge, and check that LARS and coordinate
    # descent give the same answers
    # Note it used to be the case that Lars had to use the drop for good
    # strategy for this but this is no longer the case with the
    # equality_tolerance checks
    X = [[1e20, 1e20, 0],
         [-1e-32, 0, 0],
         [1, 1, 1]]
    y = [10, 10, 1]
    alpha = .0001

    def objective_function(coef):
        return (1. / (2. * len(X)) * linalg.norm(y - np.dot(X, coef)) ** 2
                + alpha * linalg.norm(coef, 1))

    lars = linear_model.LassoLars(alpha=alpha, normalize=False)
    assert_warns(ConvergenceWarning, lars.fit, X, y)
    lars_coef_ = lars.coef_
    lars_obj = objective_function(lars_coef_)

    coord_descent = linear_model.Lasso(alpha=alpha, tol=1e-4, normalize=False)
    cd_coef_ = coord_descent.fit(X, y).coef_
    cd_obj = objective_function(cd_coef_)

    assert_less(lars_obj, cd_obj * (1. + 1e-8))