def run():
# Get command-line arguments
args = get_args()
# Initialize logger
if args.v:
init_logging(args.log, level='DEBUG')
else:
init_logging(args.log)
# Run subcommand
if args.cmd == 'fetch-ini':
from fetchini import main
main.main(args)
elif args.cmd == 'check-vocab':
from checkvocab import main
main.main(args)
elif args.cmd == 'drs':
from drs import main
main.main(args)
elif args.cmd == 'mapfile':
from mapfile import main
main.main(args)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Draw a sample of items')
parser.add_argument('-v',
'--verbose',
help='verbose output',
action='store_true')
parser.add_argument('-s',
'--size',
help='size of sample',
type=int,
required=True)
parser.add_argument('-f', '--filter', help='filter', type=str, default='')
parser.add_argument('-q', '--query', help='query', type=str, default='')
parser.add_argument('-o',
'--output',
help='output file',
type=str,
required=True)
args = parser.parse_args()
init_logging(args.verbose)
if args.filter:
args.filter = parse_filter_text(args.filter)
else:
args.filter = {}
sample_data = generate_sample_data(args.size, args.filter, args.query)
save_sample_data(sample_data, args.output)
"test_p": best_p,
"test_r": best_r,
"test_f": best_f
},
"params": params
}
# write results to file
result_writer = ResultWriter(train_args.result_log_dir)
identifier = train_args.identifier
result_writer.write_result(identifier, train_args.fold_num,
params.get("num_of_pass"), report)
if __name__ == '__main__':
init_logging()
parser = argparse.ArgumentParser(
description="Parse arguments for model training")
parser.add_argument("--identifier",
type=str,
help="an identifier string that describes the model")
parser.add_argument("--fold_num",
type=int,
help="dictates which fold to use")
parser.add_argument("--max_train_epoch",
type=int,
help="maximum training epoch in training loop")
parser.add_argument("--patience",
type=int,
help="epochs to wait before seeing new lowest test "