def prepare_data():
static_data = write_database()
project_data_manager = ProjectsDataManager(static_data, is_test=False)
nwp_response = project_data_manager.nwp_extractor()
if nwp_response == DONE:
data_response = project_data_manager.create_datasets()
else:
raise RuntimeError('Something was going wrong with nwp extractor')
if data_response == DONE:
project_data_manager.create_projects_relations()
else:
raise RuntimeError('Something was going wrong with data manager')
if hasattr(project_data_manager, 'data_eval'):
project_data_manager.is_test = True
nwp_response = project_data_manager.nwp_extractor()
if nwp_response == DONE:
nwp_response = project_data_manager.create_datasets()
if nwp_response != DONE:
raise RuntimeError(
'Something was going wrong with on evaluation dataset creator'
)
else:
raise RuntimeError(
'Something was going wrong with nwp extractor on evaluation')
print("Data is prepared, training can start")
def prepare_data():
static_data = write_database()
project_data_manager = ProjectsDataManager(static_data)
nwp_response = project_data_manager.nwp_extractor(test=False)
if nwp_response == 'Done':
data_response = project_data_manager.create_datasets(test=False)
else:
raise RuntimeError('Something was going wrong with NWP extractor')
if data_response == 'Done':
project_data_manager.create_projects_relations()
else:
raise RuntimeError('Something was going wrong with data manager')
if hasattr(project_data_manager, 'data_eval'):
nwp_response = project_data_manager.nwp_extractor(test=True)
if nwp_response == 'Done':
_ = project_data_manager.create_datasets(test=True)
else:
raise RuntimeError(
'Something was going wrong with NWP extractor on evaluation')
def backup_project():
static_data = write_database()
project_backup_manager = ProjectsTrainManager(static_data)
project_backup_manager.clear_backup_projects()
def eval_project():
static_data = write_database()
project_eval_manager = ProjectsEvalManager(static_data)
project_eval_manager.evaluate()
def train_project():
static_data = write_database()
project_train_manager = ProjectsTrainManager(static_data)
project_train_manager.fit()