def test_flat_dict(self):
node_attrs = sp.csr_matrix(
np.array([[0., 3., 2.], [0., 0., 4.], [1., 1., 0.], [0., 0., 1.],
[0., 2., 0.]]))
attr_names = np.array(['a', 'b', 'c'])
edge_attrs = np.array([[0, 1.], [1, 0.], [2, 1.], [3, 0.], [1, 0.],
[5, 4.], [6, 0.], [7, 3.], [8, 2.]])
edge_attr_names = np.array(['ae', 'be'])
labels = np.array([0, 1, 1, 0, 2])
class_names = np.array(['in', 'between', 'out'])
spA = SparseGraph(self.A,
attr_matrix=node_attrs,
edge_attr_matrix=edge_attrs,
attr_names=attr_names,
edge_attr_names=edge_attr_names,
labels=labels,
class_names=class_names)
d = spA.to_flat_dict()
spB = SparseGraph.from_flat_dict(d)
assert np.allclose(spA.adj_matrix.A, spB.adj_matrix.A)
assert (spA.attr_matrix != spB.attr_matrix).nnz == 0
assert np.allclose(spA.edge_attr_matrix, spB.edge_attr_matrix)
assert np.allclose(spA.labels, spB.labels)
assert all((a_name == b_name
for a_name, b_name in zip(spA.attr_names, spB.attr_names)))
assert all((a_name == b_name for a_name, b_name in zip(
spA.edge_attr_names, spB.edge_attr_names)))
assert all(
(a_name == b_name
for a_name, b_name in zip(spA.class_names, spB.class_names)))
def setup(self):
A = sp.csr_matrix(np.array(
[[1. , 0. , 0.5, 0. , 0. ],
[0. , 1. , 1. , 0. , 0. ],
[0.5, 0. , 1. , 0. , 0. ],
[0. , 0. , 1. , 0. , 2. ],
[0. , 1. , 0. , 0. , 0. ]]))
edge_attrs_A = np.array([0, 1, 2, 3, 1, 5, 6, 7, 8])
self.spA = SparseGraph(A, edge_attr_matrix=edge_attrs_A)
B = sp.csr_matrix(np.array(
[[0. , 0. , 0.2, 0. , 0. ],
[0. , 1. , 0. , 1. , 0. ],
[0.5, 0. , 0. , 1. , 0. ],
[0. , 0. , 1. , 0. , 0. ],
[1. , 0. , 0. , 2. , 0. ]]))
edge_attrs_B = np.array([7, 6, 5, 4, 3, 2, 1, 0])
self.spB = SparseGraph(B, edge_attr_matrix=edge_attrs_B)
dist_mat = np.array(
[[0. , 1. ],
[1. , 0. ]])
self.gcoll = GraphCollection([self.spA, self.spB], dist_mat)
dist_spmat = sp.csr_matrix(dist_mat)
self.gcoll_spdist = GraphCollection([self.spA, self.spB], dist_spmat)
C = sp.csr_matrix(np.array(
[[0. , 0. , 0.2, 0. ],
[0. , 1. , 0. , 1. ],
[0. , 3. , 1. , 0. ],
[1. , 0. , 0. , 2. ]]))
edge_attrs_C = np.array([0, 6, 1, 5, 2, 4, 3])
self.spC = SparseGraph(C, edge_attr_matrix=edge_attrs_C)
def test_wrong_edge_attr_dim(self):
for i in range(np.product(self.A.shape) + 2):
if i != self.A.nnz:
with pytest.raises(ValueError):
SparseGraph(self.A, edge_attr_matrix=np.arange(i))
else:
SparseGraph(self.A, edge_attr_matrix=np.arange(i))
def test_save_load_graph_collection():
A = sp.csr_matrix(
np.array([[1., 0., 0.5, 0., 0.], [0., 1., 1., 0., 0.],
[0.5, 0., 1., 0., 0.], [0., 0., 1., 0., 2.],
[0., 1., 0., 0., 0.]]))
edge_attrs_A = np.array([0, 1, 2, 3, 1, 5, 6, 7, 8])
spA = SparseGraph(A, edge_attr_matrix=edge_attrs_A)
B = sp.csr_matrix(
np.array([[0., 0., 0.2, 0., 0.], [0., 1., 0., 1., 0.],
[0.5, 0., 0., 1., 0.], [0., 0., 1., 0., 0.],
[1., 0., 0., 2., 0.]]))
edge_attrs_B = np.array([7, 6, 5, 4, 3, 2, 1, 0])
spB = SparseGraph(B, edge_attr_matrix=edge_attrs_B)
dist_mat = sp.csr_matrix(np.array([[0., 1.], [1., 0.]]))
gcollA = GraphCollection([spA, spB], dists=dist_mat, metadata="test")
test_file = "test_graph_collection.npz"
save_to_npz(test_file, gcollA)
gcollB = load_from_npz(test_file)
os.remove(test_file)
assert np.allclose(gcollA.graphs[0].adj_matrix.A,
gcollB.graphs[0].adj_matrix.A)
assert np.allclose(gcollA.graphs[0].edge_attr_matrix,
gcollB.graphs[0].edge_attr_matrix)
assert np.allclose(gcollA.graphs[1].adj_matrix.A,
gcollB.graphs[1].adj_matrix.A)
assert np.allclose(gcollA.graphs[1].edge_attr_matrix,
gcollB.graphs[1].edge_attr_matrix)
assert (gcollA.dists != gcollB.dists).nnz == 0
assert gcollA.metadata == gcollB.metadata
def test_standardize(self):
spA = SparseGraph(self.A)
spA.standardize()
resA = sp.csr_matrix(
np.array([[0., 0., 1., 0., 0.], [0., 0., 1., 0., 1.],
[1., 1., 0., 1., 0.], [0., 0., 1., 0., 1.],
[0., 1., 0., 1., 0.]]))
assert np.allclose(spA.adj_matrix.A, resA.A)
def test_to_undirected(self):
spA = SparseGraph(self.A)
spA.to_undirected()
resA = sp.csr_matrix(
np.array([[1., 0., 0.5, 0., 0.], [0., 1., 1., 0., 1.],
[0.5, 1., 1., 1., 0.], [0., 0., 1., 0., 2.],
[0., 1., 0., 2., 0.]]))
assert np.allclose(spA.adj_matrix.A, resA.A)
def test_standardize_edgeattrs(self):
edge_attrs = np.array([0, 1, 2, 3, 1, 5, 6, 7, 8])
spA = SparseGraph(self.A, edge_attr_matrix=edge_attrs)
spA.standardize()
resA = sp.csr_matrix(
np.array([[0., 0., 1., 0., 0.], [0., 0., 1., 0., 1.],
[1., 1., 0., 1., 0.], [0., 0., 1., 0., 1.],
[0., 1., 0., 1., 0.]]))
res_edge_attrs = np.array([1, 3, 8, 1, 3, 6, 6, 7, 8, 7])
assert np.allclose(spA.adj_matrix.A, resA.A)
assert np.allclose(spA.edge_attr_matrix, res_edge_attrs)
def test_to_undirected_edgeattrs(self):
edge_attrs = np.array([0, 1, 2, 3, 1, 5, 6, 7, 8])
spA = SparseGraph(self.A, edge_attr_matrix=edge_attrs)
spA.to_undirected()
resA = sp.csr_matrix(
np.array([[1., 0., 0.5, 0., 0.], [0., 1., 1., 0., 1.],
[0.5, 1., 1., 1., 0.], [0., 0., 1., 0., 2.],
[0., 1., 0., 2., 0.]]))
res_edge_attrs = np.array([0, 1, 2, 3, 8, 1, 3, 5, 6, 6, 7, 8, 7])
assert np.allclose(spA.adj_matrix.A, resA.A)
assert np.allclose(spA.edge_attr_matrix, res_edge_attrs)
def test_save_load_edgeattrs():
A = sp.csr_matrix(
np.array([[1., 0., 0.5, 0., 0.], [0., 1., 1., 0., 0.],
[0.5, 0., 1., 0., 0.], [0., 0., 1., 0., 2.],
[0., 1., 0., 0., 0.]]))
edge_attrs = np.array([0, 1, 2, 3, 1, 5, 6, 7, 8])
spA = SparseGraph(A, edge_attr_matrix=edge_attrs)
test_file = "test_edgeattrs.npz"
save_to_npz(test_file, spA)
spB = load_from_npz(test_file)
os.remove(test_file)
assert np.allclose(spA.adj_matrix.A, spB.adj_matrix.A)
assert np.allclose(spA.edge_attr_matrix, spB.edge_attr_matrix)
def test_to_undirected_edgeattrs_contradiction(self):
edge_attrs = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])
spA = SparseGraph(self.A, edge_attr_matrix=edge_attrs)
with pytest.raises(ValueError):
spA.to_undirected()
def test_to_undirected_contradiction(self):
self.A[0, 2] = 0.2
spA = SparseGraph(self.A)
with pytest.raises(ValueError):
spA.to_undirected()