def main():
parser = argparse.ArgumentParser(
description='Outputs a human readable model.')
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).')
args = parser.parse_args()
input = open(args.input)
output = open(args.output, 'w')
for i, line in enumerate(input.readlines()):
line = line.rstrip()
output_lst = []
for w in itersplit(line, ' '):
if not w.strip(): continue
if ',' not in w: continue
output_lst.append(w[w.index(',') + 1:])
output.write(" ".join(output_lst) + "\n")
logging.info("Document %d: %d words written." % (i, len(output_lst)))
input.close()
output.close()
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 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). Must be multimodal.')
parser.add_argument('--output', '-o', metavar='FILE',
help='The output corpus (in Andrews format).')
parser.add_argument('--outvocab', '-V', metavar='FILE',
help='The output vocab labels; necessary for OOV processing later.')
args = parser.parse_args()
vocab_labels = load_labels(args.vocab)
logging.info("First pass; gathering statistics.")
inpt = open(args.input)
numlines = len(inpt.readlines())
inpt.close()
output_labels = {}
output_labels_file = utfopenwrite(args.outvocab)
logging.info("Starting second pass; actually writing output.")
output = open(args.output, 'w', 1024*1024)
inpt = open(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))
outline = []
for chunk in itersplit(line, ' '):
chunk = chunk.rstrip()
if not chunk: continue
if ',' not in chunk: continue # strip just words
idx = chunk.index(',')
wid = int(chunk[:idx])
rest = chunk[idx:]
if wid not in output_labels:
output_labels[wid] = len(output_labels) + 1
output_labels_file.write("%d\t" % output_labels[wid])
output_labels_file.write(vocab_labels[wid])
output_labels_file.write("\n")
outline.append(str(output_labels[wid]) + rest)
if outline:
output.write(' '.join(outline))
output.write('\n')
inpt.close()
output.close()
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 main():
parser = argparse.ArgumentParser(description='Outputs a human readable model.')
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).')
args = parser.parse_args()
input = open(args.input)
output = open(args.output, 'w')
for i, line in enumerate(input.readlines()):
line = line.rstrip()
output_lst = []
for w in itersplit(line, ' '):
if not w.strip(): continue
if ',' not in w: continue
output_lst.append(w[w.index(',')+1:])
output.write(" ".join(output_lst) + "\n")
logging.info("Document %d: %d words written." % (i, len(output_lst)))
input.close()
output.close()
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). Must be multimodal.')
parser.add_argument('--output',
'-o',
metavar='FILE',
help='The output corpus (in Andrews format).')
parser.add_argument(
'--outvocab',
'-V',
metavar='FILE',
help='The output vocab labels; necessary for OOV processing later.')
args = parser.parse_args()
vocab_labels = load_labels(args.vocab)
logging.info("First pass; gathering statistics.")
inpt = open(args.input)
numlines = len(inpt.readlines())
inpt.close()
output_labels = {}
output_labels_file = utfopenwrite(args.outvocab)
logging.info("Starting second pass; actually writing output.")
output = open(args.output, 'w', 1024 * 1024)
inpt = open(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))
outline = []
for chunk in itersplit(line, ' '):
chunk = chunk.rstrip()
if not chunk: continue
if ',' not in chunk: continue # strip just words
idx = chunk.index(',')
wid = int(chunk[:idx])
rest = chunk[idx:]
if wid not in output_labels:
output_labels[wid] = len(output_labels) + 1
output_labels_file.write("%d\t" % output_labels[wid])
output_labels_file.write(vocab_labels[wid])
output_labels_file.write("\n")
outline.append(str(output_labels[wid]) + rest)
if outline:
output.write(' '.join(outline))
output.write('\n')
inpt.close()
output.close()