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 "