init

forecasting_model/utils/config_model.py

@@ -0,0 +1,62 @@
+from pydantic import BaseModel, Field
+from typing import Optional, List, Union
+from enum import Enum
+
+class WaveletTransformConfig(BaseModel):
+    apply: bool = False
+    target_or_feature: str = "target"
+    wavelet_type: str = "db4"
+    level: int = 3
+    use_coeffs: List[str] = ["approx", "detail_1"]
+
+class DataConfig(BaseModel):
+    data_path: str
+    datetime_col: str
+    target_col: str
+
+class FeatureConfig(BaseModel):
+    sequence_length: int
+    forecast_horizon: int
+    lags: List[int]
+    rolling_window_sizes: List[int]
+    use_time_features: bool
+    scaling_method: Optional[str] = None
+    wavelet_transform: Optional[WaveletTransformConfig] = None
+
+class ModelConfig(BaseModel):
+    input_size: Optional[int] = None  # Will be calculated
+    hidden_size: int
+    num_layers: int
+    dropout: float
+    use_residual_skips: bool = False
+    output_size: Optional[int] = None  # Will be calculated
+
+class TrainingConfig(BaseModel):
+    batch_size: int
+    epochs: int
+    learning_rate: float
+    loss_function: str
+    device: str
+    early_stopping_patience: Optional[int] = None
+    scheduler_step_size: Optional[int] = None
+    scheduler_gamma: Optional[float] = None
+
+class CrossValidationConfig(BaseModel):
+    n_splits: int
+    test_size_fraction: float
+    val_size_fraction: float
+    initial_train_size: Optional[Union[int, float]] = None
+
+class EvaluationConfig(BaseModel):
+    metrics: List[str]
+    eval_batch_size: int
+    save_plots: bool
+    plot_sample_size: int
+
+class MainConfig(BaseModel):
+    data: DataConfig
+    features: FeatureConfig
+    model: ModelConfig
+    training: TrainingConfig
+    cross_validation: CrossValidationConfig
+    evaluation: EvaluationConfig