File size: 3,043 Bytes
7885a28 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
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
|