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prepocess.py
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import time
import warnings
import re
from tqdm import tqdm
from Ref_Data import APPO
from Ref_Data import replace_word
import input
from nltk.corpus import stopwords
from nltk.stem.wordnet import WordNetLemmatizer
import createFeature
import json
warnings.filterwarnings("ignore")
PATH='data/'
def cleanComment(comments):
"""
This function receives comments and returns clean word-list
"""
def correct_typos(comment):
pattern = '(motha fuker)|(motha fucker)|(motha fukkah)|(motha fukker)|(mother fuckers{0,})|(mother fukah)|(mother fuker)|(mother fukkah)|(mother fukker)|'
pattern += '(mutha fucker)|(mutha fukah)|(mutha fuker)|(mutha fukkah)|(mutha fukker)|(mu\,the\,rfu\,ckers)|(mothjer fucker)|(motha fuckas{0,})'
comment = re.sub(pattern, ' motherfucker ', comment)
comment = re.sub('( f off)|(f u c k o f f)', ' fuck off ', comment)
comment = re.sub('(f u c k t a r d)', ' fucktard ', comment)
comment = re.sub('(go f yourself)', ' go fuck yourself ', comment)
comment = re.sub('(f u c k e r)|(fu\*er)', ' fucker ', comment)
comment = re.sub('( fuc king)|( fuc ing)|(f u c k i n g)|(fuck!ng)|(f\. u\. c\. k\. i\. n\. g)|(fucking+)', ' fucking ', comment)
comment = re.sub('(f u c k)|( fuc k)|(f uc k)|(fu\.ck)|(f\*ck)|(fuck{3,})|(fffffffff uuuuuu uuuuu ccccccccccccc kkkkk)|(f\. u\. c\. k)|(fu\,ck )', ' fuck ', comment)
comment = re.sub('(fuck){2,}', ' fuck fuck fuck fuck ', comment)
comment = re.sub('(blahblahman\d+)', ' blah blah man ', comment)
comment = re.sub('( f you)|(fack you)|(fack u)',' fuck you ',comment)
comment = re.sub('(b i t c h)|(b!tch)', 'bitch', comment)
comment = re.sub('(c u n t s)|(c\,un\,t)', ' cunt ', comment)
comment = re.sub('(c u n t)', ' cunt ', comment)
comment = re.sub('(d a m e)', ' dame ', comment)
comment = re.sub('(w\,hor\,es)', ' whore ', comment)
comment = re.sub('(idi\.o\.t)|(id\.iot)', ' idiot ', comment)
comment = re.sub('(st\.u\.p\.id)|(s\'tu\.pi\.d)', ' stupid ', comment)
comment = re.sub('(w a n k e r)', ' wanker ', comment)
comment = re.sub('(d i c k h e a d)', ' dickhead ', comment)
comment = re.sub('(d e m o n s)', ' demon ', comment)
comment = re.sub('(lov3r)', ' lover ', comment)
comment = re.sub('(f@ggot)|(fagg0t)|(fa ggot)|(f\.a\.g\.g\.o\.t)|(f a g g o t)|(fa\,gg\,ot)', ' faggot ', comment)
comment = re.sub('(b u m s)', ' bum ', comment)
comment = re.sub('( c ock)|(c\*ck)', ' cock ', comment)
comment = re.sub('(a r m p i t s)', ' armpit ', comment)
comment = re.sub('(s m e l l)', ' smell ', comment)
comment = re.sub('(s t i n k y)', ' stinky ', comment)
comment = re.sub('(s u ck)|(s u c k)|($uck)|(su ck )|( suck{3,})', ' suck ', comment)
comment = re.sub('(d i c k)|(d!ck)|( di ck )', ' dick ', comment)
comment = re.sub('(pen!s)', ' penis ', comment)
comment = re.sub('( ass monkey)', ' asshole ', comment)
comment = re.sub('( p i s s)|(p\.i\.s\.s)', ' piss ', comment)
comment = re.sub('( h e l l)', ' hell ', comment)
comment = re.sub('( a s s)|(a\$\$)( a s s )',' ass ',comment)
comment = re.sub('( w t f)',' wtf ',comment)
comment = re.sub('(k!kes{0,})', ' kike ', comment)
comment = re.sub('(hijos de puta)', ' son of bitch ', comment)
comment = re.sub('( s t f u)',' stfu ' ,comment)
comment = re.sub('(n i g g e r)|(nig gger)|(n e g r o)|(n!ger)|(n!gger)|(n!gga)|(n!gg@r)','nigger',comment)
comment = re.sub('( b!\+ch)|( b!tch)|( bi\+ch)|( b!t\*h)|(b\,itch\,es)|(bi\,tc\,h)',' bitch ',comment)
comment = re.sub('( s\.o\.b\.)|( s\.o\.b)',' sob ',comment)
comment = re.sub('( sh!t)|( shi\+)|( sh!\+)|( shi t )',' shit ',comment)
comment = re.sub('( p ussy)', ' pussy ', comment)
comment = re.sub('( let\'s )', ' let us ', comment)
comment = re.sub('(\'s )', ' ', comment)
comment = re.sub('(blow jobs)|(blowjobs)|(blow job)', ' blowjob ', comment)
comment = re.sub('(go fack)','go fuck',comment)
comment = re.sub('( \d\d:\d\d)',replace_word['num'],comment)
comment = re.sub('@', 'a', comment)
comment = re.sub('\$', 's', comment)
return comment
def deal_emoji(comment):
repl = {
"<3": " good ",
":d": " good ",
":dd": " good ",
":p": " good ",
"8\)": " good ",
":-\)": " good ",
":\)": " good ",
";\)": " good ",
"\(-:": " good ",
"\(:": " good ",
"yay!": " good ",
"yay": " good ",
"yaay": " good ",
"yaaay": " good ",
"yaaaay": " good ",
"yaaaaay": " good ",
":'\)": " sad ",
":-\(": " bad ",
":\(": " sad ",
":s": " sad ",
":-s": " sad ",
":/": " worry ",
":>": " angry ",
}
for pattern,word in repl.items():
comment = re.sub(pattern, word, comment)
return comment
patternLink = '(https?|ftp|file)://[-A-Za-z0-9+&@#/%?=~_|!:,.;]+[-A-Za-z0-9+&@#/%=~_|]'
patternIP = '\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}'
patternEmail = '[A-Za-z\d]+([-_.][A-Za-z\d]+)*@([A-Za-z\d]+[-.])+[A-Za-z\d]{2,4}'
patternNum = '(\d+\.\d+)|(\d+\,)|(\d+\-)|(\d+\:)|(\$\d+)|(\$\d+\.\d+)'
patternRgb = '#(?:[0-9a-f]{3}){1,2}'
from nltk.tokenize import TweetTokenizer
tknzr = TweetTokenizer()
lem = WordNetLemmatizer()
clean_comments = []
for comment in tqdm(comments):
comment = comment.lower()
# 去除邮箱 邮箱先去 再去IP
comment = re.sub(patternEmail, replace_word['link'], comment)
# 去除IP
comment = re.sub(patternIP, replace_word['link'], comment)
# 去除usernames
comment = re.sub("\[\[.*\]", " ", comment)
# 去除网址
comment = re.sub(patternLink, replace_word['link'], comment)
comment = re.sub(patternRgb, " ", comment)
comment = correct_typos(comment)
comment = deal_emoji(comment)
comment = re.sub("_", ' ', comment)
comment = re.sub("#", ' # ', comment)
# 去除非ascii字符
comment = re.sub("[^\x00-\x7F]+", " ", comment)
comment = re.sub(patternNum,replace_word['num'],comment)
comment = re.sub("\.", ' . ', comment) #帮助分词
comment = re.sub('[\|=\*/\`\~\\\\\}\{]+', ' ', comment)
comment = re.sub('[\"]+', ' " ', comment)
comment = re.sub('\'{2,}', ' " ', comment)
# 分词
words = tknzr.tokenize(comment)
words = [APPO[word] if word in APPO else word for word in words]
# 数字统一
for i in range(len(words)):
if words[i].isdigit() and words[i]!='911':
words[i] = replace_word['num']
comment = " ".join(words)
comment = comment.lower()
comment = re.sub("-", ' ', comment)
comment = re.sub('\s+',' ',comment)
comment = re.sub('(\. )+',' . ',comment)
comment = re.sub('(\. \.)+',' . ',comment)
comment = re.sub('("")+', '', comment)
comment = re.sub('( \' )', ' " ', comment)
comment = re.sub('(\( \))+', '', comment)
# 分词
words = tknzr.tokenize(comment)
words = [APPO[word] if word in APPO else word for word in words]
comment = " ".join(words)
for i in range(97,97+26):
ch = chr(i)
comment = re.sub(ch +'{3,}',ch , comment)
# 纠正拼写错误/
# for word,pos in tknzr(comment):
# if w_dict.check(word) == False:
# try:
# comment = comment[:pos] + \
# w_dict.suggest(word)[0] + \
# comment[pos+len(word):]
# print(word,w_dict.suggest(word)[0])
# except IndexError:
# continue
clean_comments.append(comment)
return clean_comments
def clean_dataset(dataset,filename):
import multiprocessing as mlp
results = []
pool = mlp.Pool(mlp.cpu_count())
comments = list(dataset['comment_text'])
aver_t = int(len(dataset) / mlp.cpu_count()) + 1
for i in range(mlp.cpu_count()):
result = pool.apply_async(cleanComment, args=(comments[i * aver_t:(i + 1) * aver_t],))
results.append(result)
pool.close()
pool.join()
clean_comments = []
for result in results:
clean_comments.extend(result.get())
dataset['comment_text'] = clean_comments
dataset.to_csv(PATH+filename,index=False)
def splitTarget(filename):
list_classes = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]
labels=input.read_dataset(filename,list_classes)
labels.to_csv(PATH+'labels.csv',index=False)
def translation_sub(dataset,file):
# 将训练测试集中的外国语言替换成翻译后的
with open('translation.json','r') as f:
translation = json.loads(f.read())
for key,value in tqdm(translation.items()):
if key[:2] == file:
index = int(key[2:])
dataset.loc[index,'comment_text'] = value[0]
return dataset
def pipeline(
file = ( 'train.csv','test.csv',
# 'train_fr.csv','train_es.csv','train_de.csv'
)
):
for filename in tqdm(file):
dataset = input.read_dataset(filename)
# dataset = translation_sub(dataset,filename[:2])
dataset.fillna(replace_word['unknow'],inplace=True)
dataset = createFeature.countFeature(dataset)
clean_dataset(dataset,'clean_'+filename)
createFeature.get_char_text()
from ConvAIData import get_label_feature
get_label_feature()
# from createFeature import LDAFeature
# from Ref_Data import NUM_TOPIC
# LDAFeature(NUM_TOPIC)
if __name__ == "__main__":
splitTarget('train.csv')
pipeline()