"""
Time Series Forecasting Module with LSTM

This module provides a configurable PyTorch-based LSTM model for time series forecasting,
with support for feature engineering, cross-validation, and evaluation.
"""

__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 forecasting_model.train.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
]