# coding:utf-8
#
# unike/module/model/TransH.py
#
# git pull from OpenKE-PyTorch by LuYF-Lemon-love <luyanfeng_nlp@qq.com> on May 7, 2023
# updated by LuYF-Lemon-love <luyanfeng_nlp@qq.com> on Feb 25, 2024
#
# 该头文件定义了 TransH.
"""
TransH - 是第二个平移模型，将关系建模为超平面上的平移操作。
"""
import torch
import typing
import torch.nn as nn
import torch.nn.functional as F
from .Model import Model
from typing_extensions import override
[文档]class TransH(Model):
"""
``TransH`` :cite:`TransH` 提出于 2014 年，是第二个平移模型，将关系建模为超平面上的平移操作。
评分函数为:
.. math::
\Vert (h-r_w^T hr_w)+r_d-(t-r_w^T tr_w)\Vert_{L_1/L_2}
正三元组的评分函数的值越小越好，如果想获得更详细的信息请访问 :ref:`TransH <transh>`。
例子::
from unike.data import KGEDataLoader, BernSampler, TradTestSampler
from unike.module.model import TransH
from unike.module.loss import MarginLoss
from unike.module.strategy import NegativeSampling
from unike.config import Trainer, Tester
# dataloader for training
dataloader = KGEDataLoader(
in_path = "../../benchmarks/FB15K237/",
batch_size = 4096,
neg_ent = 25,
test = True,
test_batch_size = 30,
num_workers = 16,
train_sampler = BernSampler,
test_sampler = TradTestSampler
)
# define the model
transh = TransH(
ent_tol = dataloader.get_ent_tol(),
rel_tol = dataloader.get_rel_tol(),
dim = 200,
p_norm = 1,
norm_flag = True)
# define the loss function
model = NegativeSampling(
model = transh,
loss = MarginLoss(margin = 4.0),
# regul_rate = 0.01
)
# test the model
tester = Tester(model = transh, data_loader = dataloader, use_gpu = True, device = 'cuda:1')
# train the model
trainer = Trainer(model = model, data_loader = dataloader.train_dataloader(),
epochs = 1000, lr = 0.5, use_gpu = True, device = 'cuda:1',
tester = tester, test = True, valid_interval = 100,
log_interval = 100, save_interval = 100, save_path = '../../checkpoint/transh.pth',
delta = 0.01)
trainer.run()
"""
[文档] def __init__(
self,
ent_tol: int,
rel_tol: int,
dim: int = 100,
p_norm: int = 1,
norm_flag: bool = True,
margin: float | None = None):
"""创建 TransH 对象。
:param ent_tol: 实体的个数
:type ent_tol: int
:param rel_tol: 关系的个数
:type rel_tol: int
:param dim: 实体、关系嵌入向量和和法向量的维度
:type dim: int
:param p_norm: 评分函数的距离函数, 按照原论文，这里可以取 1 或 2。
:type p_norm: int
:param norm_flag: 是否利用 :py:func:`torch.nn.functional.normalize` 对实体和关系嵌入的最后一维执行 L2-norm。
:type norm_flag: bool
:param margin: 当使用 ``RotatE`` :cite:`RotatE` 的损失函数 :py:class:`unike.module.loss.SigmoidLoss`，需要提供此参数，将 ``TransE`` :cite:`TransE` 的正三元组的评分由越小越好转化为越大越好，如果想获得更详细的信息请访问 :ref:`RotatE <rotate>`。
:type margin: float
"""
super(TransH, self).__init__(ent_tol, rel_tol)
#: 实体、关系嵌入向量和和法向量的维度
self.dim: int = dim
#: 评分函数的距离函数, 按照原论文，这里可以取 1 或 2。
self.p_norm: int = p_norm
#: 是否利用 :py:func:`torch.nn.functional.normalize`
#: 对实体和关系嵌入向量的最后一维执行 L2-norm。
self.norm_flag: bool = norm_flag
#: 根据实体个数，创建的实体嵌入
self.ent_embeddings: torch.nn.Embedding = nn.Embedding(self.ent_tol, self.dim)
#: 根据关系个数，创建的关系嵌入
self.rel_embeddings: torch.nn.Embedding = nn.Embedding(self.rel_tol, self.dim)
#: 根据关系个数，创建的法向量
self.norm_vector: torch.nn.Embedding = nn.Embedding(self.rel_tol, self.dim)
if margin != None:
#: 当使用 ``RotatE`` :cite:`RotatE` 的损失函数 :py:class:`unike.module.loss.SigmoidLoss`，需要提供此参数，将 ``TransE`` :cite:`TransE` 的正三元组的评分由越小越好转化为越大越好，如果想获得更详细的信息请访问 :ref:`RotatE <rotate>`。
self.margin: torch.nn.parameter.Parameter = nn.Parameter(torch.Tensor([margin]))
self.margin.requires_grad = False
self.margin_flag = True
else:
self.margin_flag = False
nn.init.xavier_uniform_(self.ent_embeddings.weight.data)
nn.init.xavier_uniform_(self.rel_embeddings.weight.data)
nn.init.xavier_uniform_(self.norm_vector.weight.data)
[文档] @override
def forward(
self,
triples: torch.Tensor,
negs: torch.Tensor = None,
mode: str = 'single') -> torch.Tensor:
"""
定义每次调用时执行的计算。
:py:class:`torch.nn.Module` 子类必须重写 :py:meth:`torch.nn.Module.forward`。
:param triples: 正确的三元组
:type triples: torch.Tensor
:param negs: 负三元组类别
:type negs: torch.Tensor
:param mode: 模式
:type triples: str
:returns: 三元组的得分
:rtype: torch.Tensor
"""
head_emb, relation_emb, tail_emb = self.tri2emb(triples, negs, mode)
norm_vector = self.norm_vector(triples[:, 1]).unsqueeze(dim=1)
head_emb = self._transfer(head_emb, norm_vector)
tail_emb = self._transfer(tail_emb, norm_vector)
score = self._calc(head_emb, relation_emb, tail_emb)
if self.margin_flag:
return self.margin - score
else:
return score
[文档] def _transfer(
self,
e: torch.Tensor,
norm: torch.Tensor) -> torch.Tensor:
"""
将头实体或尾实体的向量投影到超平面上。
:param e: 头实体或尾实体向量。
:type e: torch.Tensor
:param norm: 法向量
:type norm: torch.Tensor
:returns: 投影后的实体向量
:rtype: torch.Tensor
"""
norm = F.normalize(norm, p = 2, dim = -1)
return e - torch.sum(e * norm, -1, True) * norm
[文档] def _calc(
self,
h: torch.Tensor,
r: torch.Tensor,
t: torch.Tensor) -> torch.Tensor:
"""计算 TransH 的评分函数。
:param h: 头实体的向量。
:type h: torch.Tensor
:param r: 关系实体的向量。
:type r: torch.Tensor
:param t: 尾实体的向量。
:type t: torch.Tensor
:returns: 三元组的得分
:rtype: torch.Tensor
"""
# 对嵌入的最后一维进行归一化
if self.norm_flag:
h = F.normalize(h, 2, -1)
r = F.normalize(r, 2, -1)
t = F.normalize(t, 2, -1)
score = (h + r) - t
# 利用距离函数计算得分
score = torch.norm(score, self.p_norm, -1)
return score
[文档] @override
def predict(
self,
data: dict[str, typing.Union[torch.Tensor,str]],
mode: str) -> torch.Tensor:
"""TransH 的推理方法。
:param data: 数据。
:type data: dict[str, typing.Union[torch.Tensor,str]]
:param mode: 'head_predict' 或 'tail_predict'
:type mode: str
:returns: 三元组的得分
:rtype: torch.Tensor
"""
triples = data["positive_sample"]
head_emb, relation_emb, tail_emb = self.tri2emb(triples, mode=mode)
norm_vector = self.norm_vector(triples[:, 1]).unsqueeze(dim=1)
head_emb = self._transfer(head_emb, norm_vector)
tail_emb = self._transfer(tail_emb, norm_vector)
score = self._calc(head_emb, relation_emb, tail_emb)
if self.margin_flag:
score = self.margin - score
return score
else:
return -score
[文档] def regularization(
self,
data: dict[str, typing.Union[torch.Tensor, str]]) -> torch.Tensor:
"""L2 正则化函数（又称权重衰减），在损失函数中用到。
:param data: 数据。
:type data: dict[str, typing.Union[torch.Tensor, str]]
:returns: 模型参数的正则损失
:rtype: torch.Tensor
"""
pos_sample = data["positive_sample"]
neg_sample = data["negative_sample"]
mode = data["mode"]
pos_head_emb, pos_relation_emb, pos_tail_emb = self.tri2emb(pos_sample)
pos_norm_vector = self.norm_vector(pos_sample[:, 1]).unsqueeze(dim=1)
if mode == "bern":
neg_head_emb, neg_relation_emb, neg_tail_emb = self.tri2emb(neg_sample)
else:
neg_head_emb, neg_relation_emb, neg_tail_emb = self.tri2emb(pos_sample, neg_sample, mode)
neg_norm_vector = self.norm_vector(pos_sample[:, 1]).unsqueeze(dim=1)
pos_regul = (torch.mean(pos_head_emb ** 2) +
torch.mean(pos_relation_emb ** 2) +
torch.mean(pos_tail_emb ** 2) +
torch.mean(pos_norm_vector ** 2)) / 4
neg_regul = (torch.mean(neg_head_emb ** 2) +
torch.mean(neg_relation_emb ** 2) +
torch.mean(neg_tail_emb ** 2) +
torch.mean(neg_norm_vector ** 2)) / 4
regul = (pos_regul + neg_regul) / 2
return regul
[文档]def get_transh_hpo_config() -> dict[str, dict[str, typing.Any]]:
"""返回 :py:class:`TransH` 的默认超参数优化配置。
默认配置为::
parameters_dict = {
'model': {
'value': 'TransH'
},
'dim': {
'values': [50, 100, 200]
},
'p_norm': {
'values': [1, 2]
},
'norm_flag': {
'value': True
}
}
:returns: :py:class:`TransH` 的默认超参数优化配置
:rtype: dict[str, dict[str, typing.Any]]
"""
parameters_dict = {
'model': {
'value': 'TransH'
},
'dim': {
'values': [50, 100, 200]
},
'p_norm': {
'values': [1, 2]
},
'norm_flag': {
'value': True
}
}
return parameters_dict