-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathpreprocessing_syn.py
191 lines (154 loc) · 7.41 KB
/
preprocessing_syn.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
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
# [ Imports ]
# [ -Python ]
import os
import pickle
import sys
import time
# [ -Third Party ]
import numpy as np
# [ -Project ]
from utils.attr_utils import addEdgeAttribute, addNodeAttribute, getUndirAttribute, getDirAttribute
from utils.multi_sparse_utils import generate_multi_graph_synthetic, get_multi_graph_signature, find_center
from utils.io_sparse_utils import loadEdgeFeature, loadNodeFeature
def preprocessing(edge_dir, node_dir = None, edge_label_dir = None, save_dir = "", graph_type = 'Undirected',
number = 5, noise_level = 0.01, weighted_noise = 1.0, center_distance = 'canberra', findcenter = 0,
attr_only = False, edge_noise_only = False, weighted = False, node_label = True, is_perm = True):
#findcenter = 1: find and check that one and original center; 0: check all, -1: original only
path = './private_data/' + save_dir
if not os.path.exists(path):
os.makedirs(path)
start_preprocess = time.time()
multi_graphs, multi_perm, syn_path = generate_multi_graph_synthetic(
filename=edge_dir, graph_type=graph_type, number=number,
edge_noise_only=edge_noise_only, noise_level=noise_level, weighted_noise=weighted_noise, weighted=weighted, is_perm=is_perm
)
node_num, n = multi_graphs['M0'].get_shape()
nodeAttributesValue, nodeAttributesName = [], []
edgeAttributessValue, edgeAttributesName = [], []
# P = sparse.lil_matrix((node_num, n))
# for i in range(node_num):
# P[i, i] = 1
# P = P.tocsr()
graph_attrs = {}
if node_dir:
nodeAttributesValue, nodeAttributesName = loadNodeFeature(node_dir)
if edge_label_dir:
edgeAttributessValue, edgeAttributesName = loadEdgeFeature(edge_label_dir)
### get graph attributes
attributes = []
if graph_type == 'Undirected':
if not attr_only:
attributes = [
'Degree', 'NodeBetweennessCentrality', 'PageRank',
'EgonetDegree', 'AvgNeighborDeg', 'EgonetConnectivity'
]
if weighted_noise:
attributes += [
'WeightedDegree', 'EgoWeightedDegree', 'AvgWeightedNeighborDeg', 'EgonetWeightedConnectivity'
]
attributes += edgeAttributesName
for key in multi_graphs.keys():
attributesA = getUndirAttribute(syn_path + '/' + key +'.edges', node_num, weighted_noise)
# attributesA = getUndirAttribute(syn_path + '/' + key, node_num)
# TODO: handle when permutation possible
if key == 'M0':
attributesA = addEdgeAttribute(attributesA, edgeAttributesName, edgeAttributessValue, multi_perm[key])
attributesA, onehot_nodeAttributeNames = addNodeAttribute(attributesA, nodeAttributesName, nodeAttributesValue, multi_perm[key])
else:
attributesA = addEdgeAttribute(attributesA, edgeAttributesName, edgeAttributessValue, multi_perm[key], noise_level = noise_level)
attributesA, onehot_nodeAttributeNames = addNodeAttribute(attributesA, nodeAttributesName, nodeAttributesValue, multi_perm[key], noise_level = noise_level)
graph_attrs[key] = attributesA[['Graph', 'Id']+attributes + onehot_nodeAttributeNames]
attributes += onehot_nodeAttributeNames
elif graph_type == 'Directed':
if not attr_only:
attributes = [
'Degree', 'InDegree', 'OutDegree', 'NodeBetweennessCentrality',
'PageRank', 'HubsScore', 'AuthoritiesScore',
'EgonetDegree', 'EgonetInDegree', 'EgonetOutDegree',
'AvgNeighborDeg', 'AvgNeighborInDeg', 'AvgNeighborOutDeg','EgonetConnectivity'
]
if weighted_noise:
attributes += [
'WeightedDegree', 'WeightedInDegree', 'WeightedOutDegree', 'EgoWeightedDegree',
'AvgWeightedNeighborDeg', 'EgonetWeightedConnectivity',
'EgoWeightedInDegree', 'EgoWeightedOutDegree', 'AvgWeightedNeighborInDeg', 'AvgWeightedNeighborOutDeg'
]
attributes += edgeAttributesName
for key in multi_graphs.keys():
attributesA = getDirAttribute(syn_path + '/' + key + '.edges', node_num, weighted_noise)
# attributesA = getDirAttribute(syn_path + '/' + key, node_num)
if key == 'M0':
attributesA = addEdgeAttribute(attributesA, edgeAttributesName, edgeAttributessValue, multi_perm[key])
attributesA, onehot_nodeAttributeNames = addNodeAttribute(attributesA, nodeAttributesName, nodeAttributesValue, multi_perm[key])
else:
attributesA = addEdgeAttribute(attributesA, edgeAttributesName, edgeAttributessValue, multi_perm[key], noise_level=noise_level)
attributesA, onehot_nodeAttributeNames = addNodeAttribute(attributesA, nodeAttributesName, nodeAttributesValue, multi_perm[key], noise_level=noise_level)
graph_attrs[key] = attributesA[['Graph', 'Id'] + attributes + onehot_nodeAttributeNames]
attributes += onehot_nodeAttributeNames
with open(path + '/attributes', 'w') as f:
for a in attributes:
f.write(a + '\n')
graph_signatures = get_multi_graph_signature(graph_attrs)
centers = []
found_center = find_center(graph_signatures, center_distance)
print("found center: "+found_center)
if findcenter == 1:
centers.append(found_center)
if centers[0] != 'M0':
centers.append('M0')
else:
print("found same center!!")
elif findcenter == 0:
centers = sorted(multi_graphs.keys())
else:
centers.append('M0')
if number == 1:
centers = ['M0']
print("check for center graph: {}".format(centers))
# Save
# save centers
with open(path + '/centers', 'w') as f:
for c in centers:
f.write(c + '\n')
f.close()
# print list(graph_attrs['M1']['Degree'])
pickle.dump(multi_graphs, open(path + '/multi_graphs.pkl', 'wb'))
pickle.dump(graph_attrs, open(path + '/attributes.pkl', 'wb'))
pickle.dump(multi_perm, open(path + '/permutations.pkl', 'wb'))
if node_label:
pickle.dump(np.array(nodeAttributesValue)[:,0], open(path + '/node_label.pkl', 'wb'))
else:
pickle.dump(None, open(path + '/node_label.pkl', 'wb'))
# g = pickle.load(open(path + '/attributes.pkl', 'rb'))
# print list(g['M1']['Degree'])
end_preprocess = time.time()
preprocess_time = end_preprocess - start_preprocess
with open(path + '/metadata', 'w') as f:
f.write('graph_type' + " " + str(graph_type) + '\n')
f.write('noise_level' + " " + str(noise_level) + '\n')
f.write('weighted_noise' + " " + str(weighted_noise) + '\n')
f.write('found_center' + " " + str(found_center) + '\n')
f.write('number' + " " + str(number) + '\n')
f.write('node_dir' + " " + str(node_dir) + '\n')
f.write('edge_label_dir' + " " + str(edge_label_dir) + '\n')
f.write('center_distance' + " " + str(center_distance) + '\n')
f.write('node_attribute_number' + " " + str(len(onehot_nodeAttributeNames)) + '\n')
f.write('node_label' + " " + str(int(node_label)) + '\n')
f.write('preprocess_time' + " " + str(preprocess_time) + '\n')
f.close()
print('noise level: '+str(noise_level))
print('Pre-processing time: ' + str(preprocess_time))
if __name__ == '__main__':
# python prepocessing_syn.py edge_dir [node_dir] save_dir num_graphs
if len(sys.argv) == 4:
preprocessing(edge_dir = sys.argv[1], save_dir = sys.argv[2], number = int(sys.argv[3]))
# elif len(sys.argv) == 5:
# preprocessing(edge_dir = sys.argv[1], node_dir = sys.argv[2], number = int(sys.argv[4]), save_dir = sys.argv[3])
elif len(sys.argv) == 5:
preprocessing(edge_dir = sys.argv[1], save_dir = sys.argv[2], noise_level = float(sys.argv[3]), number = int(sys.argv[4]))
elif len(sys.argv) == 6:
preprocessing(edge_dir = sys.argv[1], node_dir = sys.argv[2], save_dir = sys.argv[3]
, noise_level = float(sys.argv[4]), number = int(sys.argv[5]))
elif len(sys.argv) == 7:
preprocessing(edge_dir = sys.argv[1], node_dir = sys.argv[2], edge_label_dir = sys.argv[3], save_dir = sys.argv[4]
, noise_level = float(sys.argv[5]), number = int(sys.argv[6]))