def load_er_data():
frac_train = 0.9
pattern = 'nrange-%d-%d-n_graph-%d-p-%.2f' % (
cmd_args.min_n, cmd_args.max_n, cmd_args.n_graphs, cmd_args.er_p)
num_train = int(frac_train * cmd_args.n_graphs)
train_glist = []
test_glist = []
label_map = {}
for i in range(cmd_args.min_c, cmd_args.max_c + 1):
cur_list = load_pkl(
'%s/ncomp-%d-%s.pkl' % (cmd_args.data_folder, i, pattern),
cmd_args.n_graphs)
assert len(cur_list) == cmd_args.n_graphs
train_glist += [S2VGraph(cur_list[j], i) for j in range(num_train)]
test_glist += [
S2VGraph(cur_list[j], i) for j in range(num_train, len(cur_list))
]
label_map[i] = i - cmd_args.min_c
cmd_args.num_class = len(label_map)
cmd_args.feat_dim = 1
print('# train:', len(train_glist), ' # test:', len(test_glist))
return label_map, train_glist, test_glist
def load_graphs():
frac_train = 0.9
pattern = 'nrange-%d-%d-n_graph-%d-p-%.2f' % (
cmd_args.min_n, cmd_args.max_n, cmd_args.n_graphs, cmd_args.er_p)
num_train = int(frac_train * cmd_args.n_graphs)
train_glist = []
test_glist = []
label_map = {}
for i in range(cmd_args.min_c, cmd_args.max_c + 1):
cur_list = load_pkl(
'%s/ncomp-%d-%s.pkl' % (cmd_args.data_folder, i, pattern),
cmd_args.n_graphs)
print('Input filename:\n',
'%s/ncomp-%d-%s.pkl' % (cmd_args.data_folder, i, pattern))
assert len(cur_list) == cmd_args.n_graphs
print('----- type(cur_list) =\n', type(cur_list))
print('----- type(cur_list[0]) =\n', type(cur_list[0]))
train_glist += [S2VGraph(cur_list[j], i) for j in range(num_train)]
test_glist += [
S2VGraph(cur_list[j], i) for j in range(num_train, len(cur_list))
]
label_map[i] = i - cmd_args.min_c
print('# train:', len(train_glist), ' # test:', len(test_glist))
return label_map, train_glist, test_glist