Kaggle competition: Recruit Restaurant Visitor Forecasting
<SOS> metric: RMSLE Kaggle data source: recruit-restaurant-visitor-forecasting<set_options>
The first is a w
del model
gc.collect()<import_modules>
dfs = { re.search('/([^/\.]*)\.csv', fn ).group(1):pd.read_csv(fn)for fn in glob.glob('.. /input/*.csv')}
print('data frames read:{}'.format(list(dfs.keys())))
 print('local variables with the same names are created.')
 for k, v in dfs.items() : locals() [k] = v
print('holidays at weekends are not special, right?')
 wkend_holidays = date_info.apply(( lambda x:(x.day_of_week=='Sunday' or x.day_of_week=='Saturday')and x.holiday_flg==1), axis=1)
 date_info.loc[wkend_holidays, 'holiday_flg'] = 0
print('add decreasing weights from now')
 date_info['weight'] =(( date_info.index + 1)/ len(date_info)) ** 8
tqdm.pandas()
print(os.listdir(".. /input"))<data_type_conversions>
print('weighted mean visitors for each(air_store_id, day_of_week, holiday_flag)or(air_store_id, day_of_week)')
 visit_data = air_visit_data.merge(date_info, left_on='visit_date', right_on='calendar_date', how='left')
 visit_data.drop('calendar_date', axis=1, inplace=True)
 visit_data['visitors'] = visit_data.visitors.map(pd.np.log1p)
 wmean = lambda x:(( x.weight * x.visitors ).sum() / x.weight.sum())
 visitors = visit_data.groupby(['air_store_id', 'day_of_week', 'holiday_flg'] ).apply(wmean ).reset_index()
visitors.rename(columns={0:'visitors'}, inplace=True)
 print('prepare to merge with date_info and visitors')
 sample_submission['air_store_id'] = sample_submission.id.map(lambda x: '_'.join(x.split('_')[:-1]))
sample_submission['calendar_date'] = sample_submission.id.map(lambda x: x.split('_')[2])
 sample_submission.drop('visitors', axis=1, inplace=True)
 sample_submission = sample_submission.merge(date_info, on='calendar_date', how='left')
 sample_submission = sample_submission.merge(visitors, on=['air_store_id', 'day_of_week', 'holiday_flg'], how='left')
 missings = sample_submission.visitors.isnull()
sample_submission.loc[missings, 'visitors'] = sample_submission[missings].merge(
 visitors[visitors.holiday_flg==0], on=('air_store_id', 'day_of_week'), how='left')['visitors_y'].values
missings = sample_submission.visitors.isnull()
sample_submission.loc[missings, 'visitors'] = sample_submission[missings].merge(
 visitors[['air_store_id', 'visitors']].groupby('air_store_id' ).mean().reset_index() , on='air_store_id', how='left')['visitors_y'].values
sample_submission['visitors'] = sample_submission.visitors.map(pd.np.expm1)
 print("done" )
for keys in train_df.columns:
if sum(train_df[keys].isna())!=0:
train_df[keys] = train_df[keys].fillna(0 )<categorify>
sample_submission2 = sample_submission.copy()
class pre_processing() :
def __init__(self, data):
self.data = data
def clean_contraction(self, data):
contraction_mapping = {
"Trump's" : 'trump is',"'cause": 'because',',cause': 'because',';cause': 'because',"ain't": 'am not','ain,t': 'am not',
'ain;t': 'am not','ain´t': 'am not','ain’t': 'am not',"aren't": 'are not',
'aren,t': 'are not','aren;t': 'are not','aren´t': 'are not','aren’t': 'are not',"can't": 'cannot',"can't've": 'cannot have','can,t': 'cannot','can,t,ve': 'cannot have',
'can;t': 'cannot','can;t;ve': 'cannot have',
'can´t': 'cannot','can´t´ve': 'cannot have','can’t': 'cannot','can’t’ve': 'cannot have',
"could've": 'could have','could,ve': 'could have','could;ve': 'could have',"couldn't": 'could not',"couldn't've": 'could not have','couldn,t': 'could not','couldn,t,ve': 'could not have','couldn;t': 'could not',
'couldn;t;ve': 'could not have','couldn´t': 'could not',
'couldn´t´ve': 'could not have','couldn’t': 'could not','couldn’t’ve': 'could not have','could´ve': 'could have',
'could’ve': 'could have',"didn't": 'did not','didn,t': 'did not','didn;t': 'did not','didn´t': 'did not',
'didn’t': 'did not',"doesn't": 'does not','doesn,t': 'does not','doesn;t': 'does not','doesn´t': 'does not',
'doesn’t': 'does not',"don't": 'do not','don,t': 'do not','don;t': 'do not','don´t': 'do not','don’t': 'do not',
"hadn't": 'had not',"hadn't've": 'had not have','hadn,t': 'had not','hadn,t,ve': 'had not have','hadn;t': 'had not',
'hadn;t;ve': 'had not have','hadn´t': 'had not','hadn´t´ve': 'had not have','hadn’t': 'had not','hadn’t’ve': 'had not have',"hasn't": 'has not','hasn,t': 'has not','hasn;t': 'has not','hasn´t': 'has not','hasn’t': 'has not',
"haven't": 'have not','haven,t': 'have not','haven;t': 'have not','haven´t': 'have not','haven’t': 'have not',"he'd": 'he would',
"he'd've": 'he would have',"he'll": 'he will',
"he's": 'he is','he,d': 'he would','he,d,ve': 'he would have','he,ll': 'he will','he,s': 'he is','he;d': 'he would',
'he;d;ve': 'he would have','he;ll': 'he will','he;s': 'he is','he´d': 'he would','he´d´ve': 'he would have','he´ll': 'he will',
'he´s': 'he is','he’d': 'he would','he’d’ve': 'he would have','he’ll': 'he will','he’s': 'he is',"how'd": 'how did',"how'll": 'how will',
"how's": 'how is','how,d': 'how did','how,ll': 'how will','how,s': 'how is','how;d': 'how did','how;ll': 'how will',
'how;s': 'how is','how´d': 'how did','how´ll': 'how will','how´s': 'how is','how’d': 'how did','how’ll': 'how will',
'how’s': 'how is',"i'd": 'i would',"i'll": 'i will',"i'm": 'i am',"i've": 'i have','i,d': 'i would','i,ll': 'i will',
'i,m': 'i am','i,ve': 'i have','i;d': 'i would','i;ll': 'i will','i;m': 'i am','i;ve': 'i have',"isn't": 'is not',
'isn,t': 'is not','isn;t': 'is not','isn´t': 'is not','isn’t': 'is not',"it'd": 'it would',"it'll": 'it will',"It's":'it is',
"it's": 'it is','it,d': 'it would','it,ll': 'it will','it,s': 'it is','it;d': 'it would','it;ll': 'it will','it;s': 'it is','it´d': 'it would','it´ll': 'it will','it´s': 'it is',
'it’d': 'it would','it’ll': 'it will','it’s': 'it is',
'i´d': 'i would','i´ll': 'i will','i´m': 'i am','i´ve': 'i have','i’d': 'i would','i’ll': 'i will','i’m': 'i am',
'i’ve': 'i have',"let's": 'let us','let,s': 'let us','let;s': 'let us','let´s': 'let us',
'let’s': 'let us',"ma'am": 'madam','ma,am': 'madam','ma;am': 'madam',"mayn't": 'may not','mayn,t': 'may not','mayn;t': 'may not',
'mayn´t': 'may not','mayn’t': 'may not','ma´am': 'madam','ma’am': 'madam',"might've": 'might have','might,ve': 'might have','might;ve': 'might have',"mightn't": 'might not','mightn,t': 'might not','mightn;t': 'might not','mightn´t': 'might not',
'mightn’t': 'might not','might´ve': 'might have','might’ve': 'might have',"must've": 'must have','must,ve': 'must have','must;ve': 'must have',
"mustn't": 'must not','mustn,t': 'must not','mustn;t': 'must not','mustn´t': 'must not','mustn’t': 'must not','must´ve': 'must have',
'must’ve': 'must have',"needn't": 'need not','needn,t': 'need not','needn;t': 'need not','needn´t': 'need not','needn’t': 'need not',"oughtn't": 'ought not','oughtn,t': 'ought not','oughtn;t': 'ought not',
'oughtn´t': 'ought not','oughtn’t': 'ought not',"sha'n't": 'shall not','sha,n,t': 'shall not','sha;n;t': 'shall not',"shan't": 'shall not',
'shan,t': 'shall not','shan;t': 'shall not','shan´t': 'shall not','shan’t': 'shall not','sha´n´t': 'shall not','sha’n’t': 'shall not',
"she'd": 'she would',"she'll": 'she will',"she's": 'she is','she,d': 'she would','she,ll': 'she will',
'she,s': 'she is','she;d': 'she would','she;ll': 'she will','she;s': 'she is','she´d': 'she would','she´ll': 'she will',
'she´s': 'she is','she’d': 'she would','she’ll': 'she will','she’s': 'she is',"should've": 'should have','should,ve': 'should have','should;ve': 'should have',
"shouldn't": 'should not','shouldn,t': 'should not','shouldn;t': 'should not','shouldn´t': 'should not','shouldn’t': 'should not','should´ve': 'should have',
'should’ve': 'should have',"that'd": 'that would',"that's": 'that is','that,d': 'that would','that,s': 'that is','that;d': 'that would',
'that;s': 'that is','that´d': 'that would','that´s': 'that is','that’d': 'that would','that’s': 'that is',"there'd": 'there had',
"there's": 'there is','there,d': 'there had','there,s': 'there is','there;d': 'there had','there;s': 'there is',
'there´d': 'there had','there´s': 'there is','there’d': 'there had','there’s': 'there is',
"they'd": 'they would',"they'll": 'they will',"they're": 'they are',"they've": 'they have',
'they,d': 'they would','they,ll': 'they will','they,re': 'they are','they,ve': 'they have','they;d': 'they would','they;ll': 'they will','they;re': 'they are',
'they;ve': 'they have','they´d': 'they would','they´ll': 'they will','they´re': 'they are','they´ve': 'they have','they’d': 'they would','they’ll': 'they will',
'they’re': 'they are','they’ve': 'they have',"wasn't": 'was not','wasn,t': 'was not','wasn;t': 'was not','wasn´t': 'was not',
'wasn’t': 'was not',"we'd": 'we would',"we'll": 'we will',"we're": 'we are',"we've": 'we have','we,d': 'we would','we,ll': 'we will',
'we,re': 'we are','we,ve': 'we have','we;d': 'we would','we;ll': 'we will','we;re': 'we are','we;ve': 'we have',
"weren't": 'were not','weren,t': 'were not','weren;t': 'were not','weren´t': 'were not','weren’t': 'were not','we´d': 'we would','we´ll': 'we will',
'we´re': 'we are','we´ve': 'we have','we’d': 'we would','we’ll': 'we will','we’re': 'we are','we’ve': 'we have',"what'll": 'what will',"what're": 'what are',"what's": 'what is',
"what've": 'what have','what,ll': 'what will','what,re': 'what are','what,s': 'what is','what,ve': 'what have','what;ll': 'what will','what;re': 'what are',
'what;s': 'what is','what;ve': 'what have','what´ll': 'what will',
'what´re': 'what are','what´s': 'what is','what´ve': 'what have','what’ll': 'what will','what’re': 'what are','what’s': 'what is',
'what’ve': 'what have',"where'd": 'where did',"where's": 'where is','where,d': 'where did','where,s': 'where is','where;d': 'where did',
'where;s': 'where is','where´d': 'where did','where´s': 'where is','where’d': 'where did','where’s': 'where is',
"who'll": 'who will',"who's": 'who is','who,ll': 'who will','who,s': 'who is','who;ll': 'who will','who;s': 'who is',
'who´ll': 'who will','who´s': 'who is','who’ll': 'who will','who’s': 'who is',"won't": 'will not','won,t': 'will not','won;t': 'will not',
'won´t': 'will not','won’t': 'will not',"wouldn't": 'would not','wouldn,t': 'would not','wouldn;t': 'would not','wouldn´t': 'would not',
'wouldn’t': 'would not',"you'd": 'you would',"you'll": 'you will',"you're": 'you are','you,d': 'you would','you,ll': 'you will',
'you,re': 'you are','you;d': 'you would','you;ll': 'you will',
'you;re': 'you are','you´d': 'you would','you´ll': 'you will','you´re': 'you are','you’d': 'you would','you’ll': 'you will','you’re': 'you are',
'´cause': 'because','’cause': 'because',"you've": "you have","could'nt": 'could not',
"havn't": 'have not',"here’s": "here is",'i""m': 'i am',"i'am": 'i am',"i'l": "i will","i'v": 'i have',"wan't": 'want',"was'nt": "was not","who'd": "who would",
"who're": "who are","who've": "who have","why'd": "why would","would've": "would have","y'all": "you all","y'know": "you know","you.i": "you i",
"your'e": "you are","arn't": "are not","agains't": "against","c'mon": "common","doens't": "does not",'don""t': "do not","dosen't": "does not",
"dosn't": "does not","shoudn't": "should not","that'll": "that will","there'll": "there will","there're": "there are",
"this'll": "this all","u're": "you are", "ya'll": "you all","you'r": "you are","you’ve": "you have","d'int": "did not","did'nt": "did not","din't": "did not","dont't": "do not","gov't": "government",
"i'ma": "i am","is'nt": "is not","‘I":'I',
'ᴀɴᴅ':'and','ᴛʜᴇ':'the','ʜᴏᴍᴇ':'home','ᴜᴘ':'up','ʙʏ':'by','ᴀᴛ':'at','…and':'and','civilbeat':'civil beat',\
'TrumpCare':'Trump care','Trumpcare':'Trump care', 'OBAMAcare':'Obama care','ᴄʜᴇᴄᴋ':'check','ғᴏʀ':'for','ᴛʜɪs':'this','ᴄᴏᴍᴘᴜᴛᴇʀ':'computer',\
'ᴍᴏɴᴛʜ':'month','ᴡᴏʀᴋɪɴɢ':'working','ᴊᴏʙ':'job','ғʀᴏᴍ':'from','Sᴛᴀʀᴛ':'start','gubmit':'submit','CO₂':'carbon dioxide','ғɪʀsᴛ':'first',\
'ᴇɴᴅ':'end','ᴄᴀɴ':'can','ʜᴀᴠᴇ':'have','ᴛᴏ':'to','ʟɪɴᴋ':'link','ᴏғ':'of','ʜᴏᴜʀʟʏ':'hourly','ᴡᴇᴇᴋ':'week','ᴇɴᴅ':'end','ᴇxᴛʀᴀ':'extra',\
'Gʀᴇᴀᴛ':'great','sᴛᴜᴅᴇɴᴛs':'student','sᴛᴀʏ':'stay','ᴍᴏᴍs':'mother','ᴏʀ':'or','ᴀɴʏᴏɴᴇ':'anyone','ɴᴇᴇᴅɪɴɢ':'needing','ᴀɴ':'an','ɪɴᴄᴏᴍᴇ':'income',\
'ʀᴇʟɪᴀʙʟᴇ':'reliable','ғɪʀsᴛ':'first','ʏᴏᴜʀ':'your','sɪɢɴɪɴɢ':'signing','ʙᴏᴛᴛᴏᴍ':'bottom','ғᴏʟʟᴏᴡɪɴɢ':'following','Mᴀᴋᴇ':'make',\
'ᴄᴏɴɴᴇᴄᴛɪᴏɴ':'connection','ɪɴᴛᴇʀɴᴇᴛ':'internet','financialpost':'financial post', 'ʜaᴠᴇ':' have ', 'ᴄaɴ':' can ', 'Maᴋᴇ':' make ', 'ʀᴇʟɪaʙʟᴇ':' reliable ', 'ɴᴇᴇᴅ':' need ',
'ᴏɴʟʏ':' only ', 'ᴇxᴛʀa':' extra ', 'aɴ':' an ', 'aɴʏᴏɴᴇ':' anyone ', 'sᴛaʏ':' stay ', 'Sᴛaʀᴛ':' start', 'SHOPO':'shop',
}
def clean_contractions(text, mapping):
specials = ["’", "‘", "´", "`"]
for s in specials:
text = text.replace(s, "'")
 text = ' '.join([mapping[t] if t in mapping else t for t in text.split(" ")])
 return text
data = data.progress_apply(lambda x: clean_contractions(x, contraction_mapping))
return data
def remove_punc(self, data):
punct_mapping = {"_":" ", "`":" "}
punct = "/-'?!.,
punct += '©^®` <→°€™› ♥←×§″′Â█½à…“★”–●â►−¢²¬░¶↑±¿▾═¦║―¥▓—‹─▒：¼⊕▼▪†■’▀¨▄♫☆é¯♦¤▲è¸¾Ã⋅‘∞∙）↓、│（»，♪╩╚³・╦╣╔╗▬❤ïØ¹≤‡√'
def clean_special_chars(text, punct, mapping):
for p in mapping:
text = text.replace(p, mapping[p])
 for p in punct:
text = text.replace(p, f' {p} ')
 return text
data = data.astype(str)
 data = data.progress_apply(lambda x: clean_special_chars(x, punct, punct_mapping))
return data
def lower(self, data):
data = data.astype(str ).progress_apply(lambda x: x.lower())
 return data
def remove_stop_words(self, data):
stop = {'a',
'about',
'above',
'after',
'again',
'against',
'ain',
'all',
'am',
'an',
'and',
'any',
'are',
'aren',
"aren't",
'as',
'at',
'be',
'because',
'been',
'before',
'being',
'below',
'between',
'both',
'but',
'by',
'can',
'couldn',
"couldn't",
'd',
'did',
'didn',
"didn't",
'do',
'does',
'doesn',
"doesn't",
'doing',
'don',
"don't",
'down',
'during',
'each',
'few',
'for',
'from',
'further',
'had',
'hadn',
"hadn't",
'has',
'hasn',
"hasn't",
'have',
'haven',
"haven't",
'having',
'he',
'her',
'here',
'hers',
'herself',
'him',
'himself',
'his',
'how',
'i',
'if',
'in',
'into',
'is',
'isn',
"isn't",
'it',
"it's",
'its',
'itself',
'just',
'll',
'm',
'ma',
'me',
'mightn',
"mightn't",
'more',
'most',
'mustn',
"mustn't",
'my',
'myself',
'needn',
"needn't",
'no',
'nor',
'not',
'now',
'o',
'of',
'off',
'on',
'once',
'only',
'or',
'other',
'our',
'ours',
'ourselves',
'out',
'over',
'own',
're',
's',
'same',
'shan',
"shan't",
'she',
"she's",
'should',
"should've",
'shouldn',
"shouldn't",
'so',
'some',
'such',
't',
'than',
'that',
"that'll",
'the',
'their',
'theirs',
'them',
'themselves',
'then',
'there',
'these',
'they',
'this',
'those',
'through',
'to',
'too',
'under',
'until',
'up',
've',
'very',
'was',
'wasn',
"wasn't",
'we',
'were',
'weren',
"weren't",
'what',
'when',
'where',
'which',
'while',
'who',
'whom',
'why',
'will',
'with',
'won',
"won't",
'wouldn',
"wouldn't",
'y',
'you',
"you'd",
"you'll",
"you're",
"you've",
'your',
'yours',
'yourself',
'yourselves'}
data = data.progress_apply(lambda x:(' ' ).join([i for i in x.split(' ')if i not in stop]))
return data

 def clean_spaces(self, data):
data = data.progress_apply(lambda x: correct_spaces(x))
return data
def preprocess(self):
print('correct contractions')
 self.data = self.clean_contraction(self.data)
 print('done')
 return self.data
train_df['comment_text'] = pre_processing(train_df['comment_text'] ).preprocess()
test_df['comment_text'] = pre_processing(test_df['comment_text'] ).preprocess()
str_='lstm preprocess done'
os.system('echo '+str_ )<define_variables>
test_df = pd.read_csv('.. /input/sample_submission.csv')
 test_df['store_id'], test_df['visit_date'] = test_df['id'].str[:20], test_df['id'].str[21:]
test_df.drop(['visitors'], axis=1, inplace=True)
 test_df['visit_date'] = pd.to_datetime(test_df['visit_date'])
 air_data = pd.read_csv('.. /input/air_visit_data.csv', parse_dates=['visit_date'])
 air_data['dow'] = air_data['visit_date'].dt.dayofweek
train = air_data[air_data['visit_date'] > '2017-01-28'].reset_index()
train['dow'] = train['visit_date'].dt.dayofweek
test_df['dow'] = test_df['visit_date'].dt.dayofweek
aggregation = {'visitors' :{'total_visitors' : 'median'}}
agg_data = train.groupby(['air_store_id', 'dow'] ).agg(aggregation ).reset_index()
agg_data.columns = ['air_store_id','dow','visitors']
agg_data['visitors'] = agg_data['visitors']
merged = pd.merge(test_df, agg_data, how='left', left_on=[
'store_id','dow'], right_on=['air_store_id','dow'])
 final = merged[['id','visitors']]
final.fillna(0, inplace=True)
 dfs = { re.search('/([^/\.]*)\.csv', fn ).group(1):pd.read_csv(
 fn)for fn in glob.glob('.. /input/*.csv')}
for k, v in dfs.items() : locals() [k] = v
weekend_hdays = date_info.apply(
(lambda x:(x.day_of_week=='Sunday' or x.day_of_week=='Saturday')
 and x.holiday_flg==1), axis=1)
 date_info.loc[weekend_hdays, 'holiday_flg'] = 0
date_info['weight'] =(date_info.index + 1)/ len(date_info)
 visit_data = air_visit_data.merge(
 date_info, left_on='visit_date', right_on='calendar_date', how='left')
 visit_data.drop('calendar_date', axis=1, inplace=True)
 visit_data['visitors'] = visit_data.visitors.map(pd.np.log1p)
 wmean = lambda x:(( x.weight * x.visitors ).sum() / x.weight.sum())
 visitors = visit_data.groupby(
 ['air_store_id', 'day_of_week', 'holiday_flg'] ).apply(wmean ).reset_index()
visitors.rename(columns={0:'visitors'}, inplace=True)
 sample_submission['air_store_id'] = sample_submission.id.map(
 lambda x: '_'.join(x.split('_')[:-1]))
sample_submission['calendar_date'] = sample_submission.id.map(lambda x: x.split('_')[2])
 sample_submission.drop('visitors', axis=1, inplace=True)
 sample_submission = sample_submission.merge(date_info, on='calendar_date', how='left')
 sample_submission = sample_submission.merge(
 visitors, on=['air_store_id', 'day_of_week', 'holiday_flg'], how='left')
 missings = sample_submission.visitors.isnull()
sample_submission.loc[missings, 'visitors'] = sample_submission[missings].merge(
 visitors[visitors.holiday_flg==0], on=(
 'air_store_id', 'day_of_week'), how='left')['visitors_y'].values
missings = sample_submission.visitors.isnull()
sample_submission.loc[missings, 'visitors'] = sample_submission[missings].merge(
 visitors[['air_store_id', 'visitors']].groupby('air_store_id' ).mean().reset_index() ,
on='air_store_id', how='left')['visitors_y'].values
sample_submission['visitors'] = sample_submission.visitors.map(pd.np.expm1)
 sample_submission = sample_submission[['id', 'visitors']]
final['visitors'][final['visitors'] ==0] = sample_submission['visitors'][final['visitors'] ==0]
sub_file = final.copy()
sub_file['visitors'] = 2/(1/final['visitors'] + 1/sample_submission['visitors'] )
IDENTITY_COLUMNS = [
'male', 'female', 'homosexual_gay_or_lesbian', 'christian', 'jewish',
'muslim', 'black', 'white', 'psychiatric_or_mental_illness'
]
AUX_COLUMNS = ['target', 'severe_toxicity', 'obscene', 'identity_attack', 'insult', 'threat']
TARGET_COLUMN = ['target']<feature_engineering>
new = sub_file.copy()
CHARS_TO_REMOVE = '!"
“”’'∞θ÷α•à−β∅³π‘₹´°£€\×™√²—'
x_train = train_df['comment_text'].astype(str)
 x_test = test_df['comment_text'].astype(str)
 tokenizer = Tokenizer(filters=CHARS_TO_REMOVE, lower=False)
 tokenizer.fit_on_texts(tqdm(list(x_train)+ list(x_test)))
 x_train = tokenizer.texts_to_sequences(tqdm(x_train))
x_test = tokenizer.texts_to_sequences(x_test)
 MAX_LEN = 220
x_train = pad_sequences(tqdm(x_train), maxlen=MAX_LEN)
 x_test = pad_sequences(x_test, maxlen=MAX_LEN)
 y_train = train_df['target'].values
y_identity =(train_df[IDENTITY_COLUMNS].values>0.5 ).astype(int)
 y_aux_train = train_df[AUX_COLUMNS].values
str_='lstm tokenizer done'
os.system('echo '+str_ )<feature_engineering>
new['visitors'] = sub_file['visitors']*.2 + sample_submission2['visitors']*.8
<load_pretrained><EOS> 
new.to_csv("four_kernel_weighted.csv", float_format='%.4f', index=None )