def test_normalize_metric_warning():
"""
Test that a UserWarning is emitted when using normalized stress with
metric-MDS.
"""
msg = "Normalized stress is not supported"
sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]])
with pytest.raises(ValueError, match=msg):
mds.smacof(sim, metric=True, normalized_stress=True)
def test_normed_stress(k):
"""Test that non-metric MDS normalized stress is scale-invariant."""
sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]])
X1, stress1 = mds.smacof(sim,
metric=False,
normalized_stress=True,
max_iter=5,
random_state=0)
X2, stress2 = mds.smacof(k * sim,
metric=False,
normalized_stress=True,
max_iter=5,
random_state=0)
assert_allclose(stress1, stress2, rtol=1e-5)
assert_allclose(X1, X2, rtol=1e-5)
def test_smacof():
# test metric smacof using the data of "Modern Multidimensional Scaling",
# Borg & Groenen, p 154
sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]])
Z = np.array([[-.266, -.539], [.451, .252], [.016, -.238], [-.200, .524]])
X, _ = mds.smacof(sim, init=Z, n_components=2, max_iter=1, n_init=1)
X_true = np.array([[-1.415, -2.471], [1.633, 1.107], [.249, -.067],
[-.468, 1.431]])
assert_array_almost_equal(X, X_true, decimal=3)
def test_smacof_error():
# Not symmetric similarity matrix:
sim = np.array([[0, 5, 9, 4],
[5, 0, 2, 2],
[3, 2, 0, 1],
[4, 2, 1, 0]])
with pytest.raises(ValueError):
mds.smacof(sim)
# Not squared similarity matrix:
sim = np.array([[0, 5, 9, 4],
[5, 0, 2, 2],
[4, 2, 1, 0]])
with pytest.raises(ValueError):
mds.smacof(sim)
# init not None and not correct format:
sim = np.array([[0, 5, 3, 4],
[5, 0, 2, 2],
[3, 2, 0, 1],
[4, 2, 1, 0]])
Z = np.array([[-.266, -.539],
[.016, -.238],
[-.200, .524]])
with pytest.raises(ValueError):
mds.smacof(sim, init=Z, n_init=1)