-
Notifications
You must be signed in to change notification settings - Fork 19
/
process_dataset.py
151 lines (130 loc) · 5.41 KB
/
process_dataset.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
import os
import pickle
import networkx as nx
import numpy as np
import scipy.sparse as sp
from utils.arg_helper import mkdir
from utils.visual_utils import plot_graphs_list
# load ENZYMES and PROTEIN and DD dataset
def graph_load_batch(min_num_nodes=20, max_num_nodes=1000, name='ENZYMES', node_attributes=True, graph_labels=True):
"""
load many graphs, e.g. enzymes
:return: a list of graphs
"""
print('Loading graph dataset: ' + str(name))
G = nx.Graph()
# load data
path = 'dataset/' + name + '/'
data_adj = np.loadtxt(path + name + '_A.txt', delimiter=',').astype(int)
data_node_att = []
if node_attributes:
data_node_att = np.loadtxt(path + name + '_node_attributes.txt', delimiter=',')
data_node_label = np.loadtxt(path + name + '_node_labels.txt', delimiter=',').astype(int)
data_graph_indicator = np.loadtxt(path + name + '_graph_indicator.txt', delimiter=',').astype(int)
if graph_labels:
data_graph_labels = np.loadtxt(path + name + '_graph_labels.txt', delimiter=',').astype(int)
data_tuple = list(map(tuple, data_adj))
# print(len(data_tuple))
# print(data_tuple[0])
# add edges
G.add_edges_from(data_tuple)
# add node attributes
for i in range(data_node_label.shape[0]):
if node_attributes:
G.add_node(i + 1, feature=data_node_att[i])
G.add_node(i + 1, label=data_node_label[i])
G.remove_nodes_from(list(nx.isolates(G)))
print(G.number_of_nodes())
print(G.number_of_edges())
# split into graphs
graph_num = data_graph_indicator.max()
node_list = np.arange(data_graph_indicator.shape[0]) + 1
graphs = []
max_nodes = 0
for i in range(graph_num):
# find the nodes for each graph
nodes = node_list[data_graph_indicator == i + 1]
G_sub = G.subgraph(nodes)
if graph_labels:
G_sub.graph['label'] = data_graph_labels[i]
# print('nodes', G_sub.number_of_nodes())
# print('edges', G_sub.number_of_edges())
# print('label', G_sub.graph)
if min_num_nodes <= G_sub.number_of_nodes() <= max_num_nodes:
graphs.append(G_sub)
if G_sub.number_of_nodes() > max_nodes:
max_nodes = G_sub.number_of_nodes()
# print(G_sub.number_of_nodes(), 'i', i)
# print('Graph dataset name: {}, total graph num: {}'.format(name, len(graphs)))
# logging.warning('Graphs loaded, total num: {}'.format(len(graphs)))
print('Loaded')
return graphs
def parse_index_file(filename):
index = []
for line in open(filename):
index.append(int(line.strip()))
return index
# load cora, citeseer and pubmed dataset
def graph_load(dataset='cora'):
"""
Load a single graph dataset
:param dataset: dataset name
:return:
"""
names = ['x', 'tx', 'allx', 'graph']
objects = []
for i in range(len(names)):
load = pickle.load(open("dataset/ind.{}.{}".format(dataset, names[i]), 'rb'), encoding='latin1')
# print('loaded')
objects.append(load)
# print(load)
# [x, tx, allx]: <class 'list'>: [(140, 1433), (1000, 1433), (1708, 1433)]
# len(graph) == 2708
x, tx, allx, graph = tuple(objects)
test_idx_reorder = parse_index_file("dataset/ind.{}.test.index".format(dataset))
test_idx_range = np.sort(test_idx_reorder)
if dataset == 'citeseer':
# Fix citeseer dataset (there are some isolated nodes in the graph)
# Find isolated nodes, add them as zero-vecs into the right position
test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder) + 1)
tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1]))
tx_extended[test_idx_range - min(test_idx_range), :] = tx
tx = tx_extended
features = sp.vstack((allx, tx)).tolil()
features[test_idx_reorder, :] = features[test_idx_range, :]
G = nx.from_dict_of_lists(graph)
return features, G
def citeseer_ego(radius=3, node_min=50, node_max=400):
_, G = graph_load(dataset='citeseer')
G = max(nx.connected_component_subgraphs(G), key=len)
G = nx.convert_node_labels_to_integers(G)
graphs = []
for i in range(G.number_of_nodes()):
G_ego = nx.ego_graph(G, i, radius=radius)
assert isinstance(G_ego, nx.Graph)
if G_ego.number_of_nodes() >= node_min and (G_ego.number_of_nodes() <= node_max):
G_ego.remove_edges_from(G_ego.selfloop_edges())
graphs.append(G_ego)
return graphs
def save_dataset(graphs, save_name):
mkdir('data')
file_path = os.path.join('data', save_name)
print(save_name, len(graphs))
with open(file_path + '.pkl', 'wb') as f:
pickle.dump(obj=graphs, file=f, protocol=pickle.HIGHEST_PROTOCOL)
with open(file_path + '.txt', 'w') as f:
f.write(save_name + '\n')
f.write(str(len(graphs)))
plot_graphs_list(graphs, title=save_name, save_dir='data')
if __name__ == '__main__':
dataset_name = 'PROTEINS_full'
suffix = '_30'
graphs = graph_load_batch(min_num_nodes=20, max_num_nodes=30, name=dataset_name,
node_attributes=True, graph_labels=True)
print(max([g.number_of_nodes() for g in graphs]))
save_dataset(graphs, dataset_name + suffix)
dataset_name = 'ego'
suffix = '_small'
graphs = citeseer_ego(radius=1, node_min=4, node_max=18)[:200]
save_dataset(graphs, dataset_name+suffix)
print(max([g.number_of_nodes() for g in graphs]))