pybind11_ke.module.model.RESCAL 源代码

# coding:utf-8
#
# pybind11_ke/module/model/RESCAL.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 12, 2023
# 
# 该头文件定义了 RESCAL.

"""
RESCAL - 一个张量分解模型。
"""

import torch
import typing
import numpy as np
import torch.nn as nn
from .Model import Model
from typing_extensions import override

class RESCAL(Model):

	"""
	``RESCAL`` :cite:`RESCAL` 提出于 2011 年，是很多张量分解模型的基石，模型较复杂。

	评分函数为:

	.. math::
	
		-\mathbf{h}^T \mathbf{M}_r \mathbf{t}

	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:`RESCAL <rescal>`。

	例子::

		from pybind11_ke.config import Trainer, Tester
		from pybind11_ke.module.model import RESCAL
		from pybind11_ke.module.loss import MarginLoss
		from pybind11_ke.module.strategy import NegativeSampling

		# define the model
		rescal = RESCAL(
			ent_tol = train_dataloader.get_ent_tol(),
			rel_tol = train_dataloader.get_rel_tol(),
			dim = 50
		)

		# define the loss function
		model = NegativeSampling(
			model = rescal, 
			loss = MarginLoss(margin = 1.0),
			batch_size = train_dataloader.get_batch_size(), 
		)

		# test the model
		tester = Tester(model = rescal, data_loader = test_dataloader, use_gpu = True, device = 'cuda:1')

		# train the model
		trainer = Trainer(model = model, data_loader = train_dataloader, epochs = 1000,
			lr = 0.1, opt_method = 'adagrad', use_gpu = True, device = 'cuda:1',
			tester = tester, test = True, valid_interval = 100,
			log_interval = 100, save_interval = 100,
			save_path = '../../checkpoint/rescal.pth', use_wandb = False)
		trainer.run()
	"""

	def __init__(
		self,
		ent_tol: int,
		rel_tol: int,
		dim: int = 100):

		"""创建 RESCAL 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: 实体和关系嵌入向量的维度
		:type dim: int
		"""

		super(RESCAL, self).__init__(ent_tol, rel_tol)

		#: 实体和关系嵌入向量的维度
		self.dim: int = dim
		#: 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，创建的关系矩阵
		self.rel_matrices: torch.nn.Embedding = nn.Embedding(self.rel_tol, self.dim * self.dim)

		nn.init.xavier_uniform_(self.ent_embeddings.weight.data)
		nn.init.xavier_uniform_(self.rel_matrices.weight.data)
	
	def _calc(
		self,
		h: torch.Tensor,
		t: torch.Tensor,
		r: torch.Tensor) -> torch.Tensor:

		"""计算 RESCAL 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: 关系矩阵。
		:type r: torch.Tensor
		:returns: 三元组的得分
		:rtype: torch.Tensor
		"""

		t = t.view(-1, self.dim, 1)
		r = r.view(-1, self.dim, self.dim)
		tr = torch.matmul(r, t)
		tr = tr.view(-1, self.dim)
		return -torch.sum(h * tr, -1)

	@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']
		h = self.ent_embeddings(batch_h)
		t = self.ent_embeddings(batch_t)
		r = self.rel_matrices(batch_r)
		score = self._calc(h ,t, r)
		return score

	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_matrices(batch_r)
		regul = (torch.mean(h ** 2) + torch.mean(t ** 2) + torch.mean(r ** 2)) / 3
		return regul

	@override
	def predict(
		self,
		data: dict[str, typing.Union[torch.Tensor,str]]) -> np.ndarray:

		"""RESCAL 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)
		return score.cpu().data.numpy()

def get_rescal_hpo_config() -> dict[str, dict[str, typing.Any]]:

	"""返回 :py:class:`RESCAL` 的默认超参数优化配置。
	
	默认配置为::
	
		parameters_dict = {
			'model': {
				'value': 'RESCAL'
			},
			'dim': {
				'values': [50, 100, 200]
			}
		}

	:returns: :py:class:`RESCAL` 的默认超参数优化配置
	:rtype: dict[str, dict[str, typing.Any]]
	"""

	parameters_dict = {
		'model': {
			'value': 'RESCAL'
		},
		'dim': {
			'values': [50, 100, 200]
		}
	}
		
	return parameters_dict