# coding:utf-8
#
# pybind11_ke/module/model/TransD.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 Jan 22, 2023
#
# 该头文件定义了 TransD.
"""
TransD - 自动生成映射矩阵，简单而且高效，是对 TransR 的改进。
"""
import torch
import typing
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from .Model import Model
from typing_extensions import override
[文档]class TransD(Model):
"""
``TransD`` :cite:`TransD` 提出于 2015 年，自动生成映射矩阵，简单而且高效，是对 TransR 的改进。
评分函数为:
.. math::
\parallel (\mathbf{r}_p \mathbf{h}_p^T + \mathbf{I})\mathbf{h} + \mathbf{r} - (\mathbf{r}_p \mathbf{t}_p^T + \mathbf{I})\mathbf{t} \parallel_{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:`TransD <transd>`。
例子::
from pybind11_ke.config import Trainer, Tester
from pybind11_ke.module.model import TransD
from pybind11_ke.module.loss import MarginLoss
from pybind11_ke.module.strategy import NegativeSampling
# define the model
transd = TransD(
ent_tol = train_dataloader.get_ent_tol(),
rel_tol = train_dataloader.get_rel_tol(),
dim_e = config.dim_e,
dim_r = config.dim_r,
p_norm = config.p_norm,
norm_flag = config.norm_flag)
# define the loss function
model = NegativeSampling(
model = transd,
loss = MarginLoss(margin = config.margin),
batch_size = train_dataloader.get_batch_size()
)
# dataloader for test
test_dataloader = TestDataLoader(in_path = config.in_path)
# test the model
tester = Tester(model = transd, data_loader = test_dataloader, use_gpu = config.use_gpu, device = config.device)
# train the model
trainer = Trainer(model = model, data_loader = train_dataloader, epochs = config.epochs,
lr = config.lr, opt_method = config.opt_method, use_gpu = config.use_gpu, device = config.device,
tester = tester, test = config.test, valid_interval = config.valid_interval,
log_interval = config.log_interval, save_interval = config.save_interval,
save_path = config.save_path, use_wandb = True)
trainer.run()
"""
[文档] def __init__(
self,
ent_tol: int,
rel_tol: int,
dim_e: int = 100,
dim_r: int = 100,
p_norm: int = 1,
norm_flag: bool = True,
margin: float | None = None):
"""创建 TransD 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_e: 实体嵌入和实体投影向量的维度
:type dim_e: int
:param dim_r: 关系嵌入和关系投影向量的维度
:type dim_r: 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:`pybind11_ke.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(TransD, self).__init__(ent_tol, rel_tol)
#: 实体嵌入和实体投影向量的维度
self.dim_e: int = dim_e
#: 关系嵌入和关系投影向量的维度
self.dim_r: int = dim_r
#: 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_e)
#: 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_r)
#: 根据实体个数，创建的实体投影向量
self.ent_transfer: torch.nn.Embedding = nn.Embedding(self.ent_tol, self.dim_e)
#: 根据关系个数，创建的关系投影向量
self.rel_transfer: torch.nn.Embedding = nn.Embedding(self.rel_tol, self.dim_r)
if margin != None:
#: 当使用 ``RotatE`` :cite:`RotatE` 的损失函数 :py:class:`pybind11_ke.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: bool = True
else:
self.margin_flag: bool = False
nn.init.xavier_uniform_(self.ent_embeddings.weight.data)
nn.init.xavier_uniform_(self.rel_embeddings.weight.data)
nn.init.xavier_uniform_(self.ent_transfer.weight.data)
nn.init.xavier_uniform_(self.rel_transfer.weight.data)
[文档] @override
def forward(
self,
data: dict[str, typing.Union[torch.Tensor, str]]) -> 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 data: 数据。
:type data: dict[str, typing.Union[torch.Tensor,str]]
:returns: 三元组的得分
:rtype: torch.Tensor
"""
batch_h = data['batch_h']
batch_t = data['batch_t']
batch_r = data['batch_r']
mode = data['mode']
h = self.ent_embeddings(batch_h)
t = self.ent_embeddings(batch_t)
r = self.rel_embeddings(batch_r)
h_transfer = self.ent_transfer(batch_h)
t_transfer = self.ent_transfer(batch_t)
r_transfer = self.rel_transfer(batch_r)
h = self._transfer(h, h_transfer, r_transfer)
t = self._transfer(t, t_transfer, r_transfer)
score = self._calc(h ,t, r, mode)
if self.margin_flag:
return self.margin - score
else:
return score
[文档] def _transfer(
self,
e: torch.Tensor,
e_transfer: torch.Tensor,
r_transfer: torch.Tensor) -> torch.Tensor:
"""
将头实体或尾实体的向量映射到关系向量空间。
:param e: 头实体或尾实体向量。
:type e: torch.Tensor
:param e_transfer: 头实体或尾实体的投影向量
:type e_transfer: torch.Tensor
:param r_transfer: 关系的投影向量
:type r_transfer: torch.Tensor
:returns: 投影后的实体向量
:rtype: torch.Tensor
"""
if e.shape[0] != r_transfer.shape[0]:
e = e.view(-1, r_transfer.shape[0], e.shape[-1])
e_transfer = e_transfer.view(-1, r_transfer.shape[0], e_transfer.shape[-1])
r_transfer = r_transfer.view(-1, r_transfer.shape[0], r_transfer.shape[-1])
e = F.normalize(
self._resize(e, -1, r_transfer.size()[-1]) + torch.sum(e * e_transfer, -1, True) * r_transfer,
p = 2,
dim = -1
)
return e.view(-1, e.shape[-1])
else:
return F.normalize(
self._resize(e, -1, r_transfer.size()[-1]) + torch.sum(e * e_transfer, -1, True) * r_transfer,
p = 2,
dim = -1
)
[文档] def _resize(
self,
tensor: torch.Tensor,
axis: int,
size: int) -> torch.Tensor:
"""计算实体向量与单位矩阵的乘法，并返回结果向量。
源代码使用 :py:func:`torch.narrow` 进行缩小向量，
:py:func:`torch.nn.functional.pad` 进行填充向量。
:param tensor: 实体向量。
:type tensor: torch.Tensor
:param axis: 在哪个轴上进行乘法运算。
:type axis: int
:param size: 运算结果在 ``axis`` 上的维度大小，一般为关系向量的维度。
:type size: int
:returns: 乘法结果的向量
:rtype: torch.Tensor
"""
shape = tensor.size()
osize = shape[axis]
if osize == size:
return tensor
if (osize > size):
return torch.narrow(tensor, axis, 0, size)
paddings = []
for i in range(len(shape)):
if i == axis:
paddings = [0, size - osize] + paddings
else:
paddings = [0, 0] + paddings
print (paddings)
return F.pad(tensor, paddings = paddings, mode = "constant", value = 0)
[文档] def _calc(
self,
h: torch.Tensor,
t: torch.Tensor,
r: torch.Tensor,
mode: str) -> torch.Tensor:
"""计算 TransD 的评分函数。
:param h: 头实体的向量。
:type h: torch.Tensor
:param t: 尾实体的向量。
:type t: torch.Tensor
:param r: 关系的向量。
:type r: torch.Tensor
:param mode: ``normal`` 表示 :py:class:`pybind11_ke.data.TrainDataLoader`
为训练同时进行头实体和尾实体负采样的数据，``head_batch`` 和 ``tail_batch``
表示为了减少数据传输成本，需要进行广播的数据，在广播前需要 reshape。
:type mode: str
: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)
# 保证 h, r, t 都是三维的
if mode != 'normal':
h = h.view(-1, r.shape[0], h.shape[-1])
t = t.view(-1, r.shape[0], t.shape[-1])
r = r.view(-1, r.shape[0], r.shape[-1])
# 两者结果一样，括号只是逻辑上的，'head_batch' 是替换 head，否则替换 tail
if mode == 'head_batch':
score = h + (r - t)
else:
score = (h + r) - t
# 利用距离函数计算得分
score = torch.norm(score, self.p_norm, -1).flatten()
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
"""
batch_h = data['batch_h']
batch_t = data['batch_t']
batch_r = data['batch_r']
h = self.ent_embeddings(batch_h)
t = self.ent_embeddings(batch_t)
r = self.rel_embeddings(batch_r)
h_transfer = self.ent_transfer(batch_h)
t_transfer = self.ent_transfer(batch_t)
r_transfer = self.rel_transfer(batch_r)
regul = (torch.mean(h ** 2) +
torch.mean(t ** 2) +
torch.mean(r ** 2) +
torch.mean(h_transfer ** 2) +
torch.mean(t_transfer ** 2) +
torch.mean(r_transfer ** 2)) / 6
return regul
[文档] @override
def predict(
self,
data: dict[str, typing.Union[torch.Tensor,str]]) -> np.ndarray:
"""TransD 的推理方法。
:param data: 数据。
:type data: dict[str, typing.Union[torch.Tensor,str]]
:returns: 三元组的得分
:rtype: numpy.ndarray
"""
score = self.forward(data)
if self.margin_flag:
score = self.margin - score
return score.cpu().data.numpy()
else:
return score.cpu().data.numpy()
[文档]def get_transd_hpo_config() -> dict[str, dict[str, typing.Any]]:
"""返回 :py:class:`TransD` 的默认超参数优化配置。
默认配置为::
parameters_dict = {
'model': {
'value': 'TransD'
},
'dim_e': {
'values': [50, 100, 200]
},
'dim_r': {
'values': [50, 100, 200]
},
'p_norm': {
'values': [1, 2]
},
'norm_flag': {
'value': True
}
}
:returns: :py:class:`TransD` 的默认超参数优化配置
:rtype: dict[str, dict[str, typing.Any]]
"""
parameters_dict = {
'model': {
'value': 'TransD'
},
'dim_e': {
'values': [50, 100, 200]
},
'dim_r': {
'values': [50, 100, 200]
},
'p_norm': {
'values': [1, 2]
},
'norm_flag': {
'value': True
}
}
return parameters_dict