def main(argv=None):
    args = docopt.docopt(__doc__, argv=argv)

    logging.basicConfig(
        level=logging.INFO if not args['--debug'] else logging.DEBUG,
        format='%(asctime)s %(levelname)s:%(name)s -- %(message)s'
    )

    params_config = yamlconf.load(open(args['<params-config>']))

    features_path = args['<features>']
    features = yamlconf.import_module(features_path)

    label_decoder = util.DECODERS[args['--label-type']]
    if args['--observations'] == "<stdin>":
        observations_f = sys.stdin
    else:
        observations_f = open(args['--observations'])

    observations = util.read_observations(observations_f, features,
                                          label_decoder)

    # Get a sepecialized scorer if we have one
    scoring = metrics.SCORERS.get(args['--scoring'], args['--scoring'])

    folds = int(args['--folds'])

    if args['--report'] == "<stdout>":
        report = sys.stdout
    else:
        report = open(args['--report'], "w")

    if args['--processes'] == "<cpu-count>":
        processes = multiprocessing.cpu_count()
    else:
        processes = int(args['--processes'])

    if args['--cv-timeout'] == "<forever>":
        cv_timeout = None
    else:
        cv_timeout = float(args['--cv-timeout']) * 60  # Convert to seconds

    scale_features = args['--scale-features']
    verbose = args['--verbose']

    run(params_config, features_path, observations, scoring, folds,
        report, processes, cv_timeout, scale_features, verbose)
dataset/ETHPy150Open wiki-ai/revscoring/revscoring/utilities/tune.py/main/original