models/time_rcd/time_rcd_config.py

from dataclasses import dataclass, field
from typing import Dict, Optional


@dataclass
class TimeSeriesConfig:
    """Configuration for time series encoder.

    Attributes:
        d_model: Dimension of model hidden states.
        d_proj: Dimension of projection layer.
        patch_size: Size of time series patches.
        num_layers: Number of transformer layers.
        num_heads: Number of attention heads.
        d_ff_dropout: Dropout rate for feed-forward networks.
        use_rope: Whether to use Rotary Position Embedding.
        activation: Activation function name.
        num_features: Number of input features.
    """
    d_model: int = 512
    d_proj: int = 256
    patch_size: int = 4
    num_query_tokens: int = 1
    num_layers: int = 8
    num_heads: int = 8
    d_ff_dropout: float = 0.1
    use_rope: bool = True
    activation: str = "gelu"
    num_features: int = 1
    test_batch_limit: int = 20


@dataclass
class TimeRCDConfig:
    """Configuration class for Time_RCD model.

    This class contains all hyperparameters and settings for the Time_RCD model.
    It is implemented as a dataclass for easy instantiation and modification.

    Attributes:
        ts_config: Configuration for time series encoder.
        batch_size: Training batch size.
        learning_rate: Learning rate for optimization.
        num_epochs: Number of training epochs.
        max_seq_len: Maximum sequence length.
        dropout: Dropout rate.
        accumulation_steps: Gradient accumulation steps.
        weight_decay: Weight decay for optimization.
        enable_ts_train: Whether to train the time series encoder.
        seed: Random seed for reproducibility.
    """

    # Model configurations
    ts_config: TimeSeriesConfig = field(default_factory=TimeSeriesConfig)

    # Training parameters
    batch_size: int = 3
    learning_rate: float = 1e-4
    num_epochs: int = 1000
    max_seq_len: int = 512
    dropout: float = 0.1
    accumulation_steps: int = 1
    weight_decay: float = 1e-5
    enable_ts_train: bool = False
    seed: int = 72
    log_freq: int = 100
    save_freq: int = 10
    save_step_freq: int = 100
    model_prefix: str = "time_rcd_qa_by_pretrain"
    test_batch_limit: int = 20
    early_stopping_patience: int = 7
    seed: int = 72
    cuda_devices: str = "0, 1, 2, 3"
    dist_port: str = "12355"     # Port for distributed training communication
    device: str = "cuda"

    def to_dict(self) -> Dict[str, any]:
        return {
            "ts_config": self.ts_config.__dict__,
            "batch_size": self.batch_size,
            "learning_rate": self.learning_rate,
            "num_epochs": self.num_epochs,
            "max_seq_len": self.max_seq_len,
            "seed": self.seed,
            "test_batch_limit": self.test_batch_limit,
            "log_freq": self.log_freq,
            "save_freq": self.save_freq,
            "save_step_freq": self.save_step_freq,
            "model_prefix": self.model_prefix,
            "device": self.device,
        }

default_config = TimeRCDConfig()