def __init__(self, aws_operations=None, logger=None):
self.log_file = 'training_logs/training_data_preprocessing_log.txt'
if aws_operations is not None:
self.aws_operations = aws_operations
else:
self.aws_operations = aws_storage_operations()
if logger is not None:
self.logger = logger
else:
self.logger = aws_logger(self.aws_operations)
def __init__(self, aws_operations=None, logger=None):
self.prediction_file_path = 'prediction_file_from_db/input_file_for_prediction.csv'
self.log_file = 'prediction_logs/prediction_data_loading_log.txt'
if aws_operations is not None:
self.aws_operations = aws_operations
else:
self.aws_operations = aws_storage_operations()
if logger is not None:
self.logger = logger
else:
self.logger = aws_logger(self.aws_operations)
def __init__(self, aws_operations=None, logger=None):
self.model_directory = 'models/'
self.log_file = 'model_operations_logs/model_storage_log.txt'
if aws_operations is not None:
self.aws_operations = aws_operations
else:
self.aws_operations = aws_storage_operations()
if logger is not None:
self.logger = logger
else:
self.logger = aws_logger(self.aws_operations)
def __init__(self, good_data_path, bad_data_path, aws_operations=None, logger=None):
self.good_data_path = good_data_path
self.bad_data_path = bad_data_path
self.log_file = 'training_logs/training_data_transformation_log.txt'
if aws_operations is not None:
self.aws_operations = aws_operations
else:
self.aws_operations = aws_storage_operations()
if logger is not None:
self.logger = logger
else:
self.logger = aws_logger(self.aws_operations)
def __init__(self):
self.prediction_output_path = 'prediction_results/predictions.csv'
self.log_file = 'prediction_logs/prediction_from_model_log.txt'
self.aws_operations = aws_storage_operations()
self.logger = aws_logger(self.aws_operations)
self.model_operations = model_storage_operations(
self.aws_operations, self.logger)
self.data_loading = load_prediction_data(self.aws_operations,
self.logger)
self.data_preprocessing = preprocess_prediction_data(
self.aws_operations, self.logger)
self.data_clustering = cluster_prediction_data(self.aws_operations,
self.logger,
self.model_operations)
def __init__(self, aws_operations=None, logger=None):
self.test_size = 0.25
self.random_state = 123
self.rfc = RandomForestClassifier(random_state=self.random_state)
self.xgbc = XGBClassifier(objective='binary:logistic',
random_state=self.random_state)
self.log_file = 'training_logs/best_model_selection_log.txt'
if aws_operations is not None:
self.aws_operations = aws_operations
else:
self.aws_operations = aws_storage_operations()
if logger is not None:
self.logger = logger
else:
self.logger = aws_logger(self.aws_operations)
def __init__(self, good_data_path, aws_operations=None, logger=None):
self.good_data_path = good_data_path
self.username = '******'
self.password = '******'
self.db_name = 'waferfaultdetectiondb'
self.csv_file_path = 'training_file_from_db/input_file_for_training.csv'
self.log_file = 'training_logs/training_data_insertion_log.txt'
if aws_operations is not None:
self.aws_operations = aws_operations
else:
self.aws_operations = aws_storage_operations()
if logger is not None:
self.logger = logger
else:
self.logger = aws_logger(self.aws_operations)
def __init__(self):
self.log_file = 'training_logs/model_training_log.txt'
self.aws_operations = aws_storage_operations()
self.logger = aws_logger(self.aws_operations)
self.model_operations = model_storage_operations(
self.aws_operations, self.logger)
self.data_loading = load_training_data(self.aws_operations,
self.logger)
self.data_preprocessing = preprocess_training_data(
self.aws_operations, self.logger)
self.data_clustering = cluster_training_data(self.aws_operations,
self.logger,
self.model_operations)
self.model_selection = select_best_model(self.aws_operations,
self.logger)
def __init__(self, folder_path):
self.collection_name = 'prediction_data'
self.good_data_path = 'prediction_data_validated/good_data/'
self.bad_data_path = 'prediction_data_validated/bad_data/'
self.log_file = 'prediction_logs/prediction_data_validation_and_insertion_log.txt'
self.aws_operations = aws_storage_operations()
self.logger = aws_logger(self.aws_operations)
self.data_validation = validate_prediction_data(
folder_path, self.good_data_path, self.bad_data_path,
self.aws_operations, self.logger)
self.data_transformation = transform_prediction_data(
self.good_data_path, self.bad_data_path, self.aws_operations,
self.logger)
self.data_insertion = insert_prediction_data(self.good_data_path,
self.aws_operations,
self.logger)
def __init__(self,
aws_operations=None,
logger=None,
model_operations=None):
self.log_file = 'prediction_logs/prediction_data_clustering_log.txt'
if aws_operations is not None:
self.aws_operations = aws_operations
else:
self.aws_operations = aws_storage_operations()
if logger is not None:
self.logger = logger
else:
self.logger = aws_logger(self.aws_operations)
if model_operations:
self.model_operations = model_operations
else:
self.model_operations = model_storage_operations(
self.aws_operations, self.logger)
def __init__(self,
aws_operations=None,
logger=None,
model_operations=None):
self.plot_file_path = 'elbow_plot.PNG'
self.random_state = 123
self.log_file = 'training_logs/training_data_clustering_log.txt'
if aws_operations is not None:
self.aws_operations = aws_operations
else:
self.aws_operations = aws_storage_operations()
if logger is not None:
self.logger = logger
else:
self.logger = aws_logger(self.aws_operations)
if model_operations:
self.model_operations = model_operations
else:
self.model_operations = model_storage_operations(
self.aws_operations, self.logger)
def __init__(self,
folder_path,
good_data_path,
bad_data_path,
aws_operations=None,
logger=None):
self.batch_directory = folder_path
self.good_data_path = good_data_path
self.bad_data_path = bad_data_path
self.schema_path = 'schema_training.json'
self.archive_folder_path = 'training_data_archive/'
self.log_file = 'training_logs/training_data_validation_log.txt'
if aws_operations is not None:
self.aws_operations = aws_operations
else:
self.aws_operations = aws_storage_operations()
if logger is not None:
self.logger = logger
else:
self.logger = aws_logger(self.aws_operations)
