-
Notifications
You must be signed in to change notification settings - Fork 0
/
evaluate_matching.py
179 lines (149 loc) · 5.4 KB
/
evaluate_matching.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
import os
import pickle
import time
from collections import defaultdict
import numpy as np
import torch
import utils
from dataset import BaseDataset, collate_fn
from model import GLeMaNet
from torch.utils.data import DataLoader
from tqdm import tqdm
def eval_mapping(groundtruth, predict_list, predict_prob):
acc = []
MRR = []
for sgn in groundtruth:
# Calculate precision
list_acc = []
for i in range(1, 11):
if groundtruth[sgn] in predict_list[sgn][:i]:
list_acc.append(1)
else:
list_acc.append(0)
acc.append(list_acc)
if groundtruth[sgn] in predict_list[sgn]:
MRR.append(1 / (predict_list[sgn].index(groundtruth[sgn]) + 1))
else:
MRR.append(0)
acc = np.mean(np.array(acc), axis=0)
MRR = np.mean(np.array(MRR))
return np.concatenate([acc, np.array([MRR])])
def evaluate(args):
data_path = os.path.join(args.data_path, args.dataset)
if args.directed:
data_path += "_directed"
result_dir = utils.ensure_dir(args.result_dir, args)
result_file = f"result_matching{args.test_keys[9:-4]}.csv"
args.train_keys = os.path.join(data_path, args.train_keys)
args.test_keys = os.path.join(data_path, args.test_keys)
with open(args.test_keys, "rb") as fp:
test_keys = pickle.load(fp)
# Only use isomorphism subgraphs for mapping testing
test_keys = list(filter(lambda x: x.endswith("iso_test"), test_keys))
print(f"Number of test data: {len(test_keys)}")
model = GLeMaNet(args)
print(
"Number of parameters: ",
sum(p.numel() for p in model.parameters() if p.requires_grad),
)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = utils.initialize_model(model, device, load_save_file=args.ckpt)
test_dataset = BaseDataset(
test_keys, data_path, embedding_dim=args.embedding_dim
)
test_dataloader = DataLoader(
test_dataset,
args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn,
)
# Starting evaluation
test_true_mapping = []
test_pred_mapping = []
list_results = []
model.eval()
st_eval = time.time()
for sample in tqdm(test_dataloader):
H, A1, A2, M, S, Y, V, _ = sample
H, A1, A2, M, S, Y, V = (
H.to(device),
A1.to(device),
A2.to(device),
M.to(device),
S.to(device),
Y.to(device),
V.to(device),
)
# Test neural network
with torch.no_grad():
pred = model.get_refined_adjs2((H, A1, A2, V))
# Collect true label and predicted label
test_true_mapping = M.data.cpu().numpy()
test_pred_mapping = pred.data.cpu().numpy()
for mapping_true, mapping_pred in zip(test_true_mapping, test_pred_mapping):
gt_mapping = {}
x_coord, y_coord = np.where(mapping_true > 0)
for x, y in zip(x_coord, y_coord):
if x < y:
gt_mapping[x] = [y] # Subgraph node: Graph node
pred_mapping = defaultdict(lambda: {})
x_coord, y_coord = np.where(mapping_pred > 0)
# TODO pred_mapping shoud be sorted by probability
for x, y in zip(x_coord, y_coord):
if x < y:
if y in pred_mapping[x]:
pred_mapping[x][y] = (
pred_mapping[x][y] + mapping_pred[x][y]
) / 2
else:
pred_mapping[x][y] = mapping_pred[
x, y
] # Subgraph node: Graph node
else:
if x in pred_mapping[y]:
pred_mapping[y][x] = (
pred_mapping[y][x] + mapping_pred[x][y]
) / 2
else:
pred_mapping[y][x] = mapping_pred[
x, y
] # Subgraph node: Graph node
sorted_predict_mapping = defaultdict(lambda: [])
sorted_predict_mapping.update(
{
k: [
y[0]
for y in sorted(
[(n, prob) for n, prob in v.items()],
key=lambda x: x[1],
reverse=True,
)
]
for k, v in pred_mapping.items()
}
)
results = eval_mapping(
gt_mapping, sorted_predict_mapping, pred_mapping)
list_results.append(results)
end = time.time()
list_results = np.array(list_results)
avg_results = np.mean(list_results, axis=0)
print("Test time: ", end - st_eval)
print("Top1-Top10 Accuracy, MRR")
print(avg_results)
with open(
os.path.join(result_dir, result_file),
"w",
encoding="utf-8",
) as f:
f.write(
"Time,Top1-Acc,Top2-Acc,Top3-Acc,Top4-Acc,Top5-Acc,Top6-Acc,Top7-Acc,Top8-Acc,Top9-Acc,Top10-Acc,MRR\n"
)
f.write("%f," % (end - st_eval))
f.write(",".join([str(x) for x in avg_results]))
f.write("\n")
if __name__ == "__main__":
args = utils.parse_args()
print(args)
evaluate(args)