def train_classifiers(user):
global args
corpus = RedditMySQLCorpus()
corpus.setup(**(cred.kwargs))
cls = {}
for sr in args.subreddits:
document = corpus.get_train_documents(args.type, user['username'], sr, args.c[0]).encode('utf-8')
cl = RedditPPM()
cl.train(document)
cls[sr] = cl
del corpus
return cls
def feature_to_numeric(features):
corpus = RedditMySQLCorpus()
corpus.setup(**(cred.kwargs))
where_list = []
for k in features:
where_list.append(' (`type` = \'%s\' AND `feature` = \'%s\') ' %
(k[0], k[1].replace('\\', '\\\\').replace(
'\n', '\\n').replace('\'', '\\\'')))
numeric = {}
for x in range(0, len(features), 100):
where_clause = 'WHERE ' + '\n OR'.join(where_list[x:x + 100])
#print(where_clause)
rows = corpus.run_sql(
'SELECT `id`, `type`, `feature` FROM `feature_map_test` ' +
where_clause, None)
for row in rows:
if (row['type'], row['feature']) not in features:
continue
numeric[row['id']] = features[(row['type'], row['feature'])]
return numeric
'\\1',
atuple['text'],
flags=re.MULTILINE)
aset = set()
wngram = ngram.get_word_ngrams(text)
for n in wngram['ngram_word']:
for k in wngram['ngram_word'][n]:
aset.add(('npos%d' % n, ' '.join(k)))
words, clean_words = ngram.get_words(text)
for word in words:
aset.add(('pos', word))
return set(aset)
if __name__ == '__main__':
corpus = RedditMySQLCorpus()
corpus.setup(**(cred.kwargs))
corpus.create()
pool = multiprocessing.Pool(2)
print('set up pool')
chunk = 200
feature_set = set()
for reddit in [
'worldnews', 'quantum', 'netsec', 'uwaterloo', 'gaming', 'news',
'AskReddit'
]:
j = 0
while True:
print('j=%d' % j)
rows = corpus.run_sql(
ranklist.append(rank)
return ranklist
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-t',
'--type',
choices=['submission', 'comment'],
required=True)
parser.add_argument('n', type=int, nargs=1)
parser.add_argument('c', type=int, nargs=1)
parser.add_argument('subreddits', type=str, nargs='+')
args = parser.parse_args(sys.argv[1:])
corpus = RedditMySQLCorpus(multiprocessing.cpu_count())
corpus.setup(**(cred.kwargs))
corpus.create()
print(args.n, args.c, args.subreddits)
userlist = corpus.get_user_list(args.type, args.c[0], args.subreddits)
userlist = userlist[:args.n[0]]
print('Got users')
pprint.pprint(userlist)
print('Downloading document list')
corpora = {}
for sr in args.subreddits:
corpora[sr] = corpus.get_test_grouped_documents(args.type, sr)
print('Downloaded %s' % sr)
del corpus
pool = multiprocessing.Pool(multiprocessing.cpu_count())
from main import cred
def fn(arg):
st = time.clock()
read = feature.simple.get_read_stats(arg['text'].encode('ascii', 'ignore'))
et = time.clock()
#tprint(('stats took %s' % (et - st)))
return (arg['id'], read['ari'], read['flesch_reading_ease'],
read['flesch_kincaid_grade_level'], read['gunning_fog_index'],
read['smog_index'], read['coleman_liau_index'], read['lix'],
read['rix'])
corpus1 = RedditMySQLCorpus()
corpus1.setup(**(cred.kwargs))
corpus1.create()
corpus2 = RedditMySQLCorpus()
corpus2.setup(**(cred.kwargs))
corpus2.create()
fp = open('data/results.csv')
for i in range(1000, 100000, 1000):
st = time.clock()
dgt = DataGetThread(corpus1,
'SELECT id, body AS text FROM comment',
