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main_SimpleHHEA.py
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main_SimpleHHEA.py
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import os
import random
import argparse
import numpy as np
from tqdm import tqdm
import torch
from CSLS_ import eval_alignment_by_sim_mat
from model import Simple_HHEA
from utils import *
### load embeddings
def noise_name_emb(name_emb, noise_ratio, emb_size=64):
sample_list = [i for i in range(emb_size)]
mask_id = random.sample(sample_list, int(emb_size * noise_ratio))
name_emb[:, mask_id] = 0
return name_emb
def load_embeddings(data_path, add_noise, noise_ratio, use_structure=True, use_time=True):
ent_name_emb, ent_dw_emb, ent_time_emb = None, None, None
### load name embeddings
kg1_name_emb = np.loadtxt(os.path.join(data_path, "ent_1_emb_64.txt"))
kg2_name_emb = np.loadtxt(os.path.join(data_path, "ent_2_emb_64.txt"))
ent_name_emb = np.array(kg1_name_emb.tolist() + kg2_name_emb.tolist())
print(f"read entity name embedding shape: {ent_name_emb.shape}")
if add_noise:
ent_name_emb = noise_name_emb(ent_name_emb, noise_ratio)
### load structure embeddings
if use_structure:
ent_dw_emb = np.loadtxt(os.path.join(data_path, "deep_emb.txt"))
print(f"read entity deepwalk emb shape: {ent_dw_emb.shape}")
### load time embeddings
if use_time:
ent_time_emb = np.array(load_ent_time_matrix(data_path))
print(f"read entity time embedding shape: {ent_time_emb.shape}")
return ent_name_emb, ent_dw_emb, ent_time_emb
### training
def l1(ll, rr):
return torch.sum(torch.abs(ll - rr), axis=-1)
def evaluate(model, dev_alignments, hit_k=[1, 5, 10], num_threads=16, csls=10):
model.eval()
with torch.no_grad():
feat = model()[dev_alignments]
Lvec, Rvec = feat[:, 0, :].detach().cpu().numpy(), feat[:, 1, :].detach().cpu().numpy()
Lvec = Lvec / np.linalg.norm(Lvec, axis=-1, keepdims=True)
Rvec = Rvec / np.linalg.norm(Rvec, axis=-1, keepdims=True)
t_prec_set, acc, t_mrr = eval_alignment_by_sim_mat(Lvec, Rvec, hit_k, num_threads, csls, accurate=True)
acc = [round(n, 3) for n in acc]
return t_prec_set, acc, t_mrr
def train(model:nn.Module, alignment_pairs, dev_alignments, epochs=1500, learning_rate=0.01, weight_decay=0.001, gamma=1.0, hit_k=[1, 5, 10]):
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
print(f"parameters: {get_n_params(model)}")
losses = []
t_prec = []
accs = []
t_mrrs = []
best_acc = [0] * len(hit_k)
best_mrr = 0
batch_size = len(alignment_pairs)
for i in tqdm(range(epochs)):
### forwad
model.train()
optimizer.zero_grad()
feat = model()[alignment_pairs]
### loss
l, r, fl, fr = feat[:, 0, :], feat[:, 1, :], feat[:, 2, :], feat[:, 3, :]
loss = torch.sum(nn.ReLU()(gamma + l1(l, r) - l1(l, fr)) + nn.ReLU()(gamma + l1(l, r) - l1(fl, r))) / batch_size
### backward
losses.append(loss.item())
loss.backward(retain_graph=True)
optimizer.step()
### evaluate
if (i + 1) % 10 == 0:
t_prec_set, acc, t_mrr = evaluate(model, dev_alignments, hit_k)
for i in range(len(hit_k)):
if best_acc[i] < acc[i]:
best_acc[i] = acc[i]
if best_mrr < t_mrr:
best_mrr = t_mrr
print(f"//best results: hits@{hit_k} = {best_acc}, mrr = {best_mrr:.3f}//")
accs.append(acc)
t_mrrs.append(t_mrr)
t_prec.append(t_prec_set)
return losses, t_prec, accs, t_mrrs, best_acc, best_mrr
if __name__ == "__main__":
### hyper parmeters
parser = argparse.ArgumentParser(description="Simple-HHEA Experiment")
parser.add_argument("--data", type=str, default="icews_wiki")
parser.add_argument("--cuda", type=int, default=3)
parser.add_argument("--random_seed", type=int, default=12306)
###### ablation settings
parser.add_argument("--add_noise", action="store_true")
parser.add_argument("--noise_ratio", type=float, default=0.3)
parser.add_argument("--no_structure", action="store_true")
parser.add_argument("--no_time", action="store_true")
###### training settings
parser.add_argument("--lr", type=float, default=0.01)
parser.add_argument("--wd", type=float, default=0.001)
parser.add_argument("--gamma", type=float, default=1.0)
parser.add_argument("--epochs", type=int, default=1500)
args = parser.parse_args()
### basic settings
data = args.data
device = f"cuda:{args.cuda}" if torch.cuda.is_available() and args.cuda >= 0 else "cpu"
use_time = ("icews_wiki" in data or "icews_yago" in data) and not args.no_time
use_structure = not args.no_structure
print(f"start exp: noise_ratio={args.noise_ratio}, data=\"{args.data}\", use_structure={use_structure}, use_time={use_time}")
### random settings
fixed(args.random_seed)
### load datas
data_path = os.path.join("data", data)
all_triples, node_size, rel_size = load_triples(data_path, True)
print(f"node_size={node_size} , rel_size={rel_size}")
train_alignments = load_alignments(os.path.join(data_path, "sup_pairs"))
dev_alignments = load_alignments(os.path.join(data_path, "ref_pairs"))
print(f"Train/Val: {len(train_alignments)}/{len(dev_alignments)}")
### load name embeddings
ent_name_emb, ent_dw_emb, ent_time_emb = load_embeddings(data_path, args.add_noise, args.noise_ratio, use_structure, use_time)
### model
model = Simple_HHEA(
time_span=1+27*13,
ent_name_emb=ent_name_emb,
ent_time_emb=ent_time_emb,
ent_dw_emb=ent_dw_emb,
use_structure=use_structure,
use_time=use_time,
emb_size=64,
structure_size=8,
time_size=8,
device=device
)
model = model.to(device)
alignment_pairs = get_train_set(train_alignments, node_size, node_size)
losses, t_prec, accs, t_mrrs, best_acc, best_mrr = train(model, alignment_pairs, dev_alignments, args.epochs, args.lr, args.wd, args.gamma, hit_k=[1, 5, 10])
### save result
result_dir = "result"
if not os.path.exists(result_dir):
os.makedirs(result_dir)
with open(os.path.join(result_dir, f"{data}_result_file_mlp.txt"), "a+", encoding="utf-8") as fw:
fw.write(f"settings: noise_ratio: {args.noise_ratio}, use_time: {use_time}, use_structure: {use_structure}\n\tbest results: hits@[1, 5, 10] = {best_acc}, mrr = {best_mrr:.3f}\n")