from unittest.mock import Mock import numpy as np import pytest from numpy.testing import assert_allclose, assert_array_almost_equal from sklearn.manifold import _mds as mds from sklearn.metrics import euclidean_distances 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([[-0.266, -0.539], [0.451, 0.252], [0.016, -0.238], [-0.200, 0.524]]) 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], [0.249, -0.067], [-0.468, 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([[-0.266, -0.539], [0.016, -0.238], [-0.200, 0.524]]) with pytest.raises(ValueError): mds.smacof(sim, init=Z, n_init=1) def test_MDS(): sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]]) mds_clf = mds.MDS(metric=False, n_jobs=3, dissimilarity="precomputed") mds_clf.fit(sim) @pytest.mark.parametrize("k", [0.5, 1.5, 2]) 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, max_iter=5, random_state=0) X2, stress2 = mds.smacof(k * sim, metric=False, max_iter=5, random_state=0) assert_allclose(stress1, stress2, rtol=1e-5) assert_allclose(X1, X2, rtol=1e-5) 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) @pytest.mark.parametrize("metric", [True, False]) def test_normalized_stress_auto(metric, monkeypatch): rng = np.random.RandomState(0) X = rng.randn(4, 3) dist = euclidean_distances(X) mock = Mock(side_effect=mds._smacof_single) monkeypatch.setattr("sklearn.manifold._mds._smacof_single", mock) est = mds.MDS(metric=metric, normalized_stress="auto", random_state=rng) est.fit_transform(X) assert mock.call_args[1]["normalized_stress"] != metric mds.smacof(dist, metric=metric, normalized_stress="auto", random_state=rng) assert mock.call_args[1]["normalized_stress"] != metric