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prep_dataset.py
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prep_dataset.py
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from sklearn.model_selection import train_test_split
dataset = 'flickr'
sparse_node_threshold = 5
def read_flickr_graph():
node_dict = dict()
graph_dict = dict()
class_dict = dict()
node_class = dict()
node_index = 1
with open('./dataset/' + dataset + '/nodes.csv', "r") as fr:
lines = fr.readlines()
for line in lines:
temp = list(line.strip('\n').split(' '))
node_dict[temp[0]] = node_index
node_index += 1
with open('./dataset/' + dataset + '/nodes.csv', "r") as fr:
lines = fr.readlines()
for line in lines:
temp = list(line.strip('\n').split(' '))
if node_dict[temp[0]] not in graph_dict.keys():
graph_dict[node_dict[temp[0]]] = set()
edge_num = 0
with open('./dataset/' + dataset + '/edges.csv', "r") as fr:
lines = fr.readlines()
for line in lines:
edge_num += 1
temp = list(line.strip('\n').split(','))
graph_dict[node_dict[temp[0]]].add(node_dict[temp[1]])
graph_dict[node_dict[temp[1]]].add(node_dict[temp[0]])
class_index = 0
with open('./dataset/' + dataset + '/groups.csv', "r") as fr:
lines = fr.readlines()
for line in lines:
temp = list(line.strip('\n').split(' '))
if temp[0] not in class_dict.keys():
class_dict[temp[0]] = class_index
class_index += 1
with open('./dataset/' + dataset + '/group-edges.csv', "r") as fr:
lines = fr.readlines()
for line in lines:
temp = list(line.strip('\n').split(','))
if node_dict[temp[0]] not in node_class.keys():
node_class[node_dict[temp[0]]] = set()
node_class[node_dict[temp[0]]].add(class_dict[temp[1]])
else:
node_class[node_dict[temp[0]]].add(class_dict[temp[1]])
print('dataset ' + dataset)
print('total node number: %d' % len(graph_dict.keys()))
print('total edge number: %d' % edge_num)
print('total class number: %d' % len(class_dict.keys()))
return node_dict, graph_dict, node_class, class_dict
def read_email_graph():
node_dict = dict()
graph_dict = dict()
node_class = dict()
classset = set()
node_index = 1
with open('./dataset/' + dataset + '/edges.txt', "r") as fr:
lines = fr.readlines()
for line in lines:
temp = list(line.strip('\n').split(' '))
if temp[0] not in node_dict.keys():
node_dict[temp[0]] = node_index
node_index += 1
if temp[1] not in node_dict.keys():
node_dict[temp[1]] = node_index
node_index += 1
# reverse_node_dict = dict(zip(node_dict.values(), node_dict.keys()))
# with open('./dataset/' + dataset + '/node_dict.txt', "w") as fw:
# for n in node_dict:
# fw.write(str(n) + ' ' + str(node_dict) + '\n')
for n in node_dict.keys():
graph_dict[node_dict[n]] = set()
edge_num = 0
with open('./dataset/' + dataset + '/edges.txt', "r") as fr:
lines = fr.readlines()
for line in lines:
edge_num += 1
temp = list(line.strip('\n').split(' '))
graph_dict[node_dict[temp[0]]].add(node_dict[temp[1]])
graph_dict[node_dict[temp[1]]].add(node_dict[temp[0]])
with open('./dataset/' + dataset + '/labels.txt', "r") as fr:
lines = fr.readlines()
for line in lines:
temp = list(line.strip('\n').split(' '))
if temp[0] in node_dict.keys():
node_class[node_dict[temp[0]]] = temp[1]
classset.add(temp[1])
print(len(classset))
print('dataset ' + dataset)
print('total node number: %d' % len(graph_dict.keys()))
print('total edge number: %d' % edge_num)
return node_dict, graph_dict, node_class
def read_wiki_graph():
node_dict = dict()
graph_dict = dict()
node_class = dict()
node_index = 1
with open('./dataset/' + dataset + '/Wiki_edgelist.txt', "r") as fr:
lines = fr.readlines()
for line in lines:
temp = list(line.strip('\n').split('\t'))
if temp[0] not in node_dict.keys():
node_dict[temp[0]] = node_index
node_index += 1
if temp[1] not in node_dict.keys():
node_dict[temp[1]] = node_index
node_index += 1
# reverse_node_dict = dict(zip(node_dict.values(), node_dict.keys()))
# with open('./dataset/' + dataset + '/node_dict.txt', "w") as fw:
# for n in node_dict:
# fw.write(str(n) + ' ' + str(node_dict) + '\n')
for n in node_dict.keys():
graph_dict[node_dict[n]] = set()
edge_num = 0
with open('./dataset/' + dataset + '/Wiki_edgelist.txt', "r") as fr:
lines = fr.readlines()
for line in lines:
edge_num += 1
temp = list(line.strip('\n').split('\t'))
graph_dict[node_dict[temp[0]]].add(node_dict[temp[1]])
graph_dict[node_dict[temp[1]]].add(node_dict[temp[0]])
with open('./dataset/' + dataset + '/Wiki_category.txt', "r") as fr:
lines = fr.readlines()
for line in lines:
temp = list(line.strip('\n').split('\t'))
if temp[0] in node_dict.keys():
node_class[node_dict[temp[0]]] = temp[1]
print('dataset ' + dataset)
print('total node number: %d' % len(graph_dict.keys()))
print('total edge number: %d' % edge_num)
return node_dict, graph_dict, node_class
def select_sparse_node(graph_dict):
sparse_node_set = set()
for i in graph_dict.keys():
if len(graph_dict[i]) <= sparse_node_threshold:
sparse_node_set.add(i)
return sparse_node_set
def node_type_info(graph_dict, sparse_node_set):
node_type_dict = dict()
for n in graph_dict.keys():
if n in sparse_node_set:
node_type_dict[n] = 'sparse'
else:
neighbor = len(graph_dict[n])
for adj in graph_dict[n]:
if adj in sparse_node_set:
neighbor -= 1
if neighbor > sparse_node_threshold:
node_type_dict[n] = 'dense'
else:
node_type_dict[n] = 'middle'
s_num = 0
d_num = 0
m_num = 0
for n in node_type_dict.keys():
if node_type_dict[n] == 'sparse':
s_num += 1
if node_type_dict[n] == 'dense':
d_num += 1
if node_type_dict[n] == 'middle':
m_num += 1
return node_type_dict
def generate_train_test_candidate_flickr(graph_dict, sparse_node_set, node_type_dict):
train_node_candidate_set = set()
test_node_candidate_set_all = set()
test_node_candidate_set_1 = set()
test_node_candidate_set_2 = set()
test_node_candidate_set_3 = set()
test_node_candidate_set_4 = set()
test_node_candidate_set_5 = set()
test_nodes_list = list()
test_node_candidate_num = 0
for n in sparse_node_set:
dense_num = 0
middle_num = 0
sparse_num = 0
for adj in graph_dict[n]:
if node_type_dict[adj] == 'dense':
dense_num += 1
elif node_type_dict[adj] == 'middle':
middle_num += 1
else:
sparse_num += 1
if middle_num == 0 and sparse_num == 0:
test_node_candidate_set_all.add(n)
if len(graph_dict[n]) == 1:
test_node_candidate_set_1.add(n)
if len(graph_dict[n]) == 2:
test_node_candidate_set_2.add(n)
if len(graph_dict[n]) == 3:
test_node_candidate_set_3.add(n)
if len(graph_dict[n]) == 4:
test_node_candidate_set_4.add(n)
if len(graph_dict[n]) == 5:
test_node_candidate_set_5.add(n)
test_node_candidate_num += 1
print(len(test_node_candidate_set_1))
print(len(test_node_candidate_set_2))
print(len(test_node_candidate_set_3))
print(len(test_node_candidate_set_4))
print(len(test_node_candidate_set_5))
print(test_node_candidate_num)
for n in graph_dict.keys():
if n in sparse_node_set:
continue
else:
if node_type_dict[n] == 'middle':
continue
else:
neighbor = len(graph_dict[n])
for adj in graph_dict[n]:
if adj in sparse_node_set:
neighbor -= 1
if 36 < neighbor <= 56:
train_node_candidate_set.add(n)
# flickr do not have 1 shot node
# test_nodes_list_1, _, _, _ = train_test_split(list(test_node_candidate_set_1),
# range(len(test_node_candidate_set_1)), train_size=157,
# random_state=19)
# test_nodes_list.extend(test_nodes_list_1)
test_nodes_list_2, _, _, _ = train_test_split(list(test_node_candidate_set_2),
range(len(test_node_candidate_set_2)), train_size=320,
random_state=19)
test_nodes_list.extend(test_nodes_list_2)
test_nodes_list_3, _, _, _ = train_test_split(list(test_node_candidate_set_3),
range(len(test_node_candidate_set_3)), train_size=260,
random_state=19)
test_nodes_list.extend(test_nodes_list_3)
test_nodes_list_4, _, _, _ = train_test_split(list(test_node_candidate_set_4),
range(len(test_node_candidate_set_4)), train_size=227,
random_state=19)
test_nodes_list.extend(test_nodes_list_4)
test_nodes_list_5, _, _, _ = train_test_split(list(test_node_candidate_set_5),
range(len(test_node_candidate_set_5)), train_size=193,
random_state=19)
test_nodes_list.extend(test_nodes_list_5)
test_node_candidate_set = set(test_node_candidate_set_all)
return train_node_candidate_set, test_node_candidate_set
def generate_train_test_candidate_email(graph_dict, sparse_node_set, node_type_dict):
test_node_candidate_num = 0
test_node_candidate_set_all = set()
train_node_candidate_set = set()
test_node_candidate_set_1 = set()
test_node_candidate_set_2 = set()
test_node_candidate_set_3 = set()
test_node_candidate_set_4 = set()
test_node_candidate_set_5 = set()
test_nodes_list = list()
for n in sparse_node_set:
dense_num = 0
middle_num = 0
sparse_num = 0
for adj in graph_dict[n]:
if node_type_dict[adj] == 'dense':
dense_num += 1
elif node_type_dict[adj] == 'middle':
middle_num += 1
else:
sparse_num += 1
if middle_num == 0 and sparse_num == 0:
test_node_candidate_set_all.add(n)
if len(graph_dict[n]) == 1:
test_node_candidate_set_1.add(n)
if len(graph_dict[n]) == 2:
test_node_candidate_set_2.add(n)
if len(graph_dict[n]) == 3:
test_node_candidate_set_3.add(n)
if len(graph_dict[n]) == 4:
test_node_candidate_set_4.add(n)
if len(graph_dict[n]) == 5:
test_node_candidate_set_5.add(n)
test_node_candidate_num += 1
print(test_node_candidate_num)
print(len(test_node_candidate_set_1))
print(len(test_node_candidate_set_2))
print(len(test_node_candidate_set_3))
print(len(test_node_candidate_set_4))
print(len(test_node_candidate_set_5))
for n in graph_dict.keys():
if n in sparse_node_set:
continue
else:
if node_type_dict[n] == 'middle':
continue
else:
neighbor = len(graph_dict[n])
for adj in graph_dict[n]:
if adj in sparse_node_set:
neighbor -= 1
if 12 < neighbor <= 32:
train_node_candidate_set.add(n)
test_nodes_list_1, _, _, _ = train_test_split(list(test_node_candidate_set_1),
range(len(test_node_candidate_set_1)), train_size=43,
random_state=19)
test_nodes_list.extend(test_nodes_list_1)
test_nodes_list_2, _, _, _ = train_test_split(list(test_node_candidate_set_2),
range(len(test_node_candidate_set_2)), train_size=17,
random_state=19)
test_nodes_list.extend(test_nodes_list_2)
test_nodes_list_3, _, _, _ = train_test_split(list(test_node_candidate_set_3),
range(len(test_node_candidate_set_3)), train_size=15,
random_state=19)
test_nodes_list.extend(test_nodes_list_3)
test_nodes_list_4, _, _, _ = train_test_split(list(test_node_candidate_set_4),
range(len(test_node_candidate_set_4)), train_size=11,
random_state=19)
test_nodes_list.extend(test_nodes_list_4)
test_nodes_list_5, _, _, _ = train_test_split(list(test_node_candidate_set_5),
range(len(test_node_candidate_set_5)), train_size=14,
random_state=19)
test_nodes_list.extend(test_nodes_list_5)
test_node_candidate_set = set(test_nodes_list)
print('train node candidate num is %d' % len(train_node_candidate_set))
print('test node candidate num is %d' % len(test_node_candidate_set))
print('=================================')
return train_node_candidate_set, test_node_candidate_set
def generate_train_test_candidate_wiki(graph_dict, sparse_node_set, node_type_dict):
test_node_candidate_num = 0
test_node_candidate_set_all = set()
train_node_candidate_set = set()
test_node_candidate_set_1 = set()
test_node_candidate_set_2 = set()
test_node_candidate_set_3 = set()
test_node_candidate_set_4 = set()
test_node_candidate_set_5 = set()
test_nodes_list = list()
for n in sparse_node_set:
dense_num = 0
middle_num = 0
sparse_num = 0
for adj in graph_dict[n]:
if node_type_dict[adj] == 'dense':
dense_num += 1
elif node_type_dict[adj] == 'middle':
middle_num += 1
else:
sparse_num += 1
if middle_num == 0 and sparse_num == 0:
test_node_candidate_set_all.add(n)
if len(graph_dict[n]) == 1:
test_node_candidate_set_1.add(n)
if len(graph_dict[n]) == 2:
test_node_candidate_set_2.add(n)
if len(graph_dict[n]) == 3:
test_node_candidate_set_3.add(n)
if len(graph_dict[n]) == 4:
test_node_candidate_set_4.add(n)
if len(graph_dict[n]) == 5:
test_node_candidate_set_5.add(n)
test_node_candidate_num += 1
print(test_node_candidate_num)
print(len(test_node_candidate_set_1))
print(len(test_node_candidate_set_2))
print(len(test_node_candidate_set_3))
print(len(test_node_candidate_set_4))
print(len(test_node_candidate_set_5))
for n in graph_dict.keys():
if n in sparse_node_set:
continue
else:
if node_type_dict[n] == 'middle':
continue
else:
neighbor = len(graph_dict[n])
for adj in graph_dict[n]:
if adj in sparse_node_set:
neighbor -= 1
if 7 < neighbor <= 20:
train_node_candidate_set.add(n)
test_nodes_list_1, _, _, _ = train_test_split(list(test_node_candidate_set_1),
range(len(test_node_candidate_set_1)), train_size=30,
random_state=19)
test_nodes_list.extend(test_nodes_list_1)
test_nodes_list_2, _, _, _ = train_test_split(list(test_node_candidate_set_2),
range(len(test_node_candidate_set_2)), train_size=24,
random_state=19)
test_nodes_list.extend(test_nodes_list_2)
test_nodes_list_3, _, _, _ = train_test_split(list(test_node_candidate_set_3),
range(len(test_node_candidate_set_3)), train_size=20,
random_state=19)
test_nodes_list.extend(test_nodes_list_3)
test_nodes_list_4, _, _, _ = train_test_split(list(test_node_candidate_set_4),
range(len(test_node_candidate_set_4)), train_size=13,
random_state=19)
test_nodes_list.extend(test_nodes_list_4)
test_nodes_list_5, _, _, _ = train_test_split(list(test_node_candidate_set_5),
range(len(test_node_candidate_set_5)), train_size=13,
random_state=19)
test_nodes_list.extend(test_nodes_list_5)
test_node_candidate_set = set(test_nodes_list)
print('train node candidate num is %d' % len(train_node_candidate_set))
print('test node candidate num is %d' % len(test_node_candidate_set))
print('=================================')
return train_node_candidate_set, test_node_candidate_set
def generate_train_test_set(train_node_candidate_set, test_node_candidate_set):
train_node_set = train_node_candidate_set
return train_node_set, test_node_candidate_set
def save_graph(graph_dict, graph_dir):
with open(graph_dir, 'w') as fw:
for key in graph_dict.keys():
fw.write(str(key))
for item in graph_dict[key]:
fw.write(' ' + str(item))
fw.write('\n')
def save_node(node_set, node_save_dir):
with open(node_save_dir, 'w') as fw:
for n in node_set:
fw.write(str(n))
fw.write('\n')
def save_dense_graph(graph_dict, graph_dir, node_type_dict):
with open(graph_dir, 'w') as fw:
for key in graph_dict.keys():
fw.write(str(key))
for item in graph_dict[key]:
if node_type_dict[item] == 'dense':
fw.write(' ' + str(item))
fw.write('\n')
def save_node_class_info(node_info, save_dir):
with open(save_dir, 'w') as fw:
for key in node_info.keys():
fw.write(str(key))
for item in node_info[key]:
fw.write(' ' + str(item))
fw.write('\n')
def train_flickr():
node2id, graph, node_class_info, class2id = read_flickr_graph()
sparse_node = select_sparse_node(graph)
node_type = node_type_info(graph, sparse_node)
train_node_candidate, test_node_candidate = generate_train_test_candidate_flickr(graph, sparse_node, node_type)
train_node, test_node = generate_train_test_set(train_node_candidate, test_node_candidate)
save_node(train_node, './data/' + dataset + '/train.csv')
save_node(test_node, './data/' + dataset + '/test.csv')
save_node_class_info(node_class_info, './data/' + dataset + '/node_class.txt')
save_graph(graph, './data/' + dataset + '/graph.adjlist')
save_dense_graph(graph, './data/' + dataset + '/graph_dense.adjlist', node_type)
def train_email():
node2id, graph, node_class_info = read_email_graph()
sparse_node = select_sparse_node(graph)
node_type = node_type_info(graph, sparse_node)
train_node_candidate, test_node_candidate = generate_train_test_candidate_email(graph, sparse_node, node_type)
train_node, test_node = generate_train_test_set(train_node_candidate, test_node_candidate)
save_node(train_node, './data/' + dataset + '/train.csv')
save_node(test_node, './data/' + dataset + '/test.csv')
save_node_class_info(node_class_info, './data/' + dataset + '/node_class.txt')
save_graph(graph, './data/' + dataset + '/graph.adjlist')
save_dense_graph(graph, './data/' + dataset + '/graph_dense.adjlist', node_type)
def train_wiki():
node2id, graph, node_class_info = read_wiki_graph()
sparse_node = select_sparse_node(graph)
node_type = node_type_info(graph, sparse_node)
train_node_candidate, test_node_candidate = generate_train_test_candidate_wiki(graph, sparse_node, node_type)
train_node, test_node = generate_train_test_set(train_node_candidate, test_node_candidate)
save_node(train_node, './data/' + dataset + '/train.csv')
save_node(test_node, './data/' + dataset + '/test.csv')
save_node_class_info(node_class_info, './data/' + dataset + '/node_class.txt')
save_graph(graph, './data/' + dataset + '/graph.adjlist')
save_dense_graph(graph, './data/' + dataset + '/graph_dense.adjlist', node_type)
if __name__ == '__main__':
if dataset == 'flickr':
train_flickr()
elif dataset == 'email':
train_email()
else:
train_wiki()