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from umap.spectral import spectral_layout, tswspectral_layout
import numpy as np
import pytest
import re
from scipy.version import full_version as scipy_full_version_
from warnings import catch_warnings
scipy_full_version = tuple(
int(n)
for n in re.findall(r"[0-9]+\.[0-9]+\.?[0-9]*", scipy_full_version_)[0].split(".")
)
@pytest.mark.skipif(
scipy_full_version < (1, 10) or scipy_full_version >= (1, 15),
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")
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