import os
import time
from logging import getLogger
from tools.trains.t002_lgb import t002_lgb_train as train
from tools.utils.args import parse_train_args
from tools.utils.configs import load_configs
from tools.utils.logs import logInit, send_line_notification
CONFIG_FILE = './configs/c044.yml'
if __name__ == '__main__':
t0 = time.time()
script_name = os.path.basename(__file__).split('.')[0]
log_file = script_name + '.log'
logger = getLogger(__name__)
logger = logInit(logger, './mnt/logs/', log_file)
args = parse_train_args(logger)
configs = load_configs(CONFIG_FILE, logger)
auc_mean, auc_std = train(args, script_name, configs, logger)
prec_time = time.time() - t0
send_line_notification(f'Finished: {script_name} '
f'using CONFIG: {CONFIG_FILE} '
f'w/ AUC {auc_mean:.5f}+-{auc_std:.5f} '
f'in {prec_time:.1f} s !')
seed_everything(71)
CONFIG_DIR = './configs/exp_configs/'
if __name__ == '__main__':
t0 = time.time()
script_name = os.path.basename(__file__).split('.')[0]
args = parse_args(None)
exp_id = args.exp_id
log_file = f'{script_name}_{exp_id}.log'
logger = getLogger(__name__)
logger = logInit(logger, './mnt/logs/', log_file)
sel_log(f'args: {sorted(vars(args).items())}', logger)
configs = load_configs(CONFIG_DIR + exp_id + '.yml', logger)
if configs['runner'] == 'r001':
from tools.runners.r001_basic_runner import Runner
elif configs['runner'] == 'r002':
from tools.runners.r002_warmup_separate import Runner
elif configs['runner'] == 'r003':
from tools.runners.r003_metric_learning import Runner
elif configs['runner'] == 'r004':
from tools.runners.r004_test_random_sampler import Runner
runner = Runner(configs, args, logger)
if not args.prediction:
if len(args.cell_types) != 1:
raise Exception('you can use just one cell type for train')
cell_type = args.cell_types[0]
runner.train_model(cell_type)
