1150324 meeting

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wavenet:
  # WaveNet model parameters
  input_channels:  24  # Number of input channels
  output_channels: 24  # Number of output channels
  residual_layers: 28  # Number of residual layers
  residual_channels: 40  # Number of channels in residual blocks
  skip_channels: 40  # Number of channels in skip connections

  # Diffusion step embedding dimensions
  diffusion_step_embed_dim_input:  128  # Input dimension
  diffusion_step_embed_dim_hidden: 256  # Middle dimension
  diffusion_step_embed_dim_output: 256  # Output dimension

  # Structured State Spaces sequence model (S4) configurations
  s4_max_sequence_length: 83  # Maximum sequence length
  s4_state_dim: 128  # State dimension
  s4_dropout: 0.2  # Dropout rate
  s4_bidirectional: true  # Whether to use bidirectional layers
  s4_use_layer_norm: true  # Whether to use layer normalization

diffusion:
  # Diffusion model parameters
  T: 100  # Number of diffusion steps
  beta_0: 0.0001  # Initial beta value
  beta_T: 0.01  # Final beta value

AFRK:
# AutoFRK model parameters, for more details, please refer to https://pypi.org/project/autoFRK/
  method: "fast"  # autoFRK method to use (e.g., "fast")
  tps_method: "rectangular"  # autoFRK's TPS method to use (e.g., "rectangular")

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# Training configuration
batch_size: 80  # Batch size
output_directory: "/home/u6025091/SSSD_CP/results/Weather2k-S4+AFRK"  # Output directory for checkpoints and logs
ckpt_iter: "max"  # Checkpoint mode (max or min)
iters_per_ckpt: 500  # Checkpoint frequency (number of epochs)
iters_per_logging: 200  # Log frequency (number of iterations)
n_iters: 4000  # Maximum number of iterations
learning_rate: 0.0005  # Learning rate

# Additional training settings
only_generate_missing: true  # Generate missing values only
use_model: 2  # Model to use for training
masking: "forecast"  # Masking strategy for missing values
missing_k: 9  # Number of missing values

# Data paths
data:
  train_path: "/home/u6025091/SSSD_CP/datasets/Weather2k/data_train_known_real.npy"  # Path to training data

# autoFRK config
enable_spatial_training: true  # Enable spatial training step
location_path: "/home/u6025091/SSSD_CP/datasets/Weather2k/stations_known_locations.npy"  # Path to known locations

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# Inference configuration
batch_size: 80  # Batch size for inference
output_directory: "/home/u6025091/SSSD_CP/results/Weather2k-S4+AFRK/inference"  # Output directory for inference results
ckpt_path: "/home/u6025091/SSSD_CP/results/Weather2k-S4+AFRK"  # Path to checkpoint for inference
trials: 1 # Replications

# Additional training settings
only_generate_missing: true  # Generate missing values only
use_model: 2  # Model to use for training
masking: "forecast"  # Masking strategy for missing values  # inference mode need to fix in the code, or using other masking strategy will be failed
missing_k: 9  # Number of missing values

# Data paths
data:
  test_path: "/home/u6025091/SSSD_CP/datasets/Weather2k/data_train_known_real.npy"  # Path to test data

# autoFRK config
enable_spatial_inference: true  # Enable spatial prediction step
enable_spatial_normalization: true  # Enable spatial normalization before and after autoFRK
known_location_path: "/home/u6025091/SSSD_CP/datasets/Weather2k/stations_known_locations.npy"  # Path to known locations
unknown_location_path: "/home/u6025091/SSSD_CP/datasets/Weather2k/stations_unknown_locations.npy"  # Path to unknown locations