some new features

.venv/lib/python3.12/site-packages/sklearn/manifold/tests/test_mds.py

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+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