def exec(self, task_input, task_output):
self._prep_dir(task_input["model_loc"])
model_path = os.path_join(task_input["model_loc"],
f'model-{task_input["inputs"]}')
if not task_input["model_override"] and os.is_dir(model_path):
#TODO implement a dynamic "path increment" model-name save functionality
pass
task_output["model"].save(model_path)
def exec(self, task_input, task_output):
ressources, training_data, backup = self._setup(task_output)
with open(training_data, 'w') as training:
if backup is not None and os.is_path_file(backup):
self._merge_training(training, backup)
else:
headders = self._get_headders(task_output["readings"])
training.write(headders)
for split_folder in ressources:
split_dir = os.path_join(task_output["res_loc"], split_folder)
if os.is_path_file(split_dir) and not os.is_dir(split_dir):
continue
self.append_training_set(training, split_dir,
os.dir_res_list(split_dir))
os.remove_dir(split_dir)