File size: 5,436 Bytes
5ad627f f1949f7 5ad627f f1949f7 5ad627f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
"""
Removes near-duplicates and tweets from top pct. of users.
original -> https://github.com/cardiffnlp/timelms/blob/main/scripts/preprocess.py
optional arguments:
-h, --help show this help message and exit
--src SRC Path to set of input tweets (.jl).
--out OUT Path to output from preprocessing (.jl).
--blacklist_pct BLACKLIST_PCT
Percent of most frequent users to ignore.
Example:
python timelm_preprocessor.py --src /mnt/share/daniel_tweet_dump/2018.raw.jl --out dataset/tweets/2018.jsonline
python timelm_preprocessor.py --src /mnt/share/daniel_tweet_dump/2019.raw.jl --out dataset/tweets/2019.jsonline
python timelm_preprocessor.py --src /mnt/share/daniel_tweet_dump/2020.raw.jl --out dataset/tweets/2020.jsonline
python timelm_preprocessor.py --src /mnt/share/daniel_tweet_dump/2021.raw.jl --out dataset/tweets/2021.jsonline
python timelm_preprocessor.py --src /mnt/share/daniel_tweet_dump/2022.raw.jl --out dataset/tweets/2022.jsonline
"""
import argparse
import json
import logging
import os
import string
import re
from collections import Counter
from datasketch import MinHash, LeanMinHash
import xxhash
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S')
re_url = re.compile(r'https?:\/\/[\w\.\/\?\=\d&#%_:/-]+')
re_user = re.compile(r'@\w+')
with open('verified_users.v091122.txt') as f:
verified_users = set([f"@{i}" for i in f.read().split('\n') if len(i)])
def clean_text(text):
text = text.replace('\n', ' ').replace('\r', ' ').replace('\t', ' ')
text = re_url.sub('{URL}', text)
users = re_user.findall(text)
for user in users:
if user not in verified_users:
text = text.replace(user, '@user')
return text
def hash_tweet(target_tweet, num_perm=16):
def normalize_text(text):
text = text.translate(str.maketrans('', '', string.punctuation)) # remove punctuation
text = text.lower()
return text
def minhash(seq):
# https://skeptric.com/minhash/
m = MinHash(num_perm=num_perm, hashfunc=xxhash.xxh64_intdigest)
for s in seq:
m.update(s.encode('utf8'))
return LeanMinHash(m)
tokens = normalize_text(target_tweet['text']).split() # whitespace tokenization
return minhash(tokens)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Removes near-duplicates and tweets from top pct. of users.')
parser.add_argument('--src', type=str, required=True, help='Path to set of input tweets (.jl).')
parser.add_argument('--out', type=str, required=True, help='Path to output from preprocessing (.jl).')
parser.add_argument('--blacklist_pct', type=float, required=False, default=0.01,
help='Percent of most frequent users to ignore.')
args = parser.parse_args()
os.makedirs(os.path.dirname(args.out), exist_ok=True)
logging.info('1st pass - Collecting username counts ...')
n_input_tweets = 0
user_counter = Counter()
with open(args.src) as in_tweets_f:
for idx, jl_str in enumerate(in_tweets_f):
if idx % 1e6 == 0:
logging.info('1st pass - at idx %d' % idx)
tweet = json.loads(jl_str)
user_counter[tweet['username']] += 1
n_input_tweets += 1
logging.info('1st pass - Completed, found %d tweets' % n_input_tweets)
logging.info('1st pass - Found %d users' % len(user_counter.keys()))
top_users = [user for user, _ in user_counter.most_common()]
n_blacklisted_users = int(len(top_users) * args.blacklist_pct)
blacklisted_users = set(top_users[:n_blacklisted_users])
n_users = len(user_counter.keys())
pct_blacklisted_users = round((n_blacklisted_users / n_users) * 100, 2)
n_blacklisted_tweets = sum([user_counter[u] for u in blacklisted_users])
pct_blacklisted_tweets = round((n_blacklisted_tweets / sum(user_counter.values())) * 100, 2)
logging.info(
f"1st pass - Blacklisted {len(blacklisted_users)} users ({pct_blacklisted_users}%), "
f"ignoring {n_blacklisted_tweets} tweets ({pct_blacklisted_tweets}%)"
)
logging.info('2nd pass - Hashing and writing valid tweets ...')
written_hashes = set()
n_written = 0
n_ignored_by_user = 0
n_ignored_by_hash = 0
with open(args.src) as in_tweets_f:
with open(args.out, 'w') as out_tweets_f:
for idx, jl_str in enumerate(in_tweets_f):
if idx % 1e5 == 0:
logging.info('2nd pass - at idx %d' % idx)
tweet = json.loads(jl_str)
tweet['text'] = clean_text(tweet['text'])
tweet_hash = hash_tweet(tweet)
if tweet['username'] in blacklisted_users:
n_ignored_by_user += 1
elif tweet_hash in written_hashes:
n_ignored_by_hash += 1
else:
out_tweets_f.write(json.dumps(tweet) + '\n')
n_written += 1
written_hashes.add(tweet_hash)
logging.info(f"2nd pass - Completed, wrote {n_written} tweets.")
if n_ignored_by_user > 0:
logging.info(f"\tignored {n_ignored_by_user} by user blacklist")
if n_ignored_by_hash > 0:
logging.info(f"\tignored {n_ignored_by_hash} by hash collision")
logging.info("Done")
|