intermediate backup

forecasting_model/__init__.py

@@ -5,4 +5,39 @@ This module provides a configurable PyTorch-based LSTM model for time series for
 with support for feature engineering, cross-validation, and evaluation.
 """
 
-__version__ = "0.1.0" 
+__version__ = "0.1.0"
+
+# Expose core components for easier import
+from .data_processing import (
+    load_raw_data,
+    engineer_features,
+    TimeSeriesCrossValidationSplitter,
+    prepare_fold_data_and_loaders,
+    TimeSeriesDataset
+)
+from .model import LSTMForecastLightningModule
+from .evaluation import (
+    evaluate_fold_predictions,
+    # Optionally expose the standalone evaluation utility if needed externally
+    # evaluate_model_on_fold_test_set
+)
+
+# Expose main configuration class from utils
+from .utils import MainConfig
+
+# Expose the main execution script function if it's intended to be callable as a function
+# from .forecasting_model import run # Assuming the main script is named forecasting_model.py
+
+# Define __all__ for explicit public API (optional but good practice)
+__all__ = [
+    "load_raw_data",
+    "engineer_features",
+    "TimeSeriesCrossValidationSplitter",
+    "prepare_fold_data_and_loaders",
+    "TimeSeriesDataset",
+    "LSTMForecastLightningModule",
+    "evaluate_fold_predictions",
+    # "evaluate_model_on_fold_test_set", # Uncomment if exposed
+    "MainConfig",
+    # "run", # Uncomment if exposed
+]