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train_ssrotate3D_WN18RR.py
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import openke
from openke.config import Trainer, Tester
from openke.module.model import SSRotate3D
from openke.module.loss import SigmoidLoss
from openke.module.strategy import NegativeSampling
from openke.data import TrainDataLoader, TestDataLoader, FixedDataLoader, FixedTestDataLoader
import openke
from openke.config import Trainer, Tester, TesterForRetrievalTest
import pdb
import torch
import os.path as osp
import argparse
def main(args):
# dataloader for training
train_dataloader = TrainDataLoader(
in_path = "./benchmarks/WN18RR/",
batch_size = 500, #2000
threads = 8,
sampling_mode = "normal",
bern_flag = 0,
filter_flag = 1,
neg_ent = 64,
neg_rel = 0
)
# define the model
rotate = SSRotate3D(
ent_tot = train_dataloader.get_ent_tot(),
rel_tot = train_dataloader.get_rel_tot(),
dim = 1024,
margin = 6.0,
epsilon = 2.0,
tolerance=0.1
)
# define the loss function
model = NegativeSampling(
model = rotate,
loss = SigmoidLoss(adv_temperature = 2),
batch_size = train_dataloader.get_batch_size(),
regul_rate = 0.0
)
if not args.test:
# train the model
trainer = Trainer(model = model, data_loader = train_dataloader, train_times = 300, alpha = 2e-5, use_gpu = True, opt_method = "adam")
trainer.run()
rotate.save_checkpoint('./checkpoint/SSRotate3D_WN18RR_epoch300.ckpt')
dataset_name = 'WN18RR'
# test the model
rotate.load_checkpoint('./checkpoint/SSRotate3D_WN18RR_epoch300.ckpt')
# get number of entities
entity2id_file = open("./benchmarks/" + dataset_name + "/entity2id.txt", 'r', encoding='utf-8')
nentities = (int)(entity2id_file.readline())
entity2id_file.close()
specific_test_dataloader_all = FixedTestDataLoader("./benchmarks/" + dataset_name + "/test2id.txt", "link", nentities)
specific_test_dataloader_11 = FixedTestDataLoader("./benchmarks/" + dataset_name + "/1-1.txt", "link", nentities)
specific_test_dataloader_1n = FixedTestDataLoader("./benchmarks/" + dataset_name + "/1-n.txt", "link", nentities)
specific_test_dataloader_n1 = FixedTestDataLoader("./benchmarks/" + dataset_name + "/n-1.txt", "link", nentities)
specific_test_dataloader_nn = FixedTestDataLoader("./benchmarks/" + dataset_name + "/n-n.txt", "link", nentities)
# load the dataset
actual_head = {} # actual_head[(r, t)] is the actual head of ?->r->t
actual_tail = {} # actual_tail[(h, r)] is the actual tail of h->r->?
relation2id = open(osp.join("benchmarks", dataset_name, "relation2id.txt"), "r",encoding='utf-8')
num_relations = int(relation2id.readline().strip())
entity2id = open(osp.join("benchmarks", dataset_name, "entity2id.txt"), "r",encoding='utf-8')
num_entities = int(entity2id.readline().strip())
relation2id.close()
entity2id.close()
triple = open(osp.join("benchmarks", dataset_name, "train2id.txt"), "r",encoding='utf-8')
tot = (int)(triple.readline())
for i in range(tot):
content = triple.readline()
h, t, r = content.strip().split()
h = int(h)
t = int(t)
r = int(r)
if not (h, r) in actual_tail:
actual_tail[(h, r)] = []
if not (r, t) in actual_head:
actual_head[(r, t)] = []
actual_tail[(h, r)].append(t)
actual_head[(r, t)].append(h)
triple.close()
valid = open(osp.join("benchmarks", dataset_name, "valid2id.txt"), "r",encoding='utf-8')
tot = (int)(valid.readline())
for i in range(tot):
content = valid.readline()
h, t, r = content.strip().split()
h = int(h)
t = int(t)
r = int(r)
if not (h, r) in actual_tail:
actual_tail[(h, r)] = []
if not (r, t) in actual_head:
actual_head[(r, t)] = []
actual_tail[(h, r)].append(t)
actual_head[(r, t)].append(h)
valid.close()
test = open(osp.join("benchmarks", dataset_name, "test2id.txt"), "r",encoding='utf-8')
tot = (int)(test.readline())
for i in range(tot):
content = test.readline()
h, t, r = content.strip().split()
h = int(h)
t = int(t)
r = int(r)
if not (h, r) in actual_tail:
actual_tail[(h, r)] = []
if not (r, t) in actual_head:
actual_head[(r, t)] = []
actual_tail[(h, r)].append(t)
actual_head[(r, t)].append(h)
test.close()
tester_all = TesterForRetrievalTest(model = rotate, data_loader = specific_test_dataloader_all, use_gpu = True, actual_head = actual_head, actual_tail = actual_tail)
tester_11 = TesterForRetrievalTest(model = rotate, data_loader = specific_test_dataloader_11, use_gpu = True, actual_head = actual_head, actual_tail = actual_tail)
tester_1n = TesterForRetrievalTest(model = rotate, data_loader = specific_test_dataloader_1n, use_gpu = True, actual_head = actual_head, actual_tail = actual_tail)
tester_n1 = TesterForRetrievalTest(model = rotate, data_loader = specific_test_dataloader_n1, use_gpu = True, actual_head = actual_head, actual_tail = actual_tail)
tester_nn = TesterForRetrievalTest(model = rotate, data_loader = specific_test_dataloader_nn, use_gpu = True, actual_head = actual_head, actual_tail = actual_tail)
print("Test on all relationship")
tester_all.run_link_prediction(type_constrain = False)
print("Test on 1-1 relationship")
tester_11.run_link_prediction(type_constrain = False)
print("Test on 1-n relationship")
tester_1n.run_link_prediction(type_constrain = False)
print("Test on n-1 relationship")
tester_n1.run_link_prediction(type_constrain = False)
print("Test on n-n relationship")
tester_nn.run_link_prediction(type_constrain = False)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--test', action='store_true')
args = parser.parse_args()
main(args)