def main():
parser = argparse.ArgumentParser(
description='Stochastically adds features to a corpus.')
parser.add_argument('--vocab',
'-v',
metavar='FILE',
help='The vocab labels.')
parser.add_argument('--input',
'-i',
metavar='FILE',
help='The input corpus (in Andrews format).')
parser.add_argument('--output',
'-o',
metavar='FILE',
help='The output corpus (in Andrews format).')
parser.add_argument('--features',
'-f',
metavar='FILE',
help='The (dense) vector space of features.')
args = parser.parse_args()
vocab_labels = load_labels(args.vocab)
features = load_features(args.features)
feature_map = word_ids_to_features(vocab_labels, features)
logging.info("First pass; gathering statistics.")
inpt = utfopen(args.input)
numlines = len(inpt.readlines())
inpt.close()
logging.info("Starting second pass; actually writing output.")
output = open(args.output, 'w', 1024 * 1024)
inpt = utfopen(args.input)
for lno, line in enumerate(inpt.readlines(), 1):
if lno % 1000 == 0:
logging.info("Processing doc# %d/%d (%4.1f%%)" %
(lno, numlines, 100 * float(lno) / numlines))
for chunk in itersplit(line, ' '):
chunk = chunk.rstrip()
if not chunk: continue
idx = chunk.rindex(":")
wid, cnt = chunk[:idx], chunk[idx + 1:]
if wid not in feature_map:
output.write(chunk + ' ')
else:
cnt = int(cnt)
dist = feature_map[wid]
cnts = Counter(stochastic_choice(dist) for i in xrange(cnt))
for fid, cnt in cnts.iteritems():
output.write('%s,%d:%d ' % (wid, fid, cnt))
output.write('\n')
inpt.close()
output.close()
def load_features(file):
with utfopen(file) as f:
feature_vectors = [l.rstrip().split("\t") for l in f.readlines()]
feature_vectors = [(w, map(float, v.split()))
for w, v in feature_vectors]
feature_cdfs = {w: tocdf(v) for w, v in feature_vectors}
return feature_cdfs
def main():
parser = argparse.ArgumentParser(description='Stochastically adds features to a corpus.')
parser.add_argument('--vocab', '-v', metavar='FILE',
help='The vocab labels.')
parser.add_argument('--input', '-i', metavar='FILE',
help='The input corpus (in Andrews format).')
parser.add_argument('--output', '-o', metavar='FILE',
help='The output corpus (in Andrews format).')
parser.add_argument('--features', '-f', metavar='FILE',
help='The (dense) vector space of features.')
args = parser.parse_args()
vocab_labels = load_labels(args.vocab)
features = load_features(args.features)
feature_map = word_ids_to_features(vocab_labels, features)
logging.info("First pass; gathering statistics.")
inpt = utfopen(args.input)
numlines = len(inpt.readlines())
inpt.close()
logging.info("Starting second pass; actually writing output.")
output = open(args.output, 'w', 1024*1024)
inpt = utfopen(args.input)
for lno, line in enumerate(inpt.readlines(), 1):
if lno % 1000 == 0:
logging.info("Processing doc# %d/%d (%4.1f%%)" % (lno, numlines, 100*float(lno)/numlines))
for chunk in itersplit(line, ' '):
chunk = chunk.rstrip()
if not chunk: continue
idx = chunk.rindex(":")
wid, cnt = chunk[:idx], chunk[idx+1:]
if wid not in feature_map:
output.write(chunk + ' ')
else:
cnt = int(cnt)
dist = feature_map[wid]
cnts = Counter(stochastic_choice(dist) for i in xrange(cnt))
for fid, cnt in cnts.iteritems():
output.write('%s,%d:%d ' % (wid, fid, cnt))
output.write('\n')
inpt.close()
output.close()
def load_features(file):
with utfopen(file) as f:
feature_vectors = [l.rstrip().split("\t") for l in f.readlines()]
feature_vectors = [(w, map(float, v.split())) for w, v in feature_vectors]
feature_cdfs = { w : tocdf(v) for w, v in feature_vectors }
return feature_cdfs
