def main(argv=None):
    # Get command line arguments
    parser = argparse.ArgumentParser(
        description="Takes an input feature file and converts it to another \
                     format. Formats are determined automatically from file \
                     extensions.",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('infile',
                        help='input feature file (ends in .arff, .csv, \
                              .jsonlines, .libsvm, .megam, .ndj, or .tsv)')
    parser.add_argument('outfile',
                        help='output feature file (ends in .arff, .csv, \
                              .jsonlines, .libsvm, .megam, .ndj, or .tsv)')
    parser.add_argument('-i', '--id_col',
                        help='Name of the column which contains the instance \
                              IDs in ARFF, CSV, or TSV files.',
                        default='id')
    parser.add_argument('-l', '--label_col',
                        help='Name of the column which contains the class \
                              labels in ARFF, CSV, or TSV files. For ARFF \
                              files, this must be the final column to count as\
                              the label.',
                        default='y')
    parser.add_argument('-q', '--quiet',
                        help='Suppress printing of "Loading..." messages.',
                        action='store_true')
    parser.add_argument('--arff_regression',
                        help='Create ARFF files for regression, not \
                              classification.',
                        action='store_true')
    parser.add_argument('--arff_relation',
                        help='Relation name to use for ARFF file.',
                        default='skll_relation')
    parser.add_argument('--reuse_libsvm_map',
                        help='If you want to output multiple files that use \
                              the same mapping from labels and features to \
                              numbers when writing libsvm files, you can \
                              specify an existing .libsvm file to reuse the \
                              mapping from.',
                        type=argparse.FileType('rb'))
    parser.add_argument('--version', action='version',
                        version='%(prog)s {0}'.format(__version__))
    args = parser.parse_args(argv)

    # Make warnings from built-in warnings module get formatted more nicely
    logging.captureWarnings(True)
    logging.basicConfig(format=('%(asctime)s - %(name)s - %(levelname)s - '
                                '%(message)s'))
    logger = logging.getLogger(__name__)

    # make sure the input file extension is one we can process
    input_extension = os.path.splitext(args.infile)[1].lower()
    output_extension = os.path.splitext(args.outfile)[1].lower()

    if input_extension not in EXT_TO_READER:
        logger.error(('Input file must be in either .arff, .csv, .jsonlines, '
                      '.libsvm, .megam, .ndj, or .tsv format. You specified: '
                      '{}').format(input_extension))
        sys.exit(1)

    # Build feature and label vectorizers from existing libsvm file if asked
    if args.reuse_libsvm_map and output_extension == '.libsvm':
        feat_map = {}
        label_map = {}
        for line in args.reuse_libsvm_map:
            line = UnicodeDammit(line, ['utf-8',
                                        'windows-1252']).unicode_markup
            if '#' not in line:
                logger.error('The LibSVM file you want to reuse the map from '
                             'was not created by SKLL and does not actually '
                             'contain the necessary mapping info.')
                sys.exit(1)
            comments = line.split('#')[1]
            _, label_map_str, feat_map_str = comments.split('|')
            feat_map.update(_pair_to_dict_tuple(pair) for pair in
                            feat_map_str.strip().split())
            label_map.update(_pair_to_dict_tuple(pair) for pair in
                             label_map_str
                             .strip().split())
        feat_vectorizer = DictVectorizer()
        feat_vectorizer.fit([{name: 1} for name in feat_map])
        feat_vectorizer.vocabulary_ = feat_map
    else:
        feat_vectorizer = None
        label_map = None

    # Iterate through input file and collect the information we need
    reader = EXT_TO_READER[input_extension](args.infile, quiet=args.quiet,
                                            label_col=args.label_col,
                                            id_col=args.id_col)
    feature_set = reader.read()
    # write out the file in the requested output format
    writer_type = EXT_TO_WRITER[output_extension]
    writer_args = {'quiet': args.quiet}
    if writer_type is DelimitedFileWriter:
        writer_args['label_col'] = args.label_col
        writer_args['id_col'] = args.id_col
    elif writer_type is ARFFWriter:
        writer_args['label_col'] = args.label_col
        writer_args['id_col'] = args.id_col
        writer_args['regression'] = args.arff_regression
        writer_args['relation'] = args.arff_relation
    elif writer_type is LibSVMWriter:
        writer_args['label_map'] = label_map
    writer = writer_type(args.outfile, feature_set, **writer_args)
    writer.write()
Handles command line arguments and gets things started.
dataset/ETHPy150Open EducationalTestingService/skll/skll/utilities/skll_convert.py/main