def read_triplet_data(folder):
filenames = ["train2id.txt", "valid2id.txt", "test2id.txt"]
count = 0
edge_index = []
edge_attr = []
count_list = []
for filename in filenames:
with open(osp.join(folder, filename), "r") as f:
num = int(f.readline().strip())
for line in f:
items = line.strip().split()
edge_index.append([int(items[0]), int(items[1])])
edge_attr.append(int(items[2]))
count += 1
count_list.append(count)
edge_index = torch.LongTensor(edge_index).t()
edge_attr = torch.LongTensor(edge_attr)
data = Data()
data.edge_index = edge_index
data.edge_attr = edge_attr
def generate_mask(start, end):
mask = torch.BoolTensor(count)
mask[:] = False
mask[start:end] = True
return mask
data.train_mask = generate_mask(0, count_list[0])
data.val_mask = generate_mask(count_list[0], count_list[1])
data.test_mask = generate_mask(count_list[1], count_list[2])
return data
def read_triplet_data(folder):
filenames = ["train2id.txt", "valid2id.txt", "test2id.txt"]
count = 0
edge_index = []
edge_attr = []
count_list = []
triples = []
num_entities = 0
num_relations = 0
entity_dic = {}
relation_dic = {}
for filename in filenames:
with open(osp.join(folder, filename), "r") as f:
_ = int(f.readline().strip())
if "train" in filename:
train_start_idx = len(triples)
elif "valid" in filename:
valid_start_idx = len(triples)
elif "test" in filename:
test_start_idx = len(triples)
for line in f:
items = line.strip().split()
edge_index.append([int(items[0]), int(items[1])])
edge_attr.append(int(items[2]))
triples.append((int(items[0]), int(items[2]), int(items[1])))
if items[0] not in entity_dic:
entity_dic[items[0]] = num_entities
num_entities += 1
if items[1] not in entity_dic:
entity_dic[items[1]] = num_entities
num_entities += 1
if items[2] not in relation_dic:
relation_dic[items[2]] = num_relations
num_relations += 1
count += 1
count_list.append(count)
edge_index = torch.LongTensor(edge_index).t()
edge_attr = torch.LongTensor(edge_attr)
data = Data()
data.edge_index = edge_index
data.edge_attr = edge_attr
def generate_mask(start, end):
mask = torch.BoolTensor(count)
mask[:] = False
mask[start:end] = True
return mask
data.train_mask = generate_mask(0, count_list[0])
data.val_mask = generate_mask(count_list[0], count_list[1])
data.test_mask = generate_mask(count_list[1], count_list[2])
return data, triples, train_start_idx, valid_start_idx, test_start_idx, num_entities, num_relations
