price_forecasting_ml/train_predict.py

from modules.transform import transformData, prepareData
from train import trainModel
from pathlib import Path
import pickle
import uuid
import os

def main():
    import logging

    directory = Path(__file__).parent.absolute()
    logging.basicConfig(level=logging.INFO)
    run_id = str(uuid.uuid4())
    artifacts_path = os.path.join(directory, 'artifacts', run_id)
    logging.info(f'Created forecasting pipeline with id {run_id}')
    os.mkdir(artifacts_path)

    prepared_data = prepareData(dir=directory, id=run_id)
    train_data, transformations = transformData(prepared_data, dir=directory, id=run_id)
    train_data.to_csv(os.path.join(artifacts_path, 'transformed_dataset.csv'))

    
    # Save transformations including StandardScaler objects
    with open(os.path.join(artifacts_path, 'transformations.pkl'), 'wb') as fp:
        pickle.dump(transformations, fp)

    nf, results = trainModel(dataset=train_data, artifacts_path=artifacts_path)

    results.to_csv(os.path.join(artifacts_path, 'training_results.csv'))

    nf.save(path=os.path.join(artifacts_path, 'model'),
        model_index=None, 
        overwrite=True,
        save_dataset=True)

if __name__ == "__main__":
    main()