def get_train_configured_instance_operation(self, cfg_fname=g_config_filename):
"""
return configured instances and operation.
"""
ml_instance_dict = dict()
ml_enable_list = []
train_oper_dict = dict() # key : first_ml, second_ml, .... value : operation unit list
config = cp.ConfigParser()
config.read(cfg_fname)
# self.ml_num = int(config['ML_Process']['ml_num'])
# self.ml_name_list = config['ML_Process']['ml_names'] \
# .replace(' ','').split(',')
# every ml config section gets machine learning class instance
for section, entries in config.items() :
try :
if section.split('_')[1] == 'ML' and entries['enable'] == 'true':
ml_enable_list.append(section.lower())
ml_instance = get_classes.class_dict[entries['ML_NAME']]() # get class instance
ml_instance.set_config(ml_instance, section_num = section[0], arg_dict = entries) # set each config
ml_instance.set_proper_config_type()
ml_instance_dict[section.lower()] = ml_instance # section.lower() = 1st_ML, 2nd_ML, ...
# when the machine learning written in config file doesn't exist, exception process is needed.
except IndexError:
pass
# get train_operations assing func as according to their type
train_operations_dict = config['Train_Operations'] # key:1st_ml; value:create, train, ..
for ml_order, train_operations_str in train_operations_dict.items():
if ml_order in ml_enable_list:
train_operations_list = train_operations_str.replace(' ', '').split(',')
train_oper_dict[ml_order.lower()] = []
for oper in train_operations_list:
train_oper_dict[ml_order.lower()].append(op.operation_unit(oper)) # set each operation and input path as operation_unit
return (ml_instance_dict, train_oper_dict)
def config(self, config_fname='config'):
config = cp.ConfigParser()
config.read(config_fname)
self.model_num = int(config['ML_Process']['model_num'])
self.model_name_list = config['ML_Process']['model_names'] \
.replace(' ','').split(',')
for section, entries in config.items(): # get model instance
try:
if section.split(
'_')[1] == 'MODEL' and entries['enable'] == 'true':
# print(items['model_name'])
model = library.class_obj_dict[entries['model_name']]()
model.set_config(arg_dict=entries)
self.model_dict[section.lower()] = model
# config 파일에 적힌 모델이 없는 경우에 대한 예외 처리 필요
except IndexError:
pass
predict_operations_list = config['Predict_operations']['predict_operations'] \
.replace(' ', '').split(',')
for oper in predict_operations_list:
self.predict_oper_list.append(op.operation_unit(oper))
train_operations_dict = config[
'Train_operations'] # key : first_model value : D:"", T"", O"" ...
for model_order, train_operations_str in train_operations_dict.items():
train_operations_list = train_operations_str.replace(' ',
'').split(',')
self.train_oper_dict[model_order] = []
for oper in train_operations_list:
self.train_oper_dict[model_order].append(
op.operation_unit(oper))
self.print_config_all()