def test_P_2_LR(
self,
P_2: np.ndarray,
minChi_P_2_LR: np.ndarray,
crispness_values_P_2_LR: np.ndarray,
optimal_crispness_P_2_LR: np.float64,
n_m_P_2_LR: np.int64,
top_eigenvalues_P_2_LR: np.ndarray,
method: str,
):
if method == "krylov":
pytest.importorskip("mpi4py")
pytest.importorskip("petsc4py")
pytest.importorskip("slepc4py")
g = GPCCA(P_2, eta=None, z="LR", method=method)
# The following very crude minChi testing is necessary,
# since the initial guess for the rotation matrix and thus minChi can vary.
minChi = g.minChi(2, 12)
assert len(minChi) == len(minChi_P_2_LR)
assert minChi[0] > -1e-08
assert minChi[1] > -1e-08
assert minChi[3] > -1e-01
assert minChi[10] > -1e-08
g.optimize({"m_min": 2, "m_max": 12})
n_m = g.n_m
assert_allclose(g.crispness_values, crispness_values_P_2_LR)
assert_allclose(g.optimal_crispness, optimal_crispness_P_2_LR)
assert_allclose(n_m, n_m_P_2_LR)
assert_allclose(g.top_eigenvalues, top_eigenvalues_P_2_LR)
assert_allclose(g.dominant_eigenvalues, top_eigenvalues_P_2_LR[:n_m])
def test_use_minChi(self):
kmin, kmax = 2, 9
kopt = []
for mu_ in [10, 50, 100, 200, 500, 1000]:
P, sd = get_known_input(mu(mu_))
g = GPCCA(P, eta=sd)
minChi = g.minChi(kmin, kmax)
kopt.append(kmax - 1 - np.argmax(np.flipud(minChi[1:-1])))
np.testing.assert_array_equal(kopt, [3] * 5 + [7])
def test_k_input(self, P: np.ndarray, sd: np.ndarray):
g = GPCCA(P, eta=sd)
with pytest.raises(
ValueError,
match=r"m_min \(5\) must be smaller than m_max \(3\)."):
g.minChi(m_min=5, m_max=3)
def test_too_small_kkmin(self, P: np.ndarray, sd: np.ndarray):
g = GPCCA(P, eta=sd)
with pytest.raises(
ValueError,
match=r"There is no point in clustering into `0` clusters."):
g.minChi(m_min=0, m_max=10)