def test_mlgk_self_loops():
kedge = Constant(1.0)
knode = Constant(1.0)
q = 0.1
mlgk = MarginalizedGraphKernel(knode, kedge, q=q)
np.random.seed(2)
for i in range(10):
n = np.random.randint(4, 20)
A = np.random.randn(n, n)
A = A + A.T
G = [Graph.from_networkx(nx.from_numpy_array(A), weight='weight')]
K = mlgk(G).item()
K0 = MLGK(G[0], knode, kedge, q, q, nodal=False)
assert (K == pytest.approx(K0, 5e-4))
def test_mlgk_typecheck():
node_kernel = Constant(1.0)
edge_kernel = Constant(1.0)
mlgk = MarginalizedGraphKernel(node_kernel, edge_kernel, q=0.5)
G = [
Graph.from_networkx(unlabeled_graph1),
Graph.from_networkx(labeled_graph1),
Graph.from_networkx(weighted_graph1, weight='w')
]
with pytest.raises(TypeError):
mlgk([G[0], G[1]])
with pytest.raises(TypeError):
mlgk([G[0], G[2]])
with pytest.raises(TypeError):
mlgk([G[1], G[2]])
with pytest.raises(TypeError):
mlgk([G[1], G[0]])
with pytest.raises(TypeError):
mlgk([G[2], G[0]])
with pytest.raises(TypeError):
mlgk([G[2], G[1]])
def test_mlgk_large():
g = nx.Graph()
n = 24
for i, row in enumerate(np.random.randint(0, 2, (n, n))):
g.add_node(i, type=0)
for j, pred in enumerate(row[:i]):
if pred:
g.add_edge(i, j, weight=1)
dfg = Graph.from_networkx(g, weight='weight')
q = 0.5
node_kernel = TensorProduct(type=KroneckerDelta(1.0))
edge_kernel = Constant(1.0)
mlgk = MarginalizedGraphKernel(node_kernel, edge_kernel, q=q)
dot = mlgk([dfg])
gold = MLGK(dfg, node_kernel, edge_kernel, q, q)
assert (dot.shape == (1, 1))
assert (dot.item() == pytest.approx(gold))
def test_mlgk_dtype():
g = nx.Graph()
n = 8
for i, row in enumerate(np.random.randint(0, 2, (n, n))):
g.add_node(i, type=0)
for j, pred in enumerate(row[:i]):
if pred:
g.add_edge(i, j, weight=1)
dfg = Graph.from_networkx(g, weight='weight')
q = 0.5
node_kernel = TensorProduct(type=KroneckerDelta(1.0))
edge_kernel = Constant(1.0)
for dtype in [np.float, np.float32, np.float64]:
mlgk = MarginalizedGraphKernel(node_kernel,
edge_kernel,
q=q,
dtype=dtype)
assert (mlgk([dfg]).dtype == dtype)
assert (mlgk.diag([dfg]).dtype == dtype)
g2.add_edge(0, 1)
g2.add_edge(1, 2)
# {1.0, 1} -- {2.0, 1}
# \ /
# {1.0, 2}
g3 = nx.Graph()
g3.add_node(0, radius=1.0, category=1)
g3.add_node(1, radius=2.0, category=1)
g3.add_node(2, radius=1.0, category=2)
g3.add_edge(0, 1)
g3.add_edge(0, 2)
g3.add_edge(1, 2)
# define node and edge kernelets
knode = TensorProduct(radius=SquareExponential(0.5),
category=KroneckerDelta(0.5))
kedge = Constant(1.0)
# compose the marginalized graph kernel and compute pairwise similarity
mlgk = MarginalizedGraphKernel(knode, kedge, q=0.05)
R = mlgk([Graph.from_networkx(g) for g in [g1, g2, g3]])
# normalize the similarity matrix
d = np.diag(R)**-0.5
K = np.diag(d).dot(R).dot(np.diag(d))
print(K)
from graphdot import Graph
from graphdot.kernel.marginalized import MarginalizedGraphKernel
from graphdot.kernel.fix import Normalization
from graphdot.microkernel import (TensorProduct, DotProduct, Constant)
# The 'category' attribute on the nodes could have variable lengths.
# So does the 'spectra' attributes on the edges.
g1 = nx.Graph()
g1.add_node(0, soap=[0.5, 1.5, 2.5, 0.5])
g1.add_node(1, soap=[0.5, 1.5, 2.5, 0.5])
g1.add_edge(0, 1, w=1.0)
g2 = nx.Graph()
g2.add_node(0, soap=[0.5, 1.5, 2.5, 3.5])
g2.add_node(1, soap=[1.5, 1.5, 0.5, 3.5])
g2.add_node(2, soap=[0.5, 2.5, 2.5, 0.5])
g2.add_edge(0, 1, w=2.0)
g2.add_edge(0, 2, w=0.5)
g2.add_edge(1, 2, w=0.5)
# compose the marginalized graph kernel and compute pairwise similarity
mlgk = Normalization(
MarginalizedGraphKernel(
node_kernel=TensorProduct(soap=DotProduct().normalized),
edge_kernel=Constant(1),
q=0.05))
G = [Graph.from_networkx(g, weight='w') for g in [g1, g2]]
print(f'Whole-graph similarity\n{mlgk(G)}')
print(f'Nodal similarity\n{mlgk(G, nodal=True)}')
g2.add_node(2) g2.add_edge(0, 1) g2.add_edge(1, 2) # 0 --- 1 # \ / # 2 g3 = nx.Graph() g3.add_node(0) g3.add_node(1) g3.add_node(2) g3.add_edge(0, 1) g3.add_edge(0, 2) g3.add_edge(1, 2) # define trivial node and edge kernelets knode = Constant(1.0) kedge = Constant(1.0) # compose the marginalized graph kernel and compute pairwise similarity mlgk = MarginalizedGraphKernel(knode, kedge, q=0.05) R = mlgk([Graph.from_networkx(g) for g in [g1, g2, g3]]) # normalize the similarity matrix d = np.diag(R)**-0.5 K = np.diag(d).dot(R).dot(np.diag(d)) # all entries should be approximately 1 plus round-off error print(K)
vario_graph1.add_edge('O1', 'H2', spectrum=(3, 5), w=2.0)
vario_graph2 = nx.Graph(title='H2')
vario_graph2.add_node('H1', rings=(3, 4))
vario_graph2.add_node('H2', rings=(3, ))
vario_graph2.add_edge('H1', 'H2', spectrum=(2, 4), w=3.0)
case_dict = {
'unlabeled': {
'graphs':
Graph.unify_datatype([
Graph.from_networkx(unlabeled_graph1),
Graph.from_networkx(unlabeled_graph2)
]),
'knode':
Constant(1.0),
'kedge':
Constant(1.0),
'q': [0.01, 0.05, 0.1, 0.5]
},
'labeled': {
'graphs':
Graph.unify_datatype([
Graph.from_networkx(labeled_graph1),
Graph.from_networkx(labeled_graph2)
]),
'knode':
TensorProduct(hybridization=KroneckerDelta(0.3),
charge=SquareExponential(1.) + 0.01).normalized,
'kedge':
Additive(order=KroneckerDelta(0.3),