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
|
from umap.spectral import spectral_layout, tswspectral_layout
import numpy as np
import pytest
from scipy.version import full_version as scipy_full_version_
from warnings import catch_warnings
scipy_full_version = tuple(int(n) for n in scipy_full_version_.split("."))
@pytest.mark.skipif(
scipy_full_version < (1, 10),
reason="SciPy installing with Python 3.7 does not converge under same circumstances"
)
def test_tsw_spectral_init(iris):
# create an arbitrary (dense) random affinity matrix
seed = 42
rng = np.random.default_rng(seed=seed)
# matrix must be of sufficient size of lobpcg will refuse to work on it
n = 20
graph = rng.standard_normal(n * n).reshape((n, n)) ** 2
graph = graph.T * graph
spec = spectral_layout(None, graph, 2, random_state=seed ** 2)
tsw_spec = tswspectral_layout(None, graph, 2, random_state=seed ** 2, tol=1e-8)
# Make sure the two methods produce similar embeddings.
rmsd = np.mean(np.sum((spec - tsw_spec) ** 2, axis=1))
assert (
rmsd < 1e-6
), "tsvd-warmed spectral init insufficiently close to standard spectral init"
@pytest.mark.skipif(
scipy_full_version < (1, 10),
reason="SciPy installing with Py 3.7 does not warn reliably on convergence failure"
)
def test_ensure_fallback_to_random_on_spectral_failure():
dim = 1000
k = 10
assert k >= 10
assert dim // 10 > k
y = np.eye(dim, k=1)
u = np.random.random((dim, dim // 10))
graph = y + y.T + u @ u.T
with pytest.warns(
UserWarning,
match="Spectral initialisation failed!"
):
tswspectral_layout(u, graph, k, random_state=42, maxiter=2, method="lobpcg")
|
