def test_convert_beta_coordinates():
# Total: (n draws x p covariates x d features)
# Each draw: (p covariates x d features)
draw1 = np.array([[0.1, 0.2, 0.3, 0.4], [0.3, 0.1, 0.1, 0.5],
[0.2, 0.2, 0.2, 0.3], [0.5, 0.1, 0.2, 0.2]])
draw2 = np.array([[0.2, 0.2, 0.3, 0.3], [0.1, 0.6, 0.2, 0.1],
[0.4, 0.4, 0.1, 0.1], [0.1, 0.1, 0.1, 0.7]])
alr_coords = np.stack([alr(draw1), alr(draw2)]) # 2 x 4 x 3
clr_coords = util.convert_beta_coordinates(alr_coords) # 2 x 4 x 4
exp_coords = np.stack([clr(draw1), clr(draw2)])
np.testing.assert_array_almost_equal(clr_coords, exp_coords)
clr_coords_sums = clr_coords.sum(axis=2)
exp_clr_coords_sums = np.zeros((2, 4))
np.testing.assert_array_almost_equal(exp_clr_coords_sums, clr_coords_sums)
def test_alr_to_clr():
mat = np.array([[0.1, 0.2, 0.3, 0.4, 0.3], [0.3, 0.1, 0.1, 0.2, 0.5],
[0.4, 0.3, 0.5, 0.1, 0.1], [0.2, 0.4, 0.1, 0.3, 0.1]])
# skbio alr & clr take rows as compositions, columns as components
alr_mat = alr(mat.T, 0) # 5 x 3
clr_mat = util.alr_to_clr(alr_mat.T).T # 5 x 4
exp_clr = clr(mat.T) # 5 x 4
np.testing.assert_array_almost_equal(clr_mat, exp_clr)
def test_clr_to_alr():
mat = np.array([[0.1, 0.2, 0.3, 0.4, 0.3], [0.3, 0.1, 0.1, 0.2, 0.5],
[0.4, 0.3, 0.5, 0.1, 0.1], [0.2, 0.4, 0.1, 0.3, 0.1]])
# skbio alr & clr take rows as compositions, columns as components
clr_mat = clr(mat.T)
alr_mat = util.clr_to_alr(clr_mat.T).T
exp_alr = alr(mat.T)
np.testing.assert_array_almost_equal(alr_mat, exp_alr)