86 lines
2.5 KiB
Python
86 lines
2.5 KiB
Python
import numpy as np
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from numpy.testing import assert_array_almost_equal
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import pytest
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from sklearn.manifold import _mds as mds
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from sklearn.utils._testing import ignore_warnings
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def test_smacof():
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# test metric smacof using the data of "Modern Multidimensional Scaling",
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# Borg & Groenen, p 154
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sim = np.array([[0, 5, 3, 4],
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[5, 0, 2, 2],
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[3, 2, 0, 1],
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[4, 2, 1, 0]])
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Z = np.array([[-.266, -.539],
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[.451, .252],
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[.016, -.238],
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[-.200, .524]])
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X, _ = mds.smacof(sim, init=Z, n_components=2, max_iter=1, n_init=1)
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X_true = np.array([[-1.415, -2.471],
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[1.633, 1.107],
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[.249, -.067],
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[-.468, 1.431]])
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assert_array_almost_equal(X, X_true, decimal=3)
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def test_smacof_error():
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# Not symmetric similarity matrix:
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sim = np.array([[0, 5, 9, 4],
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[5, 0, 2, 2],
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[3, 2, 0, 1],
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[4, 2, 1, 0]])
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with pytest.raises(ValueError):
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mds.smacof(sim)
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# Not squared similarity matrix:
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sim = np.array([[0, 5, 9, 4],
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[5, 0, 2, 2],
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[4, 2, 1, 0]])
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with pytest.raises(ValueError):
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mds.smacof(sim)
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# init not None and not correct format:
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sim = np.array([[0, 5, 3, 4],
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[5, 0, 2, 2],
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[3, 2, 0, 1],
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[4, 2, 1, 0]])
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Z = np.array([[-.266, -.539],
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[.016, -.238],
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[-.200, .524]])
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with pytest.raises(ValueError):
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mds.smacof(sim, init=Z, n_init=1)
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def test_MDS():
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sim = np.array([[0, 5, 3, 4],
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[5, 0, 2, 2],
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[3, 2, 0, 1],
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[4, 2, 1, 0]])
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mds_clf = mds.MDS(metric=False, n_jobs=3, dissimilarity="precomputed")
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mds_clf.fit(sim)
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# TODO: Remove in 1.1
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def test_MDS_pairwise_deprecated():
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mds_clf = mds.MDS(metric='precomputed')
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msg = r"Attribute _pairwise was deprecated in version 0\.24"
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with pytest.warns(FutureWarning, match=msg):
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mds_clf._pairwise
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# TODO: Remove in 1.1
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@ignore_warnings(category=FutureWarning)
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@pytest.mark.parametrize("dissimilarity, expected_pairwise", [
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("precomputed", True),
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("euclidean", False),
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])
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def test_MDS_pairwise(dissimilarity, expected_pairwise):
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# _pairwise attribute is set correctly
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mds_clf = mds.MDS(dissimilarity=dissimilarity)
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assert mds_clf._pairwise == expected_pairwise
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