# no need after optimized num_boost_round
del optimal_c_model.params['early_stopping_rounds']
return optimal_c_model
if __name__ == "__main__":
gc.enable()
warnings.filterwarnings('ignore')
# read config & apply option
c = EasyDict(config)
opt = parse_option()
c.transformer.USE_SMALL_DATA = opt.small
c.log.slackauth.NO_SEND_MESSAGE = opt.nomsg
seed_everything(c.runtime.RANDOM_SEED)
create_logger('main', **c.log)
logger = getLogger('main')
logger.info(
f':thinking_face: Starting experiment {c.runtime.VERSION}_{c.runtime.DESCRIPTION}'
)
try:
main(c)
logger.info(
f':sunglasses: Finished experiment {c.runtime.VERSION}_{c.runtime.DESCRIPTION}'
)
except Exception:
logger.critical(
f':smiling_imp: Exception occured \n {traceback.format_exc()}')
c = json.load(open(f'config/config_{opt.version}.json'))
c = EasyDict(c)
c.runtime = {}
c.runtime.version = opt.version
c.runtime.use_small_data = opt.small
c.runtime.no_send_message = opt.nomsg
c.runtime.random_seed = opt.seed
c.runtime.dsize = '.small' if c.runtime.use_small_data is True else ''
# dict to save results
r = EasyDict()
r.update(c)
r.paths = {}
r.paths.result = f'config/result_{c.runtime.version}{c.runtime.dsize}.json'
seed_everything(c.runtime.random_seed)
# check config.experiment_type
if c.experiment_type != experiment_type:
raise ValueError(
f'experiment_type in config: {c.experiment_type} does not match this script'
)
# start logging
r.paths.main_log_path = f'log/main_{c.runtime.version}{c.runtime.dsize}.log'
r.paths.train_log_path = f'log/train_{c.runtime.version}{c.runtime.dsize}.tsv'
slackauth = EasyDict(json.load(open('./slackauth.json', 'r')))
slackauth.token_path = Path().home() / slackauth.token_file
create_logger('main',
version=c.runtime.version,
log_path=r.paths.main_log_path,