init

forecasting_model/trainer.py

@@ -0,0 +1,50 @@
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from typing import Optional, Dict, Any
+from ..utils.config_model import TrainingConfig
+
+class Trainer:
+    def __init__(
+        self,
+        model: nn.Module,
+        train_loader: DataLoader,
+        val_loader: DataLoader,
+        loss_fn: nn.Module,
+        device: torch.device,
+        config: TrainingConfig,
+        scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
+        target_scaler: Optional[Any] = None
+    ):
+        self.model = model
+        self.train_loader = train_loader
+        self.val_loader = val_loader
+        self.loss_fn = loss_fn
+        self.device = device
+        self.config = config
+        self.scheduler = scheduler
+        self.target_scaler = target_scaler
+        
+        # TODO: Initialize optimizer (Adam)
+        # TODO: Initialize early stopping if configured
+
+    def train_epoch(self) -> Dict[str, float]:
+        """
+        Train for one epoch.
+        """
+        # TODO: Implement training loop for one epoch
+        pass
+
+    def evaluate(self, loader: DataLoader) -> Dict[str, float]:
+        """
+        Evaluate model on given data loader.
+        """
+        # TODO: Implement evaluation with metrics on original scale
+        pass
+
+    def train(self) -> Dict[str, Any]:
+        """
+        Main training loop with validation and early stopping.
+        """
+        # TODO: Implement full training loop with validation
+        pass