如果你使用slurm 来调度你的gpu资源，要注释掉这一行
数据集放在了Data 目录下，文本格式，比如 ml.txt
user和item下标从 1 开始，必须连续
每行代表一个交互：user_id item_id
对于同一个用户，物品出现顺序不能打乱，但不同用户的数据出现位置可以混在一起
sample
1 2
1 5
1 1
2 1
2 2
1 7
需要更改配置文件 config.json
dataset part
load_processed_dataset: the dataset you put in the `Data` will be processed into .pkl,
you can load it for saving time. true or false, e.g. false
processed_dataset_path: absolute path, e.g. "C:processed/ml.pkl"
dataloader_random_seed: float, default=0.0
train_batch_size: batch size, e.g. 64
val_batch_size: batch size, e.g. 64
test_batch_size: batch size, e.g. 64
prop_sliding_window: propotion of the sliding window step, if the input seq is exceeding the max_len,
you can use a sliding window to generate a sequence of input. default: 0.1,
if you don't want this sliding windown, set this parameter as -1.0.
worker_number: for multi-processer, usually it could be 4 times #cpu core you have
train_negative_sampler_code: popular or random, e.g. "popular"
train_negative_sample_size: for bert and sas, this is unused, set as 0
train_negative_sampling_seed: 0
test_negative_sampler_code: popular or random, e.g. "popular"
test_negative_sample_size: default: 100
test_negative_sampling_seed: 0
training part
mode: train or test, e.g. "train"
test_model_path: absolute path, e.g. "C://model//my_model.pth"
resume_path: absolute path, e.g. "C://model//check_point_model.pth"
device: cpu or cuda, default: "cpu"
num_gpu: default: 1
device_idx: note: this is a string, you can type "0", or "0, 1, 2"
optimizer: "Adam" or "SGD"
lr: learning rate, e.g. 0.001
weight_decay: l2 regularization, e.g. 0.01
decay_step: decay step for StepLR, e.g. 15
gamma: Gamma for StepLR, e.g. 0.1
num_epochs: number of epochs for training, e.g. 100
log_period_as_iter: after every certain number of iterations, the model weight will be saved as checkpoint
metric_ks: ks for Metric@k, there are 3 types of metric: MRR, NDCG and HIT, e.g. [10, 20, 50]
best_metric: Metric for determining the best model, e.g. "NDCG@10"
show_process_bar: show the processing bar when training or testing, true or false, e.g.false
model part
model_code: bert or sas
FOR BERT
bert_max_len: Length of sequence for bert, e.g. 200
bert_hidden_units: Size of hidden vectors, e.g. 64
bert_num_blocks: number of transformer layers, e.g. 2
bert_num_heads: number of heads for multi-attention, e.g. 2
bert_dropout: used for transformer blocks
bert_hidden_dropout: used for hidden unit layers
FOR SAS
sas_max_len : Length of sequence for sas, e.g. 200
sas_hidden_units: Size of hidden vectors, e.g. 64
sas_num_blocks: number of transformer layers, e.g. 2
sas_heads: number of heads for multi-attention, e.g. 2
sas_dropout: dropout rate, e.g. 0.2
l2_emb: normalization, e.g. 0.0
experiment_dir: where you want to put you trained model and log info in, e.g. "experiments"
experiment_description: default: "test"
dataset_name: the dataset filename you put in the `Data` dir, e.g. "ml.txt"将数据集放到 Data 目录下，确保格式正确
编辑 config.json 文件
安装库
pip install -r requirements.txt
run
python main.py
可以使用命令行参数，它会覆盖 config.json
example
python main.py --mode=test
命令行参数中可以选择配置文件
example
python main.py --config_file=config1.json
python main.py --config_file=config2.json
别忘记更改 test_mdoel_path 或者 resume_path 如果需要使用 mode=test 或者 resume training
训练结束后，在对应目录下可以找到模型参数和结果，默认放在 experiment 目录下，可以通过 config.json 中的 experiment_dir 中更改
目录结构如下
- test_bert_2021-03-28_0
- logs
- tensorboard_visualization
test_metrics.json
- models
best_acc_model.pth
checkpoint-recent.pth
checkpoint-recent.pth.final
config.json
- test_bert_2021-03-28_1
models 里保存模型权重信息，用来test 或者 resume training
可视化或者用图表展示模型训练过程，首先在 logs 目录下打开 cmd （windows 下）
tensorboard --logdir=tensorboard_visualization
打开浏览器进入http://localhost:6006/
