def build_ngram_corpus(args):
indir = args[2]
maxngram = int(args[3])
mydir = indir + args[3]
sampled_lines = getfilelines(indir + '/all.sampled', upto=-1)
print 'sampled lines are loaded'
lines = filter_sentences(sampled_lines)
print 'filtering finished'
if not os.path.exists(mydir): os.makedirs(mydir)
ngram2freq = calc_ngram_freq(lines, maxngram)
voc2idx = build_vocab(ngram2freq, min_freq=5)
process_corpus(lines, voc2idx, ngram2freq, maxngram, mydir + '/corpus,processed.txt')
write_vocab(voc2idx, ngram2freq, mydir + '/vocab-count.txt')
def build_ngram_corpus(args):
indir = args[2]
maxngram = int(args[3])
mydir = indir + args[3]
sampled_lines = getfilelines(indir + '/all.sampled', upto=-1)
print 'sampled lines are loaded'
lines = filter_sentences(sampled_lines)
print 'filtering finished'
if not os.path.exists(mydir): os.makedirs(mydir)
ngram2freq = calc_ngram_freq(lines, maxngram)
voc2idx = build_vocab(ngram2freq, min_freq=5)
process_corpus(lines, voc2idx, ngram2freq, maxngram,
mydir + '/corpus,processed.txt')
write_vocab(voc2idx, ngram2freq, mydir + '/vocab-count.txt')
def load_lines(lines_file, e2types, upto=-1):
logger.info('loading lines from %s ...', lines_file)
e2lines = defaultdict(list)
e2freq = defaultdict(lambda: 0)
t2lines = defaultdict(list)
c = 0
lines = getfilelines(lines_file)
for line in lines:
parts = line.split('\t')
if len(parts) != 5:
print len(parts)
print line
assert len(parts) == 5
mye = parseents(parts[1])[0]
text = parts[4].strip()
e2lines[mye].append(text)
e2freq[mye] += 1
t2lines[e2types[mye][0]].append((text,mye)) # Add text to the notable type of mye
if c == upto:
break
c += 1
logger.info('... lines loaded')
return (e2lines,t2lines, e2freq)