def main():
con = db.connect()
cur = con.cursor()
emos = open('../data/emo_top100.txt', 'r').read().decode('utf8').split('\n')
limit = int(sys.argv[1])
pbar = progbar.start(limit)
i = 0
cur.execute("SELECT text FROM microblogs WHERE comments_count > 0 AND comments_count < 100 LIMIT %d"%(limit))
fobjs = dict([(emo, open('../corpus_raw/%s.txt'%(emo), 'w')) for emo in emos])
for res in cur:
blog = res[0]
text, emos = blogger.extract(blog)
n_emo = len(emos)
if n_emo > 0 and n_emo < 6:
samples = blogger.prepare_sample(text, emos)
for e, t in samples:
if not e in fobjs:
continue
fobjs[e].write(t + '\n')
i += 1
pbar.update(i)
pbar.finish()
for fobj in fobjs.values():
fobj.close()
def main():
con = db.connect()
cur = con.cursor()
emo_mids = {}
limit = 25000000
pbar = progbar.start(limit)
i = 0
cur.execute("SELECT mid, text FROM microblogs WHERE comments_count > 0 AND comments_count < 100 LIMIT %d"%(limit))
for mid, text in cur:
text, emos = blogger.extract(text)
if len(emos) > 0 and len(emos) < 6:
samples = blogger.prepare_sample(text, emos)
for e, t in samples:
if emo_mids.has_key(e):
emo_mids[e].append(mid)
else:
emo_mids[e] = [mid, ]
i += 1
pbar.update(i)
pbar.finish()
cPickle.dump(emo_mids, open('../output/emo_mids.pkl', 'w'))
def prepare():
'''
prepare data for LSTM
data are structured as [[codes, codes, ...], ...], which are ordered by eid
'''
import blogger
from utils import progbar
coder = cPickle.load(open(PKL_TFCODER, 'r'))
datalist = []
pbar = progbar.start(N_EMO)
for eid in range(N_EMO):
lines = open(DIR_TEXT + '%d.txt'%(eid), 'r').read().split('\n')
data = []
for line in lines:
text, emo = blogger.extract(line)
codes = coder.code(text)
data.append(codes)
datalist.append(data)
pbar.update(eid + 1)
pbar.finish()
cPickle.dump(datalist, open(PKL_TFDATA, 'w'))
def extract(ifname, ofname):
n_lines = int(commands.getoutput('grep -cF "" %s'%(ifname)))
if n_lines == 0:
return
pbar = progbar.start(n_lines)
l = 0
datalist = []
ofobj = open(ofname, 'w')
with open(ifname, 'r') as ifobj:
for line in ifobj:
blog = json.loads(line)
n_text = len(blog['text'])
for i in range(1, n_text):
res = blogger.extract(blog['text'][i])
if res is not None:
datum = {}
datum['text'] = res[0]
datum['emo'] = res[1]
above = []
re_id = i
while blog['re'][re_id] is not None:
re_id = blog['re'][re_id]
above.append(blog['text'][re_id])
datum['above'] = above
follow = []
last_id = i
for j in range(i + 1, n_text):
if blog['re'][j] == last_id:
follow.append(blog['text'][j])
last_id = j
datum['follow'] = follow
ofobj.write(json.dumps(datum) + '\n')
#for k in range(n_text):
# print '%d. (-> %s) %s'%(k, blog['re'][k], blog['text'][k])
#
#print 'above:'
#print '\n'.join(above)
#print 'follow:'
#print '\n'.join(follow)
#print
#if i > 100:
# return
l += 1
pbar.update(l)
pbar.finish()
ofobj.close()
def main():
import db
import blogger
from utils import progbar
n_label = len(emos_list)
emo_map = {}
for label, emos in enumerate(emos_list):
for emo in emos:
emo_map[emo] = label
odname = 'data/dataset_emo/'
if not os.path.isdir(odname):
os.mkdir(odname)
odname += 'raw/'
if not os.path.isdir(odname):
os.mkdir(odname)
fobjs = [open(odname + '%d.txt'%(i), 'w') for i in range(n_label)]
counts = [0 for i in range(n_label)]
N = 70000
all_N = N * n_label
con = db.connect()
cur = con.cursor()
pbar = progbar.start(all_N)
l = 0
cur.execute('SELECT text FROM microblogs')
for t in cur:
res = blogger.extract(t[0])
if res is None or not emo_map.has_key(res[1]):
continue
label = emo_map[res[1]]
if counts[label] < N:
counts[label] += 1
fobjs[label].write(res[0] + '\n')
l += 1
pbar.update(l)
if counts[label] == N and sum(counts) == all_N:
break
pbar.finish()
cur.close()
con.close()
for fobj in fobjs:
fobj.close()
def extract(dname_dataset, idx):
idname = 'data/blogs/mtr/'
dir_dataset = 'data/blogs/%s/'%(dname_dataset)
odname = dir_dataset + 'tea/'
init_folders([dir_dataset, odname])
ifname = idname + '%d.txt'%(idx)
ofname = odname + '%d.txt'%(idx)
n_lines = int(commands.getoutput('grep -cF "" %s'%(ifname)))
if n_lines == 0:
print >> sys.stderr, '%s is empty'%(ifname)
return
pbar = progbar.start(n_lines)
l = 0
datalist = []
ofobj = open(ofname, 'w')
with open(ifname, 'r') as ifobj:
for line in ifobj:
blog = json.loads(line)
n_text = len(blog['text'])
for i in range(1, n_text):
res = blogger.extract(blog['text'][i])
if res is not None:
datum = {}
datum['text'] = res[0]
datum['emo'] = res[1]
above_s = []
re_id = i
while blog['re'][re_id] is not None:
re_id = blog['re'][re_id]
above_s.append(blog['text'][re_id])
datum['above_s'] = above_s
above_t = []
re_id = i - 1
while re_id >= 0:
above_t.append(blog['text'][re_id])
re_id -= 1
datum['above_t'] = above_t
ofobj.write(json.dumps(datum) + '\n')
l += 1
pbar.update(l)
pbar.finish()
ofobj.close()
def sample_seqs():
import blogger
import zhtokenizer
lines = open('data/blogs1000.txt', 'r').readlines()
for line in lines:
l = line.strip()
res = blogger.extract(l, check_emo = False)
if res == None:
continue
t = res[0]
yield zhtokenizer.tokenize(t)
def main():
import db
import datica
import blogger
from utils import progbar
con = db.connect()
cur = con.cursor()
maxN = 70000
odname = 'data/dataset_emo/'
if not os.path.isdir(odname):
os.mkdir(odname)
config = datica.load_config('data/config4.txt')
for label, eids in enumerate(config):
for eid in eids:
print >> sys.stderr, 'loading LABEL %d - EID %d'%(label, eid)
ifname = 'data/eid_mids/%d.txt'%(eid)
ofname = odname + '%d.txt'%(eid)
ofobj = open(ofname, 'w')
mids = open(ifname, 'r').read().split('\n')
if len(mids) > maxN:
mids = mids[:maxN]
pbar = progbar.start(len(mids))
l = 0
for mid in mids:
t = load_by_mid(cur, mid)
res = blogger.extract(t)
if res is not None:
text, emo = res
ofobj.write(text + '\n')
l += 1
pbar.update(l)
pbar.finish()
ofobj.close()
cur.close()
con.close()
def emo_rate(blogs): count = 0 total = 0 tlen = [] emolist = [] for blog in blogs: total += blog['comments_count'] for comm in blog['comments']: res = blogger.extract(comm['text']) if res == None or len(res[0]) < 2: continue count += 1 tlen.append(len(res[0])) emolist.append(res[1]) emohist = tohist(emolist) return 100. * count / total, np.mean(tlen), emohist
def prepare(eids = range(N_EMO)):
'''
tokenize and unigramize the text under data/dataset/text
'''
import blogger
import zhtokenizer
from utils import progbar, zhprocessor
if not os.path.isdir(DIR_UNIGRAM):
os.mkdir(DIR_UNIGRAM)
if not os.path.isdir(DIR_TOKEN):
os.mkdir(DIR_TOKEN)
unigram_list = []
token_list = []
for eid in eids:
lines = open(DIR_TEXT + '%d.txt'%(eid), 'r').read().split('\n')
unigram_list = []
token_list = []
print 'preparing data for EID-%d'%(eid)
pbar = progbar.start(len(lines))
for i, line in enumerate(lines):
text, emo = blogger.extract(line)
text = zhprocessor.simplify(text)
unigrams = zhtokenizer.unigramize(text)
tokens = zhtokenizer.tokenize(text)
unigram_list.append(unigrams)
token_list.append(tokens)
pbar.update(i + 1)
pbar.finish()
cPickle.dump(unigram_list, open(DIR_UNIGRAM + '%d.pkl'%(eid), 'w'))
cPickle.dump(token_list, open(DIR_TOKEN + '%d.pkl'%(eid), 'w'))
def collect_emo_mids():
'''
collect {emo: [mid, mid, ..], } from mysql and export to PKL_EMO_MIDS
'''
import db
from utils import probgar
print 'connecting to MySQL..'
con = db.connect()
print 'start..'
cur = con.cursor()
cur.execute('SELECT mid, text FROM microblogs WHERE comments_count > 1')
pbar = progbar.start(TOTAL_BLOGS)
loop = 0
emo_mids = {}
for mid, text in cur:
res = blogger.extract(text)
if res == None:
continue
text, emo = res
if emo_mids.has_key(emo):
emo_mids[emo].append(mid)
else:
emo_mids[emo] = [mid, ]
loop += 1
pbar.update(loop)
pbar.finish()
cPickle.dump(emo_mids, open(PKL_EMO_MIDS, 'w'))
cur.close()
con.close()
def prepare():
'''
load data/blogs/blogs_400000.txt and after filtering by blogger.is_valid
those which pass are saved into data/blogs/blogs_filtered.txt
'''
import blogger
from utils import progbar
blogs = []
lines = open(FNAME_BLOGS_RAW, 'r').readlines()
pbar = progbar.start(len(lines))
for i, l in enumerate(lines):
parts = l[:-1].decode('utf8').split('\t')
params = []
for part in parts:
if not part == '':
params.append(part)
text = params[2]
res = blogger.extract(text)
if res is not None:
uid = params[0]
mid = params[1]
comments_count = int(params[3])
blogs.append(BlogInfo(uid, mid, text, comments_count))
pbar.update(i + 1)
pbar.finish()
fobj = open(FNAME_BLOGS_FILTERED, 'w')
for blog in blogs:
fobj.write('%s\t%s\t%s\t%d\n'%(blog.uid, blog.mid, blog.text, blog.comments_count))
fobj.close()
def main():
import db
import blogger
from utils import progbar
p1 = re.compile('[:;]\)')
p2 = re.compile('[:;]\(')
patterns = [p1, p2]
n_label = 2
N = 70000
odname = 'data/dataset_sym/'
if not os.path.isdir(odname):
os.mkdir(odname)
odname += 'raw/'
if not os.path.isdir(odname):
os.mkdir(odname)
fobjs = [open(odname + '%d.txt'%(i), 'w') for i in range(n_label)]
counts = [0 for i in range(n_label)]
all_N = N * n_label
con = db.connect()
cur = con.cursor()
st_time = time.time()
print >> sys.stderr, 'executing... ',
cur.execute('SELECT text FROM microblogs')
print >> sys.stderr, time.time() - st_time
pbar = progbar.start(all_N)
l = 0
for t in cur:
res = blogger.extract(t[0], check_emo = False)
if res is None:
continue
t = res[0]
pid = None
p = None
for i, pi in enumerate(patterns):
if pi.search(t) >= 0:
p = pi
pid = i
break
if p is None:
continue
text = p.sub('', t)
if counts[pid] < N:
counts[pid] += 1
fobjs[pid].write(text + '\n')
l += 1
pbar.update(l)
if counts[pid] == N and sum(counts) == all_N:
break
pbar.finish()
cur.close()
con.close()
print counts
for fobj in fobjs:
fobj.close()
