def __init__(self, Graph):
self.reward_range = (0, 1)
# 环境获取的状态的维度128+404（邻接点和边被压缩成128表示，404维度是历史信息维度）
self.state_dim = NODE_DIM + EDGE_DIM
# 读取self.Graph = nx.read_gpickle("./data/DBLP_labeled.Graph")
# 或者直接获取返回值buildNetworkGraph.get_graph()
self.Graph = Graph
self.Graph = get_graph()
# 初始化属性，current_node,
self.current_node = None
self.path = []
self.target_node = None
self.query = None
self.qt = None
self.st = None
self.done = False
# 所有的树
self.TreeList = []
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Normal, Categorical
from torch.utils.data.sampler import BatchSampler, SubsetRandomSampler
from tensorboardX import SummaryWriter
from BiYeSheJi.Module.environment import Environment
from BiYeSheJi.Script.buildNetworkGraph import get_relation_num, get_graph
from BiYeSheJi.Module.state_encoder import StateEncoder
from BiYeSheJi.Script.get_embedding import get_node_emb_matrix, get_relation_emb_dic
from BiYeSheJi.Configuration import config
# Hyper-parameters
seed = 1
render = False
Graph = get_graph()
num_episodes = 400000
env = Environment(Graph)
num_state = env.state_dim
num_action = 128
torch.manual_seed(seed)
random.seed(seed)
Transition = namedtuple('Transition',
['state', 'action', 'reward', 'next_state'])
relation_dim = get_relation_num()
INPUT_SIZE_liner = relation_dim + 128
INPUT_SIZE_GRU = 3 * 128
OUT_SIZE_liner = 128
import sys
import math
import random
import torch
import numpy as np
from torch import nn
from BiYeSheJi.Script.buildNetworkGraph import get_graph
from BiYeSheJi.Script.get_embedding import get_node_emb_matrix, get_relation_emb_dic
from BiYeSheJi.Configuration import config
GRAPH = get_graph()
AVAILABLE_CHOICES = [1, -1, 2, -2]
AVAILABLE_CHOICE_NUMBER = len(AVAILABLE_CHOICES)
MAX_ROUND_NUMBER = 10
node_emb_matrix = get_node_emb_matrix()
edge_emb_dic = get_relation_emb_dic()
Graph = get_graph()
def _Max(matrix):
"""
取矩阵每一列最大值得到一行向量
:param matrix:
:return:
"""
return matrix[torch.argmax(matrix, dim=0),
torch.LongTensor(range(matrix.shape[1]))].view(
1, matrix.shape[1])
import json
import sys
import math
import random
import numpy as np
from BiYeSheJi.Script.buildNetworkGraph import get_graph
GRAPH = get_graph()
AVAILABLE_CHOICES = [1, -1, 2, -2]
AVAILABLE_CHOICE_NUMBER = len(AVAILABLE_CHOICES)
MAX_ROUND_NUMBER = 10
def select_best_child(node, is_exploration): # 若子节点都扩展完了，求UCB值最大的子节点
best_score = -sys.maxsize
best_sub_node = None
for sub_node in node.get_children():
if is_exploration:
# C = 1 / math.sqrt(2.0) # C越大越偏向于广度搜索，越小越偏向于深度搜索
C = 10
else:
C = 0.0
# UCT:
# left = sub_node.get_quality_value() / sub_node.get_visit_times()
# right = math.log(node.get_visit_times()) / sub_node.get_visit_times()
# score = left + C * math.sqrt(right)
# UCT--
left = sub_node.get_quality_value() / sub_node.get_visit_times()
right = math.sqrt(node.get_visit_times()) / sub_node.get_visit_times()
score = left + C * right
from BiYeSheJi.Script import buildNetworkGraph
from BiYeSheJi.Script.node2vec import Node2Vec
from BiYeSheJi.Configuration import config
import numpy as np
import json
entity_num = buildNetworkGraph.get_entity_num()
relation_num = buildNetworkGraph.get_relation_num()
entity_dic = buildNetworkGraph.get_entity_dic()
relation_dic = buildNetworkGraph.get_relation_dic()
GRAPH = buildNetworkGraph.get_graph()
def node_embedding():
if "node2vec" == config.node_encode:
node2vec = Node2Vec(GRAPH,
dimensions=config.node_encode_dim,
walk_length=20,
num_walks=20,
workers=4)
model = node2vec.fit(window=4, min_count=1, batch_words=4, workers=4)
# Look for most similar nodes
model.wv.most_similar('2') # Output node names are always strings
# Save embeddings for later use
model.wv.save_word2vec_format(config.node_encode_filename)
# Save model for later use
model.save(config.node_model_filename)
def edge_embedding():
import random
from BiYeSheJi.Module.environment import Environment
from BiYeSheJi.Script.buildNetworkGraph import get_graph
import torch
import json
random.seed(1)
start_node_id, target_node_id, query_id = 2, 3, 4
env = Environment(get_graph())
env.reset(start_node_id, target_node_id, query_id)
print(env.st)
for i in range(5):
action_node_id = random.choice(list(env.Graph[env.current_node].keys()))
env.step(action_node_id)
print(env.current_node)
print(env.path)
print(env.st)