NetworkSecurity / networksecurity /entity /config_entity.py
Inder-26
Cloud pushed and s3 data storage implemented
8268752
from datetime import datetime
import os
from networksecurity.constant import training_pipeline
print(training_pipeline.PIPELINE_NAME)
print(training_pipeline.ARTIFACT_DIR)
class TrainingPipelineConfig:
def __init__(self,timestamp=datetime.now()):
timestamp=timestamp.strftime("%m_%d_%Y_%H_M_%S")
self.pipeline_name = training_pipeline.PIPELINE_NAME
self.artifact_name = training_pipeline.ARTIFACT_DIR
self.artifact_dir = os.path.join(self.artifact_name,timestamp)
self.model_dir = os.path.join("final_model")
self.timestamp: str = timestamp
class DataIngestionConfig:
def __init__(self,training_pipeline_config: TrainingPipelineConfig):
self.data_ingestion_dir:str = os.path.join(
training_pipeline_config.artifact_dir,training_pipeline.DATA_INGESTION_DIR_NAME
)
self.feature_store_file_path: str = os.path.join(
self.data_ingestion_dir, training_pipeline.DATA_INGESTION_FEATURE_STORE_DIR, training_pipeline.FILE_NAME
)
self.training_file_path: str = os.path.join(
self.data_ingestion_dir, training_pipeline.DATA_INGESTION_INGESTED_DIR, training_pipeline.TRAIN_FILE_NAME
)
self.testing_file_path: str = os.path.join(
self.data_ingestion_dir, training_pipeline.DATA_INGESTION_INGESTED_DIR, training_pipeline.TEST_FILE_NAME
)
self.train_test_split_ratio: float = training_pipeline.DATA_INGESTION_TRAIN_TEST_SPLIT_RATION
self.collection_name: str = training_pipeline.DATA_INGESTION_COLLECTION_NAME
self.database_name: str = training_pipeline.DATA_INGESTION_DATABASE_NAME
class DataValidationConfig:
def __init__(self, training_pipeline_config: TrainingPipelineConfig):
# Main data validation directory
self.data_validation_dir: str = os.path.join(
training_pipeline_config.artifact_dir,
training_pipeline.DATA_VALIDATION_DIR_NAME
)
# Valid data directory
self.valid_data_dir: str = os.path.join(
self.data_validation_dir,
training_pipeline.DATA_VALIDATION_VALID_DIR
)
# Invalid data directory
self.invalid_data_dir: str = os.path.join(
self.data_validation_dir,
training_pipeline.DATA_VALIDATION_INVALID_DIR
)
# Valid train file path
self.valid_train_file_path: str = os.path.join(
self.valid_data_dir,
training_pipeline.TRAIN_FILE_NAME
)
# Valid test file path
self.valid_test_file_path: str = os.path.join(
self.valid_data_dir,
training_pipeline.TEST_FILE_NAME
)
# Invalid train file path
self.invalid_train_file_path: str = os.path.join(
self.invalid_data_dir,
training_pipeline.TRAIN_FILE_NAME
)
# Invalid test file path
self.invalid_test_file_path: str = os.path.join(
self.invalid_data_dir,
training_pipeline.TEST_FILE_NAME
)
# Drift report file path
self.drift_report_file_path: str = os.path.join(
self.data_validation_dir,
training_pipeline.DATA_VALIDATION_DRIFT_REPORT_DIR,
training_pipeline.DATA_VALIDATION_DRIFT_REPORT_FILE_NAME,
)
class DataTransformationConfig:
def __init__(self, training_pipeline_config: TrainingPipelineConfig):
self.data_transformation_dir: str = os.path.join(
training_pipeline_config.artifact_dir,
training_pipeline.DATA_TRANSFORMATION_DIR_NAME
)
self.transformed_train_file_path: str = os.path.join(
self.data_transformation_dir,
training_pipeline.DATA_TRANSFORMATION_TRANSFORMED_DATA_DIR,
training_pipeline.TRAIN_FILE_NAME.replace("csv", "npy")
)
self.transformed_test_file_path: str = os.path.join(
self.data_transformation_dir,
training_pipeline.DATA_TRANSFORMATION_TRANSFORMED_DATA_DIR,
training_pipeline.TEST_FILE_NAME.replace("csv", "npy")
)
self.transformed_object_file_path: str = os.path.join(
self.data_transformation_dir,
training_pipeline.DATA_TRANSFORMATION_TRANSFORMED_OBJECT_DIR,
training_pipeline.PREPROCESSING_OBJECT_FILE_NAME
)
class ModelTrainerConfig:
def __init__(self, training_pipeline_config: TrainingPipelineConfig):
self.model_trainer_dir: str = os.path.join(
training_pipeline_config.artifact_dir, training_pipeline.MODEL_TRAINER_DIR_NAME
)
self.trained_model_file_path: str = os.path.join(
self.model_trainer_dir, training_pipeline.MODEL_TRAINER_TRAINED_MODEL_DIR,
training_pipeline.MODEL_FILE_NAME
)
self.expected_accuracy: float = training_pipeline.MODEL_TRAINER_EXPECTED_SCORE
self.overfitting_underfitting_threshold = training_pipeline.MODEL_TRAINER_OVER_FITTING_UNDER_FITTING_THRESHOLD