def main():
parser = argparse.ArgumentParser(description='Add features to n-best list')
parser.add_argument('nbest', help='n-best file')
parser.add_argument('align', help='alignments file')
parser.add_argument('source', help='source file')
parser.add_argument('config', help='config file')
parser.add_argument('-features_only', type=bool, default=False, help='print features only')
args = parser.parse_args()
config = __import__(args.config)
nbests = groupby(read_nbest(args.nbest, args.align), key=lambda x: x[0])
for (_, group), source in izip(nbests, read_source(args.source)):
for sid, target, alignments, init_features in group:
features = add_features(extract_features(source, target, alignments, config), init_features)
if args.features_only:
for f in features.split(' '):
print f.split('=')[0].encode('utf8')
else:
print(u'{} ||| {} ||| {}'.format(sid, target, features).encode('utf8'))
def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(description='Re-rank n-best lists')
parser.add_argument('weights', help='weight file to use for ranking')
args = parser.parse_args()
with open(args.weights) as f:
weights = pickle.load(f)
logging.info('Reranking with %d weights', len(weights))
for sentence_id, group in groupby(read_nbest('/dev/stdin'), key=lambda t:t[0]):
sentence_id, sentence, alignments, features = max(group, key=select_best(weights))
print(sentence.encode('utf8'))
logging.info('Observed %d features in test set', len(all_observed_features))
all_weights = set(weights.iterkeys())
if all_weights != all_observed_features:
diff = (all_weights - all_observed_features) | (all_observed_features - all_weights)
if len(diff) < 20:
logging.info('Missing features: %s', ' '.join(diff))
