def main(argv=None):
"""
Handles command line arguments and gets things started.
Parameters
----------
argv : list of str
List of arguments, as if specified on the command-line.
If None, ``sys.argv[1:]`` is used instead.
"""
# 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()
def main(argv=None):
"""
Handles command line arguments and gets things started.
Parameters
----------
argv : list of str
List of arguments, as if specified on the command-line.
If None, ``sys.argv[1:]`` is used instead.
"""
# 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')
label_group = parser.add_mutually_exclusive_group(required=False)
label_group.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')
label_group.add_argument('--no_labels',
action='store_true',
default=False,
help='Used to indicate that the input data has no labels.')
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
label_col = None if args.no_labels else args.label_col
# Iterate through input file and collect the information we need
reader = EXT_TO_READER[input_extension](args.infile,
quiet=args.quiet,
label_col=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 CSVWriter or writer_type is TSVWriter:
writer_args['label_col'] = label_col
writer_args['id_col'] = args.id_col
elif writer_type is ARFFWriter:
writer_args['label_col'] = 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()
