def test_core_framework():
"""Picklability test for the Core kernel Framework [+ generic-wrapper]."""
train, _ = generate_dataset(n_graphs=100,
r_vertices=(10, 20),
r_connectivity=(0.4, 0.8),
r_weight_edges=(1, 1),
n_graphs_test=40,
random_state=rs,
features=('nl', 4))
base_graph_kernel = (WeisfeilerLehman,
dict(base_graph_kernel=VertexHistogram))
core_framework = CoreFramework(verbose=verbose,
normalize=normalize,
base_graph_kernel=base_graph_kernel)
kernel = [{
"name": "core_framework"
}, {
"name": "weisfeiler_lehman"
}, {
"name": "vertex_histogram"
}]
gk = GraphKernel(kernel=kernel, verbose=verbose, normalize=normalize)
core_framework.fit(train)
assert is_picklable(core_framework)
gk.fit(train)
assert is_picklable(gk)
def test_graphlet_sampling():
"""Picklability test for the Graphlet Sampling Kernel [+ generic-wrapper]."""
train, _ = generate_dataset(n_graphs=100,
r_vertices=(10, 20),
r_connectivity=(0.4, 0.8),
r_weight_edges=(1, 1),
n_graphs_test=40,
random_state=rs,
features=('nl', 3))
gs_kernel = GraphletSampling(verbose=verbose,
normalize=normalize,
sampling=dict(n_samples=50))
gk = GraphKernel(kernel={
"name": "graphlet_sampling",
"sampling": {
"n_samples": 50
}
},
verbose=verbose,
normalize=normalize)
gs_kernel.fit(train)
assert is_picklable(gs_kernel)
gk.fit(train)
assert is_picklable(gk)
def test_pyramid_match():
"""Picklability test for the Pyramid Match kernel [+ generic-wrapper]."""
train, _ = generate_dataset(n_graphs=100,
r_vertices=(10, 20),
r_connectivity=(0.4, 0.8),
r_weight_edges=(1, 1),
n_graphs_test=40,
random_state=rs,
features=('nl', 3))
pm_kernel = PyramidMatch(verbose=verbose, normalize=normalize)
gk = GraphKernel(kernel={"name": "pyramid_match"},
verbose=verbose,
normalize=normalize)
pm_kernel.fit(train)
assert is_picklable(pm_kernel)
gk.fit(train)
assert is_picklable(gk)
def test_edge_histogram():
"""Picklability test for the Edge Histogram kernel [+ generic-wrapper]."""
train, _ = generate_dataset(n_graphs=100,
r_vertices=(10, 20),
r_connectivity=(0.4, 0.8),
r_weight_edges=(1, 1),
n_graphs_test=40,
random_state=rs,
features=('el', 4))
eh_kernel = EdgeHistogram(verbose=verbose, normalize=normalize)
gk = GraphKernel(kernel={"name": "edge_histogram"},
verbose=verbose,
normalize=normalize)
eh_kernel.fit(train)
assert is_picklable(eh_kernel)
gk.fit(train)
assert is_picklable(gk)
def test_odd_sth():
"""Picklability test for the ODD-STh kernel [+ generic-wrapper]."""
train, _ = generate_dataset(n_graphs=100,
r_vertices=(10, 20),
r_connectivity=(0.4, 0.8),
r_weight_edges=(1, 1),
n_graphs_test=40,
random_state=rs,
features=('nl', 4))
odd_sth_kernel = OddSth(verbose=verbose, normalize=normalize)
gk = GraphKernel(kernel={"name": "odd_sth"},
verbose=verbose,
normalize=normalize)
odd_sth_kernel.fit(train)
assert is_picklable(odd_sth_kernel)
gk.fit(train)
assert is_picklable(gk)
def test_neighborhood_subgraph_pairwise_distance():
"""Picklability test for the Neighborhood Subgraph Pairwise Distance kernel [+ generic-wrapper]."""
train, _ = generate_dataset(n_graphs=100,
r_vertices=(5, 10),
r_connectivity=(0.4, 0.8),
r_weight_edges=(1, 1),
n_graphs_test=40,
random_state=rs,
features=('nl', 5, 'el', 4))
nspd_kernel = NeighborhoodSubgraphPairwiseDistance(verbose=verbose,
normalize=normalize)
gk = GraphKernel(
kernel={"name": "neighborhood_subgraph_pairwise_distance"},
verbose=verbose,
normalize=normalize)
nspd_kernel.fit(train)
assert is_picklable(nspd_kernel)
gk.fit(train)
assert is_picklable(gk)
def untangle(graph,
k_hop,
with_data: bool = True,
with_call: bool = True,
with_name: bool = True):
seeds, list_of_graphs = deltaPDG_to_list_of_Graphs(graph, khop_k=k_hop)
wl_subtree = GraphKernel(kernel=[{
"name": "weisfeiler_lehman",
"n_iter": 10
}, {
"name": "subtree_wl"
}],
normalize=True)
if len(list_of_graphs) > 0:
similarities = defaultdict(lambda: (0, 0.0))
for g1, g2 in itertools.combinations(list_of_graphs, 2):
# The graph has to be converted to {Graph, Node_Labels, Edge_Labels}
wl_subtree.fit(
[graph_to_grakel(g1, with_data, with_call, with_name)])
similarity = wl_subtree.transform(
[graph_to_grakel(g2, with_data, with_call, with_name)])[0][0]
similarities[(list_of_graphs.index(g1),
list_of_graphs.index(g2))] = similarity
n = len(list_of_graphs)
affinity = np.zeros(shape=(scipy.special.comb(n, 2, exact=True), ))
args = list(enumerate(itertools.combinations(range(n), 2)))
with ThreadPool(processes=min(os.cpu_count() - 1, 1)) as wp:
for k, value in wp.imap_unordered(
lambda i: (i[0], similarities[(i[-1][0], i[-1][1])]),
args):
affinity[k] += (1 - value
) # affinity is distance! so (1 - sim)
cluster = AgglomerativeClustering(n_clusters=None,
distance_threshold=0.5,
affinity='precomputed',
linkage='complete')
if len(affinity) < 2:
if len(affinity) == 1:
labels = np.asarray(
[0, 0]) if affinity[0] <= 0.5 else np.asarray([0, 1])
else:
labels = np.asarray([0])
else:
labels = cluster.fit_predict(
scipy.spatial.distance.squareform(affinity))
else:
labels = None
label = list()
for node, data in graph.nodes(data=True):
if 'color' in data.keys():
i = seeds.index(node) if node in seeds else -1
if labels is not None and i != -1:
data['label'] = '%d: ' % labels[i] + data['label']
label.append(labels[i])
graph.add_node(node, **data)
else:
data['label'] = '-1: ' + data['label']
label.append(-1)
graph.add_node(node, **data)
return graph
def worker(work):
for graph_location in tqdm(work, leave=False):
chain = os.path.basename(
os.path.dirname(os.path.dirname(graph_location)))
q = int(os.path.basename(os.path.dirname(graph_location)))
graph = obj_dict_to_networkx(read_graph_from_dot(graph_location))
graph = remove_all_except(graph, edges_kept)
if len(graph.nodes) == 0:
continue
t0 = time.perf_counter()
for i in range(times):
seeds, list_of_graphs = deltaPDG_to_list_of_Graphs(
graph, khop_k=k_hop)
wl_subtree = GraphKernel(kernel=[{
"name": "weisfeiler_lehman",
"n_iter": 10
}, {
"name": "subtree_wl"
}],
normalize=True)
if len(list_of_graphs) > 0:
similarities = defaultdict(lambda: (0, 0.0))
for g1, g2 in itertools.combinations(list_of_graphs, 2):
# The graph has to be converted to {Graph, Node_Labels, Edge_Labels}
wl_subtree.fit([
graph_to_grakel(g1, with_data, with_call,
with_name)
])
similarity = wl_subtree.transform([
graph_to_grakel(g2, with_data, with_call,
with_name)
])[0][0]
similarities[(list_of_graphs.index(g1),
list_of_graphs.index(g2))] = similarity
n = len(list_of_graphs)
affinity = np.zeros(
shape=(scipy.special.comb(n, 2, exact=True), ))
args = list(enumerate(itertools.combinations(range(n), 2)))
with ThreadPool(processes=min(os.cpu_count() -
1, 1)) as wp:
for k, value in wp.imap_unordered(
lambda i: (i[0], similarities[
(i[-1][0], i[-1][1])]), args):
affinity[k] += (
1 - value
) # affinity is distance! so (1 - sim)
cluster = AgglomerativeClustering(n_clusters=None,
distance_threshold=0.5,
affinity='precomputed',
linkage='complete')
if len(affinity) < 2:
if len(affinity) == 1:
labels = np.asarray([
0, 0
]) if affinity[0] <= 0.5 else np.asarray([0, 1])
else:
labels = np.asarray([0])
else:
labels = cluster.fit_predict(
scipy.spatial.distance.squareform(affinity))
else:
labels = None
t1 = time.perf_counter()
time_ = (t1 - t0) / times
truth = list()
label = list()
for node, data in graph.nodes(data=True):
if 'color' in data.keys():
if 'community' in data.keys():
truth.append(int(data['community']))
i = seeds.index(node) if node in seeds else -1
if labels is not None and i != -1:
data['label'] = '%d: ' % labels[i] + data['label']
label.append(labels[i])
graph.add_node(node, **data)
else:
data['label'] = '-1: ' + data['label']
label.append(-1)
graph.add_node(node, **data)
nx.drawing.nx_pydot.write_dot(
graph, graph_location[:-4] + '_output_wl_%d.dot' % k_hop)
truth = np.asarray(truth)
label = np.asarray(label)
acc, overlap = evaluate(truth[label > -1],
label[label > -1],
q=1 if len(label) == 0 else np.max(label) +
1)
with open(
'./out/%s/wl_%s_%d_results_%s.csv' %
(repository_name, edges_kept, k_hop, suffix), 'a') as f:
f.write(chain + ',' + str(q) + ',' + str(acc) + ',' +
str(overlap) + ',' + str(time_) + '\n')
