-
Notifications
You must be signed in to change notification settings - Fork 7
/
Copy pathpagelink.py
151 lines (123 loc) · 6.56 KB
/
pagelink.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 torch
import argparse
import pickle
from tqdm.auto import tqdm
from pathlib import Path
from utils import set_seed, print_args, set_config_args
from data_processing import load_dataset
from model import HeteroRGCN, HeteroLinkPredictionModel
from explainer import PaGELink
parser = argparse.ArgumentParser(description='Explain link predictor')
parser.add_argument('--device_id', type=int, default=-1)
'''
Dataset args
'''
parser.add_argument('--dataset_dir', type=str, default='datasets')
parser.add_argument('--dataset_name', type=str, default='aug_citation')
parser.add_argument('--valid_ratio', type=float, default=0.1)
parser.add_argument('--test_ratio', type=float, default=0.2)
parser.add_argument('--max_num_samples', type=int, default=-1,
help='maximum number of samples to explain, for fast testing. Use all if -1')
'''
GNN args
'''
parser.add_argument('--emb_dim', type=int, default=128)
parser.add_argument('--hidden_dim', type=int, default=128)
parser.add_argument('--out_dim', type=int, default=128)
parser.add_argument('--saved_model_dir', type=str, default='saved_models')
parser.add_argument('--saved_model_name', type=str, default='')
'''
Link predictor args
'''
parser.add_argument('--src_ntype', type=str, default='user', help='prediction source node type')
parser.add_argument('--tgt_ntype', type=str, default='item', help='prediction target node type')
parser.add_argument('--pred_etype', type=str, default='likes', help='prediction edge type')
parser.add_argument('--link_pred_op', type=str, default='dot', choices=['dot', 'cos', 'ele', 'cat'],
help='operation passed to dgl.EdgePredictor')
'''
Explanation args
'''
parser.add_argument('--lr', type=float, default=0.01, help='explainer learning_rate')
parser.add_argument('--alpha', type=float, default=1.0, help='explainer on-path edge regularizer weight')
parser.add_argument('--beta', type=float, default=1.0, help='explainer off-path edge regularizer weight')
parser.add_argument('--num_hops', type=int, default=2, help='computation graph number of hops')
parser.add_argument('--num_epochs', type=int, default=20, help='How many epochs to learn the mask')
parser.add_argument('--num_paths', type=int, default=40, help='How many paths to generate')
parser.add_argument('--max_path_length', type=int, default=5, help='max lenght of generated paths')
parser.add_argument('--k_core', type=int, default=2, help='k for the k-core graph')
parser.add_argument('--prune_max_degree', type=int, default=200,
help='prune the graph such that all nodes have degree smaller than max_degree. No prune if -1')
parser.add_argument('--save_explanation', default=False, action='store_true',
help='Whether to save the explanation')
parser.add_argument('--saved_explanation_dir', type=str, default='saved_explanations',
help='directory of saved explanations')
parser.add_argument('--config_path', type=str, default='', help='path of saved configuration args')
args = parser.parse_args()
if args.config_path:
args = set_config_args(args, args.config_path, args.dataset_name, 'pagelink')
if 'citation' in args.dataset_name:
args.src_ntype = 'author'
args.tgt_ntype = 'paper'
elif 'synthetic' in args.dataset_name:
args.src_ntype = 'user'
args.tgt_ntype = 'item'
if torch.cuda.is_available() and args.device_id >= 0:
device = torch.device('cuda', index=args.device_id)
else:
device = torch.device('cpu')
if args.link_pred_op in ['cat']:
pred_kwargs = {"in_feats": args.out_dim, "out_feats": 1}
else:
pred_kwargs = {}
if not args.saved_model_name:
args.saved_model_name = f'{args.dataset_name}_model'
print_args(args)
set_seed(0)
processed_g = load_dataset(args.dataset_dir, args.dataset_name, args.valid_ratio, args.test_ratio)[1]
mp_g, train_pos_g, train_neg_g, val_pos_g, val_neg_g, test_pos_g, test_neg_g = [g.to(device) for g in processed_g]
encoder = HeteroRGCN(mp_g, args.emb_dim, args.hidden_dim, args.out_dim)
model = HeteroLinkPredictionModel(encoder, args.src_ntype, args.tgt_ntype, args.link_pred_op, **pred_kwargs)
state = torch.load(f'{args.saved_model_dir}/{args.saved_model_name}.pth', map_location='cpu')
model.load_state_dict(state)
pagelink = PaGELink(model,
lr=args.lr,
alpha=args.alpha,
beta=args.beta,
num_epochs=args.num_epochs,
log=True).to(device)
test_src_nids, test_tgt_nids = test_pos_g.edges()
test_ids = range(test_src_nids.shape[0])
if args.max_num_samples > 0:
test_ids = test_ids[:args.max_num_samples]
pred_edge_to_comp_g_edge_mask = {}
pred_edge_to_paths = {}
for i in tqdm(test_ids):
src_nid, tgt_nid = test_src_nids[i].unsqueeze(0), test_tgt_nids[i].unsqueeze(0)
with torch.no_grad():
pred = model(src_nid, tgt_nid, mp_g).sigmoid().item() > 0.5
if pred:
src_tgt = ((args.src_ntype, int(src_nid)), (args.tgt_ntype, int(tgt_nid)))
paths, comp_g_edge_mask_dict = pagelink.explain(src_nid,
tgt_nid,
mp_g,
args.num_hops,
args.prune_max_degree,
args.k_core,
args.num_paths,
args.max_path_length,
return_mask=True)
pred_edge_to_comp_g_edge_mask[src_tgt] = comp_g_edge_mask_dict
pred_edge_to_paths[src_tgt] = paths
if args.save_explanation:
if not os.path.exists(args.saved_explanation_dir):
os.makedirs(args.saved_explanation_dir)
saved_edge_explanation_file = f'pagelink_{args.saved_model_name}_pred_edge_to_comp_g_edge_mask'
saved_path_explanation_file = f'pagelink_{args.saved_model_name}_pred_edge_to_paths'
pred_edge_to_comp_g_edge_mask = {edge: {k: v.cpu() for k, v in mask.items()} for edge, mask in pred_edge_to_comp_g_edge_mask.items()}
saved_edge_explanation_path = Path.cwd().joinpath(args.saved_explanation_dir, saved_edge_explanation_file)
with open(saved_edge_explanation_path, "wb") as f:
pickle.dump(pred_edge_to_comp_g_edge_mask, f)
saved_path_explanation_path = Path.cwd().joinpath(args.saved_explanation_dir, saved_path_explanation_file)
with open(saved_path_explanation_path, "wb") as f:
pickle.dump(pred_edge_to_paths, f)