machinelearningproject/main.py

from src.datascience import logger
from src.datascience.pipeline.data_ingestion_pipeline import DataIngestionTrainingPipeline
from src.datascience.pipeline.data_validation_pipeline import DataValidationTrainingPipeline
from src.datascience.pipeline.data_transformation_pipeline import DataTransformationTrainingPipeline
from src.datascience.pipeline.model_trainer_pipeline import ModelTrainerTrainingPipeline
from src.datascience.pipeline.model_evaluation_pipeline import ModelEvaluationTrainingPipeline

STAGE_NAME = "Data Ingestion stage"
try:
   logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<") 
   data_ingestion = DataIngestionTrainingPipeline()
   data_ingestion.initiate_data_ingestion()
   logger.info(f">>>>>> stage {STAGE_NAME} completed <<<<<<\n\nx==========x")
except Exception as e:
        logger.exception(e)
        raise e


STAGE_NAME = "Data Validation stage"
try:
   logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<") 
   data_ingestion = DataValidationTrainingPipeline()
   data_ingestion.initiate_data_validation()
   logger.info(f">>>>>> stage {STAGE_NAME} completed <<<<<<\n\nx==========x")
except Exception as e:
        logger.exception(e)
        raise e


STAGE_NAME = "Data Transformation stage"
try:
   logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<") 
   data_ingestion = DataTransformationTrainingPipeline()
   data_ingestion.initiate_data_transformation()
   logger.info(f">>>>>> stage {STAGE_NAME} completed <<<<<<\n\nx==========x")
except Exception as e:
        logger.exception(e)
        raise e


STAGE_NAME = "Model Trainer stage"
try:
   logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<") 
   data_ingestion = ModelTrainerTrainingPipeline()
   data_ingestion.initiate_model_training()
   logger.info(f">>>>>> stage {STAGE_NAME} completed <<<<<<\n\nx==========x")
except Exception as e:
        logger.exception(e)
        raise e

STAGE_NAME = "Model evaluation stage"
try:
   logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<") 
   data_ingestion = ModelEvaluationTrainingPipeline()
   data_ingestion.initiate_model_evaluation()
   logger.info(f">>>>>> stage {STAGE_NAME} completed <<<<<<\n\nx==========x")
except Exception as e:
        logger.exception(e)
        raise e