def test_normal(self):
graphs = helper.get_complete_graphs(num_graphs=NUM_GRAPHS, as_tuples=False)
gram_matrix = spgk.transform(graphs, depth=DEPTH)
for graph_idx, row in enumerate(gram_matrix):
for other_graph_ix, similarity in enumerate(row):
self.assertEqual(similarity, 1)
def test_no_similarity(self):
graph_1 = nx.Graph()
graph_1.add_edge('1', '2')
graph_2 = nx.Graph()
graph_2.add_edge('3', '4')
graphs = [graph_1, graph_2]
gram_matrix = spgk.transform(graphs, depth=DEPTH)
for graph_idx, row in enumerate(gram_matrix):
row_max = max(row)
for other_graph_ix, similarity in enumerate(row):
# If the compared graphs are the same, the score should be above 0
# and should be the highest for that graph (because no graph should be more similar than the same graph)
if graph_idx == other_graph_ix:
self.assertGreater(similarity, 0)
self.assertEqual(similarity, row_max)
else:
self.assertEqual(similarity, 0)
def process_graph_cache_file(graph_cache_file, args):
graph_cache_filename = graph_cache_file.split('/')[-1].rsplit('.')[0]
dataset = filename_utils.get_dataset_from_filename(graph_cache_file)
if '.phi.' in graph_cache_filename or not filter_utils.file_should_be_processed(
graph_cache_filename, args.include_filter, args.exclude_filter,
args.limit_dataset):
return
LOGGER.info('{:15} starting ({})'.format(dataset, graph_cache_filename))
fast_wl_trans = FastWLGraphKernelTransformer(
h=args.wl_h, use_early_stopping=False, truncate_to_highest_label=False)
try:
phi_graph_cache_file = graph_cache_file.replace('.npy', '.phi.npy')
X_graphs, Y = dataset_helper.get_dataset_cached(graph_cache_file)
X_graphs = graph_helper.get_graphs_only(X_graphs)
# Kernel: WL
if args.use_wl:
used_phi_graph_cache_file = phi_graph_cache_file
splitted_phi_graph_cache_file = phi_graph_cache_file.replace(
'.phi', '.splitted.phi')
phi_same_label_graph_cache_file = phi_graph_cache_file.replace(
dataset, '{}_same-label'.format(dataset)).replace(
'.phi', '.splitted.phi')
# Stop here if all files have already been created
if not args.force and np.all([
os.path.exists(x) for x in
[
splitted_phi_graph_cache_file, used_phi_graph_cache_file,
phi_same_label_graph_cache_file
]
]):
return
X_, Y_ = np.array(np.copy(X_graphs)), np.array(np.copy(Y))
if args.wl_sort_classes:
X_, Y_ = sort(X_, Y_, by=Y_)
num_vertices = len(graph_helper.get_all_node_labels(X_))
fast_wl_trans.set_params(phi_dim=num_vertices)
X_train, X_test, Y_train, Y_test = sklearn.model_selection.train_test_split(
np.copy(X_),
np.copy(Y_),
stratify=Y_,
test_size=args.wl_test_size)
X_train, Y_train = sort(X_train, Y_train, by=Y_train)
X_test, Y_test = sort(X_test, Y_test, by=Y_test)
# Splitted version
if args.force or not os.path.exists(splitted_phi_graph_cache_file):
t = sklearn.base.clone(fast_wl_trans).set_params(
same_label=True)
phi_train = t.fit_transform(np.copy(X_train))
phi_test = t.transform(np.copy(X_test))
with open(splitted_phi_graph_cache_file, 'wb') as f:
pickle.dump((phi_train, phi_test, X_train, X_test, Y_train,
Y_test), f)
# Splitted, same label
if args.force or not os.path.exists(
phi_same_label_graph_cache_file):
t = sklearn.base.clone(fast_wl_trans)
phi_train = t.fit_transform(X_train)
phi_test = t.transform(X_test)
with open(phi_same_label_graph_cache_file, 'wb') as f:
pickle.dump((phi_train, phi_test, X_train, X_test, Y_train,
Y_test), f)
# Whole dataset
if args.force or not os.path.exists(used_phi_graph_cache_file):
t = sklearn.base.clone(fast_wl_trans)
with open(used_phi_graph_cache_file, 'wb') as f:
pickle.dump((t.fit_transform(X_), Y_), f)
# Kernel: spgk
if args.use_spgk:
for depth in args.spgk_depth:
spgk_graph_cache_file = graph_cache_file.replace(
'.npy', '.spgk-{}.gram.npy'.format(depth))
if args.force or not os.path.exists(spgk_graph_cache_file):
K = spgk.transform(X_graphs, depth=depth)
with open(spgk_graph_cache_file, 'wb') as f:
pickle.dump((K, Y), f)
except Exception as e:
LOGGER.exception(e)
LOGGER.info('{:15} finished ({})'.format(dataset, graph_cache_filename))