"""
**DistMult-WN18RR-single-gpu-wandb** ||
`DistMult-WN18RR-single-gpu-adv-wandb <single_gpu_distmult_WN18RR_adv_sigmoidloss_wandb.html>`_ ||
`DistMult-WN18RR-single-gpu-adv-hpo <single_gpu_distmult_WN18RR_adv_hpo.html>`_

DistMult-WN18RR-single-gpu-wandb
====================================================================

.. Note:: created by LuYF-Lemon-love <luyanfeng_nlp@qq.com> on May 7, 2023

.. Note:: updated by LuYF-Lemon-love <luyanfeng_nlp@qq.com> on May 16, 2024

.. Note:: last run by LuYF-Lemon-love <luyanfeng_nlp@qq.com> on May 16, 2024

这一部分介绍如何用一个 GPU 在 ``WN18RR`` 知识图谱上训练 ``DistMult`` :cite:`DistMult`，使用 ``wandb`` 记录实验结果。

导入数据
-----------------
UniKE 有两个工具用于导入数据: :py:class:`unike.data.KGEDataLoader`。
"""

import os
from unike.utils import WandbLogger
from unike.data import KGEDataLoader, BernSampler, TradTestSampler
from unike.module.model import DistMult
from unike.module.loss import SoftplusLoss
from unike.module.strategy import NegativeSampling
from unike.config import Trainer, Tester

######################################################################
# 首先初始化 :py:class:`unike.utils.WandbLogger` 日志记录器，它是对 wandb 初始化操作的一层简单封装。

wandb_logger = WandbLogger().set_config(
	project="unike",
	name="DistMult-WN18RR",
	config=dict(
        in_path = os.path.join(os.path.dirname(__file__), '../../benchmarks/WN18RR/'),
        batch_size = 4096,
        neg_ent = 25,
        test = True,
        test_batch_size = 10,
        num_workers = 16,
        dim = 200,
        regul_rate = 1.0,
        use_tqdm = False,
        use_gpu = True,
        device = 'cuda:1',
        epochs = 2000,
        lr = 0.5,
        opt_method = 'adagrad',
        valid_interval = 100,
        log_interval = 100,
        save_interval = 100,
        save_path = '../../checkpoint/distMult.pth'
	)
)

config = wandb_logger.config

######################################################################
# UniKE 提供了很多数据集，它们很多都是 KGE 原论文发表时附带的数据集。
# :py:class:`unike.data.KGEDataLoader` 包含 ``in_path`` 用于传递数据集目录。

# dataloader for training
dataloader = KGEDataLoader(
	in_path = config.in_path, 
	batch_size = config.batch_size,
	neg_ent = config.neg_ent,
	test = config.test,
	test_batch_size = config.test_batch_size,
	num_workers = config.num_workers,
	train_sampler = BernSampler,
	test_sampler = TradTestSampler
)

######################################################################
# --------------
#

################################
# 导入模型
# ------------------
# UniKE 提供了很多 KGE 模型，它们都是目前最常用的基线模型。我们下面将要导入
# :py:class:`unike.module.model.DistMult`，它是最简单的双线性模型。

# define the model
distmult = DistMult(
	ent_tol = dataloader.get_ent_tol(),
	rel_tol = dataloader.get_rel_tol(),
	dim = config.dim
)

######################################################################
# --------------
#


#####################################################################
# 损失函数
# ----------------------------------------
# 我们这里使用了逻辑损失函数：:py:class:`unike.module.loss.SoftplusLoss`，
# :py:class:`unike.module.strategy.NegativeSampling` 对
# :py:class:`unike.module.loss.SoftplusLoss` 进行了封装，加入权重衰减等额外项。

# define the loss function
model = NegativeSampling(
	model = distmult, 
	loss = SoftplusLoss(), 
	regul_rate = config.regul_rate
)

######################################################################
# --------------
#

######################################################################
# 训练模型
# -------------
# UniKE 将训练循环包装成了 :py:class:`unike.config.Trainer`，
# 可以运行它的 :py:meth:`unike.config.Trainer.run` 函数进行模型学习；
# 也可以通过传入 :py:class:`unike.config.Tester`，
# 使得训练器能够在训练过程中评估模型。

# test the model
tester = Tester(model = distmult, data_loader = dataloader, use_tqdm = config.use_tqdm,
                use_gpu = config.use_gpu, device = config.device)

# train the model
trainer = Trainer(model = model, data_loader = dataloader.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, wandb_logger = wandb_logger)
trainer.run()

# close your wandb run
wandb_logger.finish()

######################################################################
# .. Note:: 上述代码的运行日志可以从 `此处 </zh-cn/latest/_static/logs/examples/DistMult/single_gpu_distmult_WN18RR_wandb.txt>`_ 下载。
# .. Note:: 上述代码的运行报告可以从 `此处 </zh-cn/latest/_static/pdfs/examples/DistMult/DistMult单卡训练示例（一）.pdf>`_ 下载。

######################################################################
# --------------
#