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creatData.py
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from dgl.data import TUDataset
from utils import *
import networkx as nx
def creat_dataset(dataset_name, min_a, max_b, num_of_example, save_path, label_or_attr):
r"""
dataset_name 字符串, min_a 子图最小点, max_b 子图最大点, num_of_example 生成样本数, save_path 字符串, label_or_attr 字符串
"""
nm = iso.numerical_node_match('x', 1.0)
data = TUDataset(dataset_name)
i = 0
while i < num_of_example:
# print(i)
for j in np.arange(len(data)):
print('第几个', i, 'wwwwwwwwwwwwwwwwwwwww')
g, label = data[j]
nxg = dgl.to_networkx(g)
if nx.is_connected(nxg.to_undirected()) is not True:
continue
da_dgl = dgl.from_networkx(nxg)
da_dgl.ndata['x'] = g.ndata[label_or_attr]
q_size = random.randint(min_a, max_b)
print(q_size)
kaiguan = True
while kaiguan:
seed = random.randint(0, da_dgl.num_nodes() - 1)
print('seed: ', seed)
q_dgl, lllable = sg(seed=seed, datu=da_dgl, size=q_size)
nx_q = dgl.to_networkx(q_dgl)
if nx.is_connected(nx_q.to_undirected()) is not True: # 检查生成的子图 是否连通
print('错误错误！！！！！！！生成子图不连通')
# 检查是否唯一子图
nx_da = dgl.to_networkx(da_dgl, node_attrs=['x'])
nx_q = dgl.to_networkx(q_dgl, node_attrs=['x'])
mm = DiGraphMatcher(G1=nx_da, G2=nx_q, node_match=nm)
aa = list(mm.subgraph_isomorphisms_iter())
print(aa)
if len(aa) == 1:
da_dgl.ndata['x'] = torch.tensor(da_dgl.ndata['x'], dtype=torch.float32)
q_dgl.ndata['x'] = torch.tensor(q_dgl.ndata['x'], dtype=torch.float32)
matching_matrix = to_m(label=lllable, q_size=q_dgl.num_nodes(), g_size=da_dgl.num_nodes())
graph_labels = {'glabel': torch.tensor(matching_matrix, dtype=torch.float32)}
path = save_path + str(i) + '.bin'
save_graphs(path, [da_dgl, q_dgl], graph_labels)
i = i + 1
kaiguan = False
if i == num_of_example:
break
def general_creat_dataset(dataset_name, min_a, max_b, num_of_example, save_path, label_or_attr):
r"""
dataset_name 字符串, min_a 子图最小点, max_b 子图最大点, num_of_example 生成样本数, save_path 字符串, label_or_attr 字符串
"""
nm = iso.numerical_node_match('x', 1.0)
data = TUDataset(dataset_name)
i = 0
while i < num_of_example:
# print(i)
for j in np.arange(len(data)):
print('第几个', i, 'wwwwwwwwwwwwwwwwwwwww')
g, label = data[j]
nxg = dgl.to_networkx(g)
if nx.is_connected(nxg.to_undirected()) is not True:
continue
da_dgl = dgl.from_networkx(nxg)
da_dgl.ndata['x'] = g.ndata[label_or_attr]
q_size = random.randint(min_a, max_b)
print(q_size)
seed = random.randint(0, da_dgl.num_nodes() - 1)
print('seed: ', seed)
q_dgl, lllable = sg(seed=seed, datu=da_dgl, size=q_size)
nx_q = dgl.to_networkx(q_dgl)
if nx.is_connected(nx_q.to_undirected()) is not True: # 检查生成的子图 是否连通
print('错误错误！！！！！！！生成子图不连通')
# 检查是否唯一子图
nx_da = dgl.to_networkx(da_dgl, node_attrs=['x'])
nx_q = dgl.to_networkx(q_dgl, node_attrs=['x'])
mm = DiGraphMatcher(G1=nx_da, G2=nx_q, node_match=nm)
aa = list(mm.subgraph_isomorphisms_iter())
print(aa)
print('有多少个子图：', len(aa))
matching_matrix = new_label(aa=aa, q_size=q_size, g_size=da_dgl.num_nodes())
graph_labels = {'glabel': torch.tensor(matching_matrix, dtype=torch.float32)}
path = save_path + str(i) + '.bin'
save_graphs(path, [da_dgl, q_dgl], graph_labels)
i = i + 1
if i == num_of_example:
break
dataset_name = 'SYNTHETIC'
min_a = 25
max_b = 40
num_of_example = 10000
save_path = './SYNTHETIC/'
label_or_attr = 'node_labels'
general_creat_dataset(dataset_name, min_a, max_b, num_of_example, save_path, label_or_attr)