106 lines
3.6 KiB
Python
106 lines
3.6 KiB
Python
from unittest.mock import Mock
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import numpy as np
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from numpy.testing import assert_array_almost_equal, assert_allclose
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import pytest
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from sklearn.manifold import _mds as mds
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from sklearn.metrics import euclidean_distances
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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], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]])
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Z = np.array([[-0.266, -0.539], [0.451, 0.252], [0.016, -0.238], [-0.200, 0.524]])
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X, _ = mds.smacof(
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sim, init=Z, n_components=2, max_iter=1, n_init=1, normalized_stress="auto"
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)
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X_true = np.array(
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[[-1.415, -2.471], [1.633, 1.107], [0.249, -0.067], [-0.468, 1.431]]
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)
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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], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]])
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with pytest.raises(ValueError):
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mds.smacof(sim, normalized_stress="auto")
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# Not squared similarity matrix:
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sim = np.array([[0, 5, 9, 4], [5, 0, 2, 2], [4, 2, 1, 0]])
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with pytest.raises(ValueError):
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mds.smacof(sim, normalized_stress="auto")
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# init not None and not correct format:
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sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]])
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Z = np.array([[-0.266, -0.539], [0.016, -0.238], [-0.200, 0.524]])
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with pytest.raises(ValueError):
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mds.smacof(sim, init=Z, n_init=1, normalized_stress="auto")
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def test_MDS():
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sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]])
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mds_clf = mds.MDS(
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metric=False, n_jobs=3, dissimilarity="precomputed", normalized_stress="auto"
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)
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mds_clf.fit(sim)
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@pytest.mark.parametrize("k", [0.5, 1.5, 2])
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def test_normed_stress(k):
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"""Test that non-metric MDS normalized stress is scale-invariant."""
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sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]])
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X1, stress1 = mds.smacof(
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sim, metric=False, normalized_stress="auto", max_iter=5, random_state=0
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)
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X2, stress2 = mds.smacof(
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k * sim, metric=False, normalized_stress="auto", max_iter=5, random_state=0
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)
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assert_allclose(stress1, stress2, rtol=1e-5)
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assert_allclose(X1, X2, rtol=1e-5)
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def test_normalize_metric_warning():
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"""
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Test that a UserWarning is emitted when using normalized stress with
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metric-MDS.
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"""
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msg = "Normalized stress is not supported"
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sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]])
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with pytest.raises(ValueError, match=msg):
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mds.smacof(sim, metric=True, normalized_stress=True)
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@pytest.mark.parametrize("metric", [True, False])
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def test_normalized_stress_default_change(metric):
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msg = "The default value of `normalized_stress` will change"
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sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]])
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est = mds.MDS(metric=metric)
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with pytest.warns(FutureWarning, match=msg):
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mds.smacof(sim, metric=metric)
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with pytest.warns(FutureWarning, match=msg):
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est.fit(sim)
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@pytest.mark.parametrize("metric", [True, False])
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def test_normalized_stress_auto(metric, monkeypatch):
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rng = np.random.RandomState(0)
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X = rng.randn(4, 3)
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dist = euclidean_distances(X)
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mock = Mock(side_effect=mds._smacof_single)
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monkeypatch.setattr("sklearn.manifold._mds._smacof_single", mock)
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est = mds.MDS(metric=metric, normalized_stress="auto", random_state=rng)
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est.fit_transform(X)
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assert mock.call_args[1]["normalized_stress"] != metric
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mds.smacof(dist, metric=metric, normalized_stress="auto", random_state=rng)
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assert mock.call_args[1]["normalized_stress"] != metric
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