1088c261
 tangwang
mv files
|
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
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
|
from tqdm import tqdm
import argparse
import multiprocessing
import logging
import os
def softmax(x, temperature=1.0):
"""
计算带有温度参数的softmax，并加入防止溢出的技巧
"""
x = np.array(x)
x_max = np.max(x)
exp_x = np.exp((x - x_max) / temperature) # 加入temperature参数
return exp_x / np.sum(exp_x)
class DeepWalk:
def __init__(self, edge_file, node_tag_file, use_softmax=True, temperature=1.0, p_tag_walk=0.5):
"""
初始化DeepWalk实例，构建图和标签索引，预处理alias采样表
"""
logging.info(f"Initializing DeepWalk with edge file: {edge_file} and node-tag file: {node_tag_file}")
self.graph = self.build_graph_from_edge_file(edge_file)
if node_tag_file:
self.node_to_tags, self.tag_to_nodes = self.build_tag_index(node_tag_file)
else:
self.node_to_tags = None
self.tag_to_nodes = None
self.alias_nodes = {}
self.p_tag_walk = p_tag_walk
logging.info(f"Graph built with {self.graph.number_of_nodes()} nodes and {self.graph.number_of_edges()} edges.")
if use_softmax:
logging.info(f"Using softmax with temperature: {temperature}")
self.preprocess_transition_probs__softmax(temperature)
else:
logging.info("Using standard alias sampling.")
self.preprocess_transition_probs()
def build_graph_from_edge_file(self, edge_file):
"""
从edge文件构建图
edge文件格式: bid1 \t bid2:weight1,bid2:weight2,...
"""
G = nx.Graph()
# 打开edge文件并读取内容
with open(edge_file, 'r') as f:
for line in f:
parts = line.strip().split('\t')
if len(parts) != 2:
continue
node, edges_str = parts
edges = edges_str.split(',')
for edge in edges:
nbr, weight = edge.split(':')
try:
node, nbr = int(node), int(nbr)
except ValueError:
continue
weight = float(weight)
# 检查图中是否已存在这条边
if G.has_edge(node, nbr):
# 如果已经有这条边，更新权重，累加新权重
G[node][nbr]['weight'] += weight
else:
# 如果没有这条边，直接添加
G.add_edge(node, nbr, weight=weight)
return G
def build_tag_index(self, node_tag_file):
"""
构建节点-标签的正排和倒排索引
node_tag_file格式: book_id \t tag1,tag2,tag3
"""
node_to_tags = {}
tag_to_nodes = {}
with open(node_tag_file, 'r') as f:
for line in f:
parts = line.strip().split('\t')
if len(parts) != 2:
continue
node, tags_str = parts
try:
node = int(node)
except ValueError:
continue
# 只保留有过用户行为的node
if not node in self.graph:
continue
tags = tags_str.split(',')
node_to_tags[node] = tags
for tag in tags:
tag_to_nodes.setdefault(tag, []).append(node)
return node_to_tags, tag_to_nodes
def preprocess_transition_probs(self):
"""
预处理节点的alias采样表，用于快速加权随机游走
"""
G = self.graph
for node in G.nodes():
unnormalized_probs = [G[node][nbr].get('weight', 1.0) for nbr in G.neighbors(node)]
norm_const = sum(unnormalized_probs)
normalized_probs = [float(u_prob) / norm_const for u_prob in unnormalized_probs]
self.alias_nodes[node] = create_alias_table(normalized_probs)
def preprocess_transition_probs__softmax(self, temperature=1.0):
"""
预处理节点的alias采样表，用于快速加权随机游走
"""
G = self.graph
for node in G.nodes():
unnormalized_probs = [G[node][nbr].get('weight', 1.0) for nbr in G.neighbors(node)]
normalized_probs = softmax(unnormalized_probs, temperature)
self.alias_nodes[node] = create_alias_table(normalized_probs)
def deepwalk_walk(self, walk_length, start_node):
"""
执行一次DeepWalk随机游走，基于alias方法加速，支持通过标签游走
"""
G = self.graph
alias_nodes = self.alias_nodes
walk = [start_node]
while len(walk) < walk_length:
cur = walk[-1]
# 根据p_tag_walk的概率决定是通过邻居游走还是通过tag游走
if self.node_to_tags and random.random() < self.p_tag_walk and cur in self.node_to_tags:
walk = self.tag_based_walk(cur, walk)
else:
walk = self.neighbor_based_walk(cur, alias_nodes, walk)
if not walk:
break
return walk
def neighbor_based_walk(self, cur, alias_nodes, walk):
"""
基于邻居的随机游走
"""
G = self.graph
cur_nbrs = list(G.neighbors(cur))
if len(cur_nbrs) > 0:
idx = alias_sample(alias_nodes[cur][0], alias_nodes[cur][1])
walk.append(cur_nbrs[idx])
else:
return None
return walk
def tag_based_walk(self, cur, walk):
"""
基于标签的随机游走
"""
tags = self.node_to_tags[cur]
if not tags:
return None
# 随机选择一个tag
chosen_tag = random.choice(tags)
# 获取该tag下的节点列表
nodes_with_tag = self.tag_to_nodes.get(chosen_tag, [])
if not nodes_with_tag:
return None
# 随机选择一个节点
chosen_node = random.choice(nodes_with_tag)
walk.append(chosen_node)
return walk
def simulate_walks(self, num_walks, walk_length, workers, output_file):
"""
多进程模拟多次随机游走，并将游走结果保存到文件
"""
G = self.graph
nodes = list(G.nodes())
num_walks_per_worker = max(1, num_walks // workers)
logging.info(f"Starting simulation with {num_walks_per_worker} walks per node, walk length {walk_length}, using {workers} workers.")
#
# results = Parallel(n_jobs=workers)(
# results = Parallel(n_jobs=workers, backend='multiprocessing')(
# results = Parallel(n_jobs=workers, backend='loky')(
results = Parallel(n_jobs=workers)(
delayed(self._simulate_walks)(nodes, num_walks_per_worker, walk_length)
for _ in range(workers)
)
walks = list(itertools.chain(*results))
# 保存游走结果到文件
self.save_walks_to_file(walks, output_file)
def _simulate_walks(self, nodes, num_walks, walk_length):
"""
模拟多次随机游走
"""
logging.info(f"_simulate_walks started, num_walks:{num_walks}, walk_length:{walk_length}")
walks = []
for i in range(num_walks):
logging.info(f"_simulate_walks run num_walks of {i}.")
random.shuffle(nodes)
for node in nodes:
walks.append(self.deepwalk_walk(walk_length=walk_length, start_node=node))
return walks
def save_walks_to_file(self, walks, output_file):
"""
将游走结果保存到文件，按Word2Vec的输入格式
"""
logging.info(f"Saving walks to file: {output_file}")
with open(output_file, 'w') as f:
for walk in walks:
walk_str = ' '.join(map(str, walk))
f.write(walk_str + '\n')
logging.info(f"Successfully saved {len(walks)} walks to {output_file}.")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run DeepWalk with tag-based random walks")
parser.add_argument('--edge-file', type=str, required=True, help="Path to the edge file") # ../../fetch_data/data/edge.txt.20240923
parser.add_argument('--node-tag-file', type=str, help="Path to the node-tag file")
parser.add_argument('--num-walks', type=int, default=100, help="Number of walks per node (default: 10)")
parser.add_argument('--walk-length', type=int, default=40, help="Length of each walk (default: 40)")
parser.add_argument('--workers', type=int, default=multiprocessing.cpu_count() - 1, help="Number of workers (default: CPU cores - 1)")
parser.add_argument('--use-softmax', action='store_true', help="Use softmax-based alias sampling (default: False)")
parser.add_argument('--temperature', type=float, default=1.0, help="Temperature for softmax (default: 1.0)")
parser.add_argument('--p-tag-walk', type=float, default=0.2, help="Probability to walk through tag-based neighbors (default: 0.5)")
parser.add_argument('--output-file', type=str, required=True, help="Path to save the walks file")
args = parser.parse_args()
# 初始化日志记录
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO)
# 初始化DeepWalk实例，传入边文件和节点标签文件
deepwalk = DeepWalk(
edge_file=args.edge_file,
node_tag_file=args.node_tag_file,
use_softmax=args.use_softmax,
temperature=args.temperature,
p_tag_walk=args.p_tag_walk
)
# 模拟随机游走并将结果保存到文件
deepwalk.simulate_walks(
num_walks=args.num_walks,
walk_length=args.walk_length,
workers=args.workers,
output_file=args.output_file
)
|