def test_precomputed_sum(self, adata: AnnData):
mat = random_transition_matrix(adata.n_obs)
pk = PrecomputedKernel(mat)
vk = VelocityKernel(adata).compute_transition_matrix(softmax_scale=4)
expected = (0.5 * vk.transition_matrix) + (0.5 * pk.transition_matrix)
actual = (pk + vk).compute_transition_matrix()
np.testing.assert_array_almost_equal(
expected.toarray(), actual.transition_matrix.toarray())
def test_precomputed_no_adata(self):
pk = PrecomputedKernel(random_transition_matrix(50))
pk.write_to_adata()
assert isinstance(pk.adata, AnnData)
assert pk.adata.shape == (50, 1)
assert pk.adata.obs.shape == (50, 0)
assert pk.adata.var.shape == (1, 0)
assert "T_fwd_params" in pk.adata.uns.keys()
np.testing.assert_array_equal(pk.adata.obsp["T_fwd"].toarray(),
pk.transition_matrix.toarray())
def test_copy_cond_num(self, adata: AnnData):
for KernelClass in [
VelocityKernel,
ConnectivityKernel,
PalantirKernel,
PrecomputedKernel,
]:
if KernelClass is PrecomputedKernel:
k1 = KernelClass(random_transition_matrix(adata.n_obs),
compute_cond_num=True)
elif KernelClass is VelocityKernel:
k1 = KernelClass(
adata, compute_cond_num=True).compute_transition_matrix(
softmax_scale=4)
else:
k1 = KernelClass(
adata, compute_cond_num=True).compute_transition_matrix()
k2 = k1.copy()
assert k1.condition_number == k2.condition_number
def test_precomputed_transition_matrix(self, adata: AnnData):
mat = random_transition_matrix(adata.n_obs)
pk = PrecomputedKernel(mat)
np.testing.assert_array_equal(mat, pk.transition_matrix.toarray())
def test_precomputed_adata(self, adata: AnnData):
pk = PrecomputedKernel(random_transition_matrix(adata.n_obs),
adata=adata)
assert pk.adata is adata
def test_precomputed_not_a_transition_matrix(self):
mat = random_transition_matrix(100)
mat[0, 0] = 0xDEADBEEF
with pytest.raises(ValueError):
_ = PrecomputedKernel(mat)