def test_fit_spectral(device):
# TODO deflake this test
pytest.skip("This test is flaky on macOS.")
np.random.seed(0)
torch.random.manual_seed(0)
n = 200
m = 3
max_iter = 1000
edges = util.all_edges(n)
weights = torch.ones(edges.shape[0])
f = penalties.Quadratic(weights)
mde = problem.MDE(
n,
m,
edges=edges,
distortion_function=f,
constraint=Standardized(),
device=device,
)
X = mde.embed(max_iter=max_iter, eps=1e-10, memory_size=10)
assert id(X) == id(mde.X)
X_spectral = quadratic.spectral(n,
m,
edges=edges,
weights=weights,
device=device)
testing.assert_allclose(
mde.average_distortion(X).detach().cpu().numpy(),
mde.average_distortion(X_spectral).detach().cpu().numpy(),
atol=1e-4,
)
def test_self_edges_raises_error(device):
torch.random.manual_seed(0)
edges = np.array([(0, 1), (0, 0), (0, 2), (1, 2), (1, 1)])
with pytest.raises(ValueError,
match=r"The edge list must not contain self edges.*"):
problem.MDE(
3,
3,
edges,
penalties.Quadratic(torch.ones(edges.shape[0])),
constraint=Standardized(),
device=device,
)
def test_differences(device):
torch.random.manual_seed(0)
edges = np.array([(0, 1), (0, 2), (1, 2)])
X = torch.randn((3, 3), dtype=torch.float32, device=device)
mde = problem.MDE(
3,
3,
edges,
penalties.Quadratic(torch.ones(3)),
constraint=Standardized(),
device=device,
)
diff = mde.differences(X)
testing.assert_allclose(X[edges[:, 0]] - X[edges[:, 1]], diff)
def test_norm_grad_zero(device):
torch.random.manual_seed(0)
edges = np.array([(0, 1)])
mde = problem.MDE(
3,
3,
edges,
penalties.Quadratic(torch.ones(3)),
constraint=Standardized(),
device=device,
)
X = torch.ones((3, 3), requires_grad=True, device=device)
norms = mde.distances(X)
norms.backward()
testing.assert_allclose(X.grad, 0.0)
def _spectral(
L,
m,
cg=False,
max_iter=40,
edges=None,
weights=None,
warm_start=False,
device=None,
):
n = L.shape[0]
if not cg:
k = m + 1
num_lanczos_vectors = max(2 * k + 1, int(np.sqrt(L.shape[0])))
eigenvalues, eigenvectors = scipy.sparse.linalg.eigsh(
L,
k,
which="SM",
ncv=num_lanczos_vectors,
tol=1e-4,
v0=np.ones(L.shape[0]),
maxiter=L.shape[0] * 5,
)
order = np.argsort(eigenvalues)[1:k]
else:
k = m
if warm_start:
mde = problem.MDE(
n, m, edges, f=penalties.Quadratic(weights), device=device
)
X_init = mde.fit(max_iter=40, use_line_search=False)
else:
X_init = util.proj_standardized(
torch.tensor(np.random.randn(n, m), device=device), demean=True
)
eigenvalues, eigenvectors = scipy.sparse.linalg.lobpcg(
A=L,
X=X_init.cpu().numpy(),
# Y: search in the orthogonal complement of the ones vector
Y=np.ones((L.shape[0], 1)),
tol=None,
# largest: find the smallest eigenvalues
largest=False,
maxiter=max_iter,
)
order = np.argsort(eigenvalues)[0:k]
return eigenvectors[:, order]
def test_average_distortion(device):
torch.random.manual_seed(0)
edges = np.array([(0, 1), (0, 2), (1, 2)])
mde = problem.MDE(
3,
2,
edges,
penalties.Quadratic(torch.tensor([1.0, 2.0, 3.0])),
constraint=Standardized(),
device=device,
)
X = torch.tensor(
[[0.0, 0.0], [1.0, 1.0], [3.0, 3.0]],
dtype=torch.float32,
device=device,
)
average_distortion = mde.average_distortion(X)
# (1*2 + 2*18 + 3*8)/3 = (2 + 36 + 24)/3 = 62/3
testing.assert_allclose(average_distortion.detach().cpu().numpy(),
62.0 / 3)
def test_average_distortion_grad(device):
torch.random.manual_seed(0)
edges = np.array([(0, 1), (0, 2), (1, 2)])
f = penalties.Quadratic(torch.tensor([1.0, 2.0, 3.0], device=device))
mde = problem.MDE(3, 2, edges, f, Standardized(), device=device)
X = torch.randn(
(3, 2),
requires_grad=True,
dtype=torch.float32,
device=device,
)
average_distortion = mde.average_distortion(X)
average_distortion.backward()
A = torch.tensor(
[[1, 1, 0], [-1, 0, 1], [0, -1, -1]],
device=device,
).float()
auto_grad = X.grad
X.grad = None
util._distortion(X, f, A, mde._lhs, mde._rhs).backward()
manual_grad = X.grad
testing.assert_allclose(auto_grad, manual_grad)
