basic:
  dataset: AirQuality
  device: cuda:0
  mode: train
  model: MTGNN
  seed: 2023

data:
  batch_size: 64
  column_wise: false
  days_per_week: 7
  horizon: 24
  input_dim: 6
  lag: 24
  normalizer: std
  num_nodes: 35
  steps_per_day: 24
  test_ratio: 0.2
  val_ratio: 0.2

model:
  gcn_true: True  # 是否使用图卷积网络 (bool)
  buildA_true: True  # 是否动态构建邻接矩阵 (bool)
  subgraph_size: 20  # 子图大小 (int)
  num_nodes: 35  # 节点数量 (int)
  node_dim: 40  # 节点嵌入维度 (int)
  dilation_exponential: 1  # 膨胀卷积指数 (int)
  conv_channels: 32  # 卷积通道数 (int)
  residual_channels: 32  # 残差通道数 (int)
  skip_channels: 64  # 跳跃连接通道数 (int)
  end_channels: 128  # 输出层通道数 (int)
  seq_len: 24  # 输入序列长度 (int)
  in_dim: 6 # 输入特征维度 (int)
  out_len: 24 # 输出序列长度 (int)
  out_dim: 6 # 输出预测维度 (int)
  layers: 3  # 模型层数 (int)
  propalpha: 0.05 # 图传播参数alpha (float)
  tanhalpha: 3 # tanh激活参数alpha (float)
  layer_norm_affline: True # 层归一化是否使用affine变换 (bool)
  gcn_depth: 2  # 图卷积深度 (int)
  dropout: 0.3  # dropout率 (float)
  predefined_A: null # 预定义邻接矩阵 (optional, None)
  static_feat: null  # 静态特征 (optional, None)

train:
  batch_size: 64
  debug: false
  early_stop: true
  early_stop_patience: 15
  epochs: 100
  grad_norm: false
  log_step: 1000
  loss_func: mae
  lr_decay: true
  lr_decay_rate: 0.3
  lr_decay_step: 5,20,40,70
  lr_init: 0.003
  mae_thresh: None
  mape_thresh: 0.001
  max_grad_norm: 5
  output_dim: 6
  plot: false
  real_value: true
  weight_decay: 0