def test_ridge_regression():
rs = np.random.RandomState(0)
alphas = np.array([0.0, 1.0, 10.0])
n_obs, n_covariate, n_trait = 25, 5, 3
X = rs.normal(size=(n_obs, n_covariate))
Y = X @ rs.normal(size=(n_covariate, n_trait)) + rs.normal(size=(n_obs, 1))
XtX, XtY = X.T @ X, X.T @ Y
# Check that results are equal for multiple alphas
res1 = ridge_regression(XtX, XtY, alphas)
res2 = np.concatenate(
[ridge_regression(XtX, XtY, alphas=alphas[[i]]) for i in range(len(alphas))],
axis=0,
)
np.testing.assert_equal(res1, res2)
def test_ridge_regression__raise_on_non_equal_first_dim():
with pytest.raises(
ValueError, match="Array arguments must have same size in first dimension"
):
ridge_regression(np.ones((2, 2)), np.ones((1, 1)), np.array([1.0]))
def test_ridge_regression__raise_on_non_symmetric():
with pytest.raises(ValueError, match="First argument must be symmetric"):
ridge_regression(np.ones((2, 1)), np.ones((2, 1)), np.array([1.0]))
