# set up running log

n_degree, n_hop = process_sampling_numbers(args.n_degree, args.n_hop)

n_degree = [str(n) for n in n_degree]

runtime_id = '{}-{}-{}-{}-{}-{}'.format(str(time.time()), args.data, args.mode[0], n_hop, 'k'.join(n_degree), args.pos_dim)

logging.basicConfig(level=logging.INFO)

logger = logging.getLogger()

logger.setLevel(logging.DEBUG)

file_path = 'log/{}.log'.format(runtime_id) #TODO: improve log name

fh = logging.FileHandler(file_path)

fh.setLevel(logging.DEBUG)

ch = logging.StreamHandler()

ch.setLevel(logging.WARN)

formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')

fh.setFormatter(formatter)

ch.setFormatter(formatter)

logger.addHandler(fh)

logger.addHandler(ch)

logger.info('Create log file at {}'.format(file_path))

logger.info('Command line executed: python ' + ' '.join(sys_argv))

logger.info('Full args parsed:')

logger.info(args)

# set up model parameters log

checkpoint_root = './saved_checkpoints/'

checkpoint_dir = checkpoint_root + runtime_id + '/'

best_model_root = './best_models/'

best_model_dir = best_model_root + runtime_id + '/'

if not os.path.exists(checkpoint_root):

os.mkdir(checkpoint_root)

logger.info('Create directory {}'.format(checkpoint_root))

if not os.path.exists(best_model_root):

os.mkdir(best_model_root)

logger.info('Create directory'.format(best_model_root))

os.mkdir(checkpoint_dir)

os.mkdir(best_model_dir)

logger.info('Create checkpoint directory {}'.format(checkpoint_dir))

logger.info('Create best model directory {}'.format(best_model_dir))

get_checkpoint_path = lambda epoch: (checkpoint_dir + 'checkpoint-epoch-{}.pth'.format(epoch))

get_ngh_store_path = lambda epoch, layer: (checkpoint_dir + 'ngh-store-epoch-{}-layer-{}.pth'.format(epoch, layer))

get_self_rep_path = lambda epoch: (checkpoint_dir + 'self-rep-epoch-{}.pth'.format(epoch))

get_prev_raw_path = lambda epoch: (checkpoint_dir + 'prev_raw-epoch-{}.pth'.format(epoch))

best_model_path = best_model_dir + 'best-model.pth'

best_model_ngh_store_path = best_model_dir + 'best-model-ngh_store.pth'

n_degree, n_hop = process_sampling_numbers(args.n_degree, args.n_hop)

n_degree = [str(n) for n in n_degree]

with open(dir+'oneline_results.txt', 'a') as f:

elements = [str(e) for e in [args.data, args.mode[0], n_hop, 'k'.join(n_degree), args.pos_dim, *[str(v)[:6] for v in test_results]]]