ANALOGY-WN18RR-single-gpu-wandb¶

这一部分介绍如何用一个 GPU 在 WN18RR 知识图谱上训练 ANALOGY [LWY17]，使用 wandb 记录实验结果。

导入数据¶

pybind11-OpenKE 有两个工具用于导入数据: pybind11_ke.data.TrainDataLoader 和 pybind11_ke.data.TestDataLoader。

from pybind11_ke.utils import WandbLogger
from pybind11_ke.config import Trainer, Tester
from pybind11_ke.module.model import Analogy
from pybind11_ke.module.loss import SoftplusLoss
from pybind11_ke.module.strategy import NegativeSampling
from pybind11_ke.data import TrainDataLoader, TestDataLoader

首先初始化 pybind11_ke.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(
    project="pybind11-ke",
    name="analogy",
    config=dict(
            in_path = '../../benchmarks/WN18RR/',
            nbatches = 100,
            threads = 2,
            sampling_mode = 'normal',
            bern = True,
            neg_ent = 25,
            neg_rel = 0,
            dim = 200,
            regul_rate = 1.0,
            use_gpu = True,
            device = 'cuda:1',
            epochs = 2000,
            lr = 0.5,
            opt_method = 'adagrad',
            test = True,
            valid_interval = 100,
            log_interval = 100,
            save_interval = 100,
            save_path = '../../checkpoint/analogy.pth',
            delta = 0.01
    )
)

config = wandb_logger.config

pybind11-KE 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。 pybind11_ke.data.TrainDataLoader 包含 in_path 用于传递数据集目录。

# dataloader for training
train_dataloader = TrainDataLoader(
    in_path = config.in_path,
    nbatches = config.nbatches,
    threads = config.threads,
    sampling_mode = config.sampling_mode,
    bern = config.bern,
    neg_ent = config.neg_ent,
    neg_rel = config.neg_rel
)

导入模型¶

pybind11-OpenKE 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 pybind11_ke.module.model.Analogy，它是双线性模型的集大成者。

# define the model
analogy = Analogy(
    ent_tol = train_dataloader.get_ent_tol(),
    rel_tol = train_dataloader.get_rel_tol(),
    dim = config.dim
)

损失函数¶

我们这里使用了逻辑损失函数：pybind11_ke.module.loss.SoftplusLoss， pybind11_ke.module.strategy.NegativeSampling 对 pybind11_ke.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 = analogy,
    loss = SoftplusLoss(),
    batch_size = train_dataloader.get_batch_size(),
    regul_rate = config.regul_rate
)

训练模型¶

pybind11-OpenKE 将训练循环包装成了 pybind11_ke.config.Trainer，可以运行它的 pybind11_ke.config.Trainer.run() 函数进行模型学习；也可以通过传入 pybind11_ke.config.Tester，使得训练器能够在训练过程中评估模型；pybind11_ke.config.Tester 使用 pybind11_ke.data.TestDataLoader 作为数据采样器。

# dataloader for test
test_dataloader = TestDataLoader(in_path = config.in_path)

# test the model
tester = Tester(model = analogy, 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, delta = config.delta, use_wandb = True)
trainer.run()