import networkx as nx
from openGraphMatching import NeuralMatcher
from openGraphMatching.utils import convert_graph
from openGraphMatching.utils import check_match_correctness
import sys
G = convert_graph('./dataset/validate/data_graph/HPRD.graph')
# q = convert_graph('./dataset/validate/query_graph/query_dense_16_6.graph')
q = convert_graph('./dataset/validate/query_graph/query_dense_16_1.graph')
neuralmatch = NeuralMatcher(G)
data = neuralmatch.is_subgraph_match(q)
matchlist = data[1]
f = open("matchlist.data", "w")
for i in matchlist:
f.write(str(i))
f.write('\n')
f.close()
flag = True
for m in matchlist:
if check_match_correctness(q, G, m) == False:
flag = False
if flag:
print('OK! It seems that every match is right')
else:
print('No!, i got something wrong')
import os.path as osp
import networkx as nx
import sys
import random
from openGraphMatching.utils import convert_graph, check_match_correctness, draw_graph
from openGraphMatching.matcher import GQLMatcher, CECIMatcher
dataset = 'validate'
path = osp.join(osp.dirname(osp.realpath(__file__)), '../dataset', dataset)
datasetpath = path + '/query_graph/'
G = convert_graph(path + '/data_graph/HPRD.graph')
of = open('generated_ans.res', 'w')
matcher = GQLMatcher(G)
query_prefix = 'query_dense_16_'
# sample = random.sample(range(1,201), 20)
# print(sample)
# for i in sample:
for i in range(1, 201):
query_name = query_prefix + str(i)
query_path = query_name + '.graph'
print(query_path)
q = convert_graph(datasetpath + query_path)
# data = matcher.is_subgraph_match(q)
imd1 = matcher.LDF(q)
imd2 = matcher.NLF(q, imd1)
# of.write(f'{query_path}:{len(data[1])}\n')
# print('Filtering rate is', data[0])
import os.path as osp
import networkx as nx
import sys
import random
from openGraphMatching.utils import convert_graph, check_match_correctness, draw_graph
from openGraphMatching.matcher import GQLMatcher, CECIMatcher
dataset = 'wordnet'
path = osp.join(osp.dirname(osp.realpath(__file__)), '../dataset', dataset)
datasetpath = path + '/query_graph/'
G = convert_graph(path + '/data_graph/wordnet.graph')
# of = open('generated_ans.res', 'w')
matcher = GQLMatcher(G)
query_prefix = 'query_dense_16_'
# sample = random.sample(range(1,201), 20)
# print(sample)
# for i in sample:
for i in range(1, 201):
query_name = query_prefix + str(i)
query_path = query_name + '.graph'
print(query_path)
q = convert_graph(datasetpath + query_path)
# data = matcher.is_subgraph_match(q)
imd1 = matcher.LDF(q)
# imd2 = matcher.NLF(q, imd1)
# of.write(f'{query_path}:{len(data[1])}\n')
# print('Filtering rate is', data[0])