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Utility functions and classes for the forecasting model.
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This package contains configuration models, helper functions, and other utilities.
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"""
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"""
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# Expose configuration models
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from .config_model import (
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MainConfig,
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DataConfig,
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FeatureConfig,
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ModelConfig,
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TrainingConfig,
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CrossValidationConfig,
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EvaluationConfig,
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OptunaConfig,
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WaveletTransformConfig, # Expose nested configs if they might be used directly
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ClippingConfig
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)
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# Define __all__ for explicit public API
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__all__ = [
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"MainConfig",
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"DataConfig",
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"FeatureConfig",
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"ModelConfig",
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"TrainingConfig",
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"CrossValidationConfig",
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"EvaluationConfig",
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"OptunaConfig",
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"WaveletTransformConfig",
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"ClippingConfig",
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]
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@ -1,62 +1,151 @@
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from pydantic import BaseModel, Field
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from typing import Optional, List, Union
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from pydantic import BaseModel, Field, field_validator, model_validator
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from typing import Optional, List, Union, Literal
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from enum import Enum
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# --- Nested Configs ---
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class WaveletTransformConfig(BaseModel):
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"""Configuration for optional wavelet transform features."""
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apply: bool = False
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target_or_feature: str = "target"
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target_or_feature: Literal['target', 'feature'] = "target"
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wavelet_type: str = "db4"
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level: int = 3
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level: int = Field(3, gt=0) # Level must be positive
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use_coeffs: List[str] = ["approx", "detail_1"]
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class ClippingConfig(BaseModel):
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"""Configuration for optional feature clipping."""
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apply: bool = False
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clip_min: float = -5.0
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clip_max: float = 5.0
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@model_validator(mode='after')
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def check_clip_range(self) -> 'ClippingConfig':
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if self.apply and self.clip_max <= self.clip_min:
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raise ValueError(f'clip_max ({self.clip_max}) must be greater than clip_min ({self.clip_min}) when clipping is applied.')
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return self
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# --- Main Config Sections ---
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class DataConfig(BaseModel):
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data_path: str
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datetime_col: str
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target_col: str
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"""Configuration related to data loading and initial preparation."""
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data_path: str = Field(..., description="Path to the input CSV data file.")
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# --- Raw Data Specifics ---
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raw_datetime_col: str = Field(..., description="Name of the raw datetime column in the CSV (e.g., 'MTU (CET/CEST)')")
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raw_target_col: str = Field(..., description="Name of the raw target/price column in the CSV (e.g., 'Day-ahead Price [EUR/MWh]')")
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raw_datetime_format: str = '%d.%m.%Y %H:%M' # Example, make it configurable if needed
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# --- Standardized Names & Processing ---
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datetime_col: str = Field(..., description="Standardized name for the datetime index after processing (e.g., 'Timestamp')")
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target_col: str = Field(..., description="Standardized name for the target column after processing (e.g., 'Price')")
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expected_frequency: Optional[str] = Field('h', description="Expected pandas frequency string (e.g., 'h', 'D', '15min'). If null, no frequency check/setting is performed.")
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fill_initial_target_nans: bool = Field(True, description="Forward/backward fill NaNs in the target column immediately after loading?")
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class FeatureConfig(BaseModel):
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sequence_length: int
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forecast_horizon: int
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lags: List[int]
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rolling_window_sizes: List[int]
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use_time_features: bool
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scaling_method: Optional[str] = None
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"""Configuration for feature engineering and preprocessing."""
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sequence_length: int = Field(..., gt=0)
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forecast_horizon: int = Field(..., gt=0)
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lags: List[int] = []
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rolling_window_sizes: List[int] = []
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use_time_features: bool = True
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sinus_curve: bool = False # Added
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cosin_curve: bool = False # Added
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wavelet_transform: Optional[WaveletTransformConfig] = None
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fill_nan: Optional[Union[str, float, int]] = 'ffill' # Added (e.g., 'ffill', 0)
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clipping: ClippingConfig = ClippingConfig() # Default instance
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scaling_method: Optional[Literal['standard', 'minmax']] = 'standard' # Added literal validation
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@field_validator('lags', 'rolling_window_sizes')
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@classmethod
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def check_positive_list_values(cls, v: List[int]) -> List[int]:
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if any(val <= 0 for val in v):
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raise ValueError('Lists lags/rolling_window_sizes must contain only positive values')
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return v
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class ModelConfig(BaseModel):
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input_size: Optional[int] = None # Will be calculated
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hidden_size: int
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num_layers: int
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dropout: float
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"""Configuration for the forecasting model architecture."""
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# input_size: Optional[int] = Field(None, gt=0) # Removed: Determined dynamically
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hidden_size: int = Field(..., gt=0)
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num_layers: int = Field(..., gt=0)
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dropout: float = Field(..., ge=0.0, le=1.0)
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use_residual_skips: bool = False
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output_size: Optional[int] = None # Will be calculated
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# Add forecast_horizon here to ensure LightningModule gets it directly
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forecast_horizon: Optional[int] = Field(None, gt=0) # Will be set from FeatureConfig
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class TrainingConfig(BaseModel):
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batch_size: int
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epochs: int
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learning_rate: float
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loss_function: str
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device: str
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early_stopping_patience: Optional[int] = None
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scheduler_step_size: Optional[int] = None
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scheduler_gamma: Optional[float] = None
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"""Configuration for the training process (PyTorch Lightning)."""
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batch_size: int = Field(..., gt=0)
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epochs: int = Field(..., gt=0) # Max epochs
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learning_rate: float = Field(..., gt=0.0)
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loss_function: Literal['MSE', 'MAE'] = 'MSE'
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# device: str = 'auto' # Handled by PL Trainer accelerator/devices args
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early_stopping_patience: Optional[int] = Field(None, ge=1) # Patience must be >= 1 if set
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scheduler_step_size: Optional[int] = Field(None, gt=0)
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scheduler_gamma: Optional[float] = Field(None, gt=0.0, lt=1.0)
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gradient_clip_val: Optional[float] = Field(None, ge=0.0) # Added
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num_workers: int = Field(0, ge=0) # Added
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precision: Literal[16, 32, 64, 'bf16'] = 32 # Added
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class CrossValidationConfig(BaseModel):
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n_splits: int
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test_size_fraction: float
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val_size_fraction: float
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initial_train_size: Optional[Union[int, float]] = None
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"""Configuration for time series cross-validation."""
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n_splits: int = Field(..., gt=0)
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test_size_fraction: float = Field(..., gt=0.0, lt=1.0, description="Fraction of the fixed training window size for the test set.")
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val_size_fraction: float = Field(..., gt=0.0, lt=1.0, description="Fraction of the fixed training window size for the validation set.")
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initial_train_size: Optional[Union[int, float]] = Field(None, gt=0.0, description="Size of the fixed training window (absolute number or fraction of total data > 0). If null, estimated automatically.")
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class EvaluationConfig(BaseModel):
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metrics: List[str]
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eval_batch_size: int
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save_plots: bool
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plot_sample_size: int
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"""Configuration for the final evaluation process."""
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# metrics: List[str] = ['MAE', 'RMSE'] # Defined internally now
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eval_batch_size: int = Field(..., gt=0)
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save_plots: bool = True
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plot_sample_size: Optional[int] = Field(1000, gt=0) # Max points for plots
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class OptunaConfig(BaseModel):
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"""Optional configuration for Optuna hyperparameter optimization."""
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enabled: bool = False
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n_trials: int = Field(20, gt=0)
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storage: Optional[str] = None # e.g., "sqlite:///output/hpo_results/study.db"
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direction: Literal['minimize', 'maximize'] = 'minimize'
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metric_to_optimize: str = 'val_mae_orig_scale'
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pruning: bool = True
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# --- Top-Level Configuration Model ---
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class MainConfig(BaseModel):
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"""Main configuration model nesting all sections."""
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project_name: str = "TimeSeriesForecasting"
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random_seed: Optional[int] = 42 # Added top-level seed
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data: DataConfig
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features: FeatureConfig
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model: ModelConfig
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model: ModelConfig # ModelConfig no longer contains input_size
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training: TrainingConfig
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cross_validation: CrossValidationConfig
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evaluation: EvaluationConfig
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evaluation: EvaluationConfig
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optuna: Optional[OptunaConfig] = OptunaConfig() # Added optional Optuna config
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@model_validator(mode='after')
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def check_forecast_horizon_consistency(self) -> 'MainConfig':
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# Ensure model config gets forecast horizon from features config if not set
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if self.features and self.model:
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if self.model.forecast_horizon is None:
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# If model config doesn't have it, set it from features config
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self.model.forecast_horizon = self.features.forecast_horizon
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elif self.model.forecast_horizon != self.features.forecast_horizon:
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# If both are set but differ, raise error
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raise ValueError(
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f"ModelConfig forecast_horizon ({self.model.forecast_horizon}) must match "
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f"FeatureConfig forecast_horizon ({self.features.forecast_horizon})."
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)
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# After potential setting, ensure model.forecast_horizon is actually set
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if self.model and (self.model.forecast_horizon is None or self.model.forecast_horizon <= 0):
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raise ValueError("ModelConfig requires a positive forecast_horizon (must be set in features config if not set explicitly in model config).")
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# Input size check is removed as it's not part of static config anymore
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return self
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class Config:
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# Example configuration for Pydantic itself
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validate_assignment = True # Re-validate on assignment
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# extra = 'forbid' # Forbid extra fields not defined in schema
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