init

forecasting_model/evaluation.py

@@ -0,0 +1,82 @@
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+from typing import Dict, Any, Optional
+from utils.config_model import EvaluationConfig
+
+def calculate_mae(y_true: np.ndarray, y_pred: np.ndarray) -> float:
+    """
+    Calculate Mean Absolute Error.
+    """
+    # TODO: Implement MAE calculation
+    pass
+
+def calculate_rmse(y_true: np.ndarray, y_pred: np.ndarray) -> float:
+    """
+    Calculate Root Mean Squared Error.
+    """
+    # TODO: Implement RMSE calculation
+    pass
+
+def plot_predictions_vs_actual(
+    y_true: np.ndarray,
+    y_pred: np.ndarray,
+    title_suffix: str,
+    filename: str,
+    max_points: Optional[int] = None
+) -> None:
+    """
+    Create line plot of predictions vs actual values.
+    """
+    # TODO: Implement prediction vs actual plot
+    pass
+
+def plot_scatter_predictions(
+    y_true: np.ndarray,
+    y_pred: np.ndarray,
+    title_suffix: str,
+    filename: str
+) -> None:
+    """
+    Create scatter plot of predictions vs actual values.
+    """
+    # TODO: Implement scatter plot
+    pass
+
+def plot_residuals_time(
+    residuals: np.ndarray,
+    title_suffix: str,
+    filename: str,
+    max_points: Optional[int] = None
+) -> None:
+    """
+    Create plot of residuals over time.
+    """
+    # TODO: Implement residuals time plot
+    pass
+
+def plot_residuals_distribution(
+    residuals: np.ndarray,
+    title_suffix: str,
+    filename: str
+) -> None:
+    """
+    Create histogram/KDE of residuals.
+    """
+    # TODO: Implement residuals distribution plot
+    pass
+
+def evaluate_fold(
+    model: torch.nn.Module,
+    test_loader: DataLoader,
+    loss_fn: torch.nn.Module,
+    device: torch.device,
+    target_scaler: Any,
+    eval_config: EvaluationConfig,
+    fold_num: int
+) -> Dict[str, float]:
+    """
+    Evaluate model on test set and generate plots.
+    """
+    # TODO: Implement full evaluation pipeline
+    pass