def test_RDPG_sample(self):
np.random.seed(8888)
g = self.graph
p_mat = self.p_mat
estimator = RDPGEstimator(n_components=2)
estimator.fit(g)
_test_sample(estimator, p_mat, atol=0.2, n_samples=200)
def test_RDPG_score(self):
p_mat = self.p_mat
graph = self.graph
estimator = RDPGEstimator()
_test_score(estimator, p_mat, graph)
with pytest.raises(ValueError):
estimator.score_samples(graph=graph[1:100, 1:100])
def test_RDPG_nparams(self):
n_verts = 1000
g = self.graph
e = RDPGEstimator(n_components=2)
e.fit(g)
assert e._n_parameters() == n_verts * 2
g[100:, 50:] = 1
e = RDPGEstimator(n_components=2)
e.fit(g)
assert e._n_parameters() == n_verts * 4
def test_RDPG_fit(self):
np.random.seed(8888)
n_points = 2000
dists = np.random.uniform(0, 1, n_points)
points = hardy_weinberg(dists)
p_mat = points @ points.T
p_mat -= np.diag(np.diag(p_mat))
g = sample_edges(p_mat)
estimator = RDPGEstimator(loops=False, n_components=3)
estimator.fit(g)
assert_allclose(estimator.p_mat_, p_mat, atol=0.2)
def test_RDPG_intputs(self):
rdpge = RDPGEstimator()
with pytest.raises(TypeError):
RDPGEstimator(loops=6)
with pytest.raises(ValueError):
rdpge.fit(self.graph[:, :99])
with pytest.raises(ValueError):
rdpge.fit(self.graph[..., np.newaxis])
with pytest.raises(TypeError):
RDPGEstimator(ase_kws=5)
with pytest.raises(TypeError):
RDPGEstimator(diag_aug_weight="f")
with pytest.raises(ValueError):
RDPGEstimator(diag_aug_weight=-1)
with pytest.raises(TypeError):
RDPGEstimator(plus_c_weight="F")
with pytest.raises(ValueError):
RDPGEstimator(plus_c_weight=-1)
