Kaggle competition: Google Analytics Customer Revenue Prediction
<SOS> metric: RMSE Kaggle data source: google-analytics-customer-revenue-prediction<save_to_csv>
def to_na(df):
to_NA_cols = ['trafficSource_adContent','trafficSource_adwordsClickInfo.adNetworkType',
'trafficSource_adwordsClickInfo.slot','trafficSource_adwordsClickInfo.gclId',
'trafficSource_keyword','trafficSource_referralPath','customDimensions_value']
to_0_cols = ['totals_transactionRevenue','trafficSource_adwordsClickInfo.page','totals_sessionQualityDim','totals_bounces',
'totals_timeOnSite','totals_newVisits','totals_pageviews','customDimensions_index','totals_transactions','totals_totalTransactionRevenue']
to_true_cols = ['trafficSource_adwordsClickInfo.isVideoAd']
to_false_cols = ['trafficSource_isTrueDirect']
df[to_NA_cols] = df[to_NA_cols].fillna('NA')
 df[to_0_cols] = df[to_0_cols].fillna(0)
 df[to_true_cols] = df[to_true_cols].fillna(True)
 df[to_false_cols] = df[to_false_cols].fillna(False)
 return df
def encode_date(df):
fld = pd.to_datetime(df['date'], infer_datetime_format=True)
 attrs = ['Year', 'Month', 'Week', 'Day', 'Dayofweek', 'Dayofyear',
'Is_month_end', 'Is_month_start', 'Is_quarter_end',
'Is_quarter_start', 'Is_year_end', 'Is_year_start','Hour']
for attr in attrs:
df['Date_'+attr] = getattr(fld.dt,attr.lower())
 return df
def weird_na(df):
cols_to_replace = {
'socialEngagementType' : 'Not Socially Engaged',
'device_browserSize' : 'not available in demo dataset',
'device_flashVersion' : 'not available in demo dataset',
'device_browserVersion' : 'not available in demo dataset',
'device_language' : 'not available in demo dataset',
'device_mobileDeviceBranding' : 'not available in demo dataset',
'device_mobileDeviceInfo' : 'not available in demo dataset',
'device_mobileDeviceMarketingName' : 'not available in demo dataset',
'device_mobileDeviceModel' : 'not available in demo dataset',
'device_mobileInputSelector' : 'not available in demo dataset',
'device_operatingSystemVersion' : 'not available in demo dataset',
'device_screenColors' : 'not available in demo dataset',
'device_screenResolution' : 'not available in demo dataset',
'geoNetwork_city' : 'not available in demo dataset',
'geoNetwork_cityId' : 'not available in demo dataset',
'geoNetwork_latitude' : 'not available in demo dataset',
'geoNetwork_longitude' : 'not available in demo dataset',
'geoNetwork_metro' : ['not available in demo dataset', '(not set)'],
'geoNetwork_networkDomain' : 'unknown.unknown',
'geoNetwork_networkLocation' : 'not available in demo dataset',
'geoNetwork_region' : 'not available in demo dataset',
'trafficSource_adwordsClickInfo.criteriaParameters' : 'not available in demo dataset',
'trafficSource_campaign' : '(not set)',
'trafficSource_keyword' : '(not provided)',
'networkDomain': '(not set)',
'city': '(not set)',
}
df = df.replace(cols_to_replace,'NA')
 return df
def del_const(df):
const_col = []
for col in df.columns:
if df[col].nunique() == 1 and df[col].isnull().sum() ==0 :
const_col.append(col)
 df.drop(const_col,axis=1,inplace=True)
 return df, const_col
def json_it(df):
JSON_COLUMNS = ['device', 'geoNetwork', 'totals', 'trafficSource']
for column in JSON_COLUMNS:
column_as_df = json_normalize(df[column])
 column_as_df.columns = [f"{column}_{subcolumn}" for subcolumn in column_as_df.columns]
df = df.drop(column, axis=1 ).merge(column_as_df, right_index=True, left_index=True)
 df['customDimensions']=df['customDimensions'].apply(literal_eval)
 df['customDimensions']=df['customDimensions'].str[0]
df['customDimensions']=df['customDimensions'].apply(lambda x: {'index':np.NaN,'value':np.NaN} if pd.isnull(x)else x)
 column_as_df = json_normalize(df['customDimensions'])
 column_as_df.columns = [f"customDimensions_{subcolumn}" for subcolumn in column_as_df.columns]
df = df.drop('customDimensions', axis=1 ).merge(column_as_df, right_index=True, left_index=True)
 return df
def convert_it(df):
df = weird_na(df)
 df = to_na(df)
 df_train = encode_date(df)
 return df
def fix_type(df):
try:
df.drop('trafficSource_campaignCode',axis=1,inplace=True)
 except:
pass
df['totals_transactionRevenue'] = df['totals_transactionRevenue'].fillna(0 ).astype(float)
 to_int = ['totals_bounces','totals_newVisits','totals_pageviews',
'customDimensions_index','totals_hits','totals_sessionQualityDim',
'totals_visits','totals_timeOnSite','trafficSource_adwordsClickInfo.page',
'totals_transactions','totals_totalTransactionRevenue']
for col in to_int :
df[col] = df[col].astype(int)
 return df
def load_it(csv_path,name):
CONST_COLLUMNS = ['socialEngagementType','device_browserSize',
'device_browserVersion','device_flashVersion',
'device_language','device_mobileDeviceBranding',
'device_mobileDeviceInfo','device_mobileDeviceMarketingName',
'device_mobileDeviceModel','device_mobileInputSelector',
'device_operatingSystemVersion','device_screenColors',
'device_screenResolution','geoNetwork_cityId',
'geoNetwork_latitude','geoNetwork_longitude',
'geoNetwork_networkLocation',
'trafficSource_adwordsClickInfo.criteriaParameters',]
JSON_COLUMNS = ['device', 'geoNetwork', 'totals', 'trafficSource']
dfs = pd.read_csv(csv_path, sep=',',
parse_dates=['date'],
converters={column: json.loads for column in JSON_COLUMNS},
dtype={'fullVisitorId': 'str'},
chunksize = 200000)
 for idx,df in enumerate(dfs):
print(idx)
 df.reset_index(drop = True,inplace = True)
 df = json_it(df)
 df = convert_it(df)
 df.drop(CONST_COLLUMNS,axis=1,inplace=True)
 df.drop('hits',axis=1,inplace=True)
 df = fix_type(df)
 df.to_pickle(f'{name}_{idx}.pkl')
 del df
gc.collect()
print('Done')
 load_it('.. /input/ga-customer-revenue-prediction/train_v2.csv','train')
 load_it('.. /input/ga-customer-revenue-prediction/test_v2.csv','test' )
y_preds = RNN_glove.predict(X_test)
 y_pred_labels = y_preds.round().astype('int')
 test_df['target'] = y_pred_labels
submission = test_df.loc[:, ['id', 'target']]
submission.to_csv('model_4_submission.csv', index=False)
 submission.head()<train_on_grid>
data_root = './'
print(os.listdir(data_root))
pd.set_option('display.max_rows',200)
RNN_final = create_LSTM_RNN_GloVe()
history = RNN_final.fit(X, y, epochs=100, batch_size=128 )<predict_on_test>
def load_data(data='train',n=2):
df = pd.DataFrame()
for i in range(n):
if data=='train':
if i > 8 :
break
dfpart = pd.read_pickle(pjoin(data_root,f'train_{i}.pkl'))
elif data=='test':
if i > 2 :
break
dfpart = pd.read_pickle(pjoin(data_root,f'test_{i}.pkl'))
df = pd.concat([df,dfpart])
 del dfpart
return df
y_preds = RNN_final.predict(X_test)
 y_pred_labels = y_preds.round().astype('int')
 test_df['target'] = y_pred_labels<prepare_output>
df_train = load_data(n=9)
 df_test = load_data('test',n=4 )
submission = test_df.loc[:, ['id', 'target']]
submission.head()<save_to_csv>
df = pd.concat([df_train, df_test] )
submission.to_csv('final_submission.csv', index=False )<load_from_url>
col_drop = ['Date_Year', 'Date_Month', 'Date_Week','Date_Hour','device_isMobile','device_deviceCategory',
'Date_Day', 'Date_Dayofweek', 'Date_Dayofyear', 'Date_Is_month_end',
'Date_Is_month_start', 'Date_Is_quarter_end', 'Date_Is_quarter_start',
'Date_Is_year_end', 'Date_Is_year_start','totals_visits',
'date','visitId','totals_totalTransactionRevenue','geoNetwork_city','geoNetwork_continent',
'geoNetwork_metro','geoNetwork_networkDomain',
'geoNetwork_region','geoNetwork_subContinent','trafficSource_adContent',
'trafficSource_adwordsClickInfo.adNetworkType','trafficSource_adwordsClickInfo.gclId',
'trafficSource_adwordsClickInfo.slot','trafficSource_campaign',
'trafficSource_keyword','trafficSource_referralPath','trafficSource_medium',
'customDimensions_value','customDimensions_index','trafficSource_source',
'totals_bounces','visitNumber','totals_newVisits']
df.drop(col_drop, axis=1, inplace=True )
!wget --quiet https://raw.githubusercontent.com/tensorflow/models/master/official/nlp/bert/tokenization.py<set_options>
country_drop=df.groupby('geoNetwork_country')['totals_transactions'].sum() [df.groupby('geoNetwork_country')['totals_transactions'].sum().sort_values() <4].index.tolist()
df.loc[df[df.geoNetwork_country.isin(country_drop)].index,'geoNetwork_country'] = 'NaN'
df.loc[df[~df.device_browser.isin(['Edge', 'Internet Explorer', 'Firefox', 'Safari', 'Chrome'])].index,'device_browser'] = 'NaN'
df.loc[df[~df.device_operatingSystem.isin(['Android', 'iOS', 'Linux', 'Chrome OS', 'Windows', 'Macintosh'])].index,'device_operatingSystem'] = 'NaN'
pd.set_option('display.max_rows', 500)
 pd.set_option('display.max_columns', 500)
 pd.set_option('display.width', 1000)
 SEED = 1337<load_from_csv>
col_lb = ['channelGrouping','device_browser','device_operatingSystem', 'geoNetwork_country',
'trafficSource_adwordsClickInfo.isVideoAd','trafficSource_isTrueDirect']
for col in col_lb:
lb = LabelEncoder()
df[col]=lb.fit_transform(df[col] )
df_train = pd.read_csv('.. /input/nlp-getting-started/train.csv', dtype={'id': np.int16, 'target': np.int8})
 df_test = pd.read_csv('.. /input/nlp-getting-started/test.csv', dtype={'id': np.int16})
 print('Training Set Shape = {}'.format(df_train.shape))
print('Training Set Memory Usage = {:.2f} MB'.format(df_train.memory_usage().sum() / 1024**2))
print('Test Set Shape = {}'.format(df_test.shape))
print('Test Set Memory Usage = {:.2f} MB'.format(df_test.memory_usage().sum() / 1024**2))<count_unique_values>
to_median = ['channelGrouping','device_browser','device_operatingSystem','geoNetwork_country','trafficSource_adwordsClickInfo.isVideoAd','trafficSource_isTrueDirect','trafficSource_adwordsClickInfo.page']
to_sum =['totals_hits','totals_pageviews','totals_timeOnSite','totals_transactionRevenue', 'totals_transactions']
to_mean =['totals_hits','totals_pageviews','totals_sessionQualityDim']
to_std = ['totals_hits','totals_pageviews']
to_time = 'visitStartTime'
print(f'Number of unique values in keyword = {df_train["keyword"].nunique() }(Training)- {df_test["keyword"].nunique() }(Test)')
 print(f'Number of unique values in location = {df_train["location"].nunique() }(Training)- {df_test["location"].nunique() }(Test)' )<feature_engineering>
target_period = pd.date_range(start='2016-08-01',end='2018-12-01', freq='2MS')
 train_period = target_period.to_series().shift(periods=-210, freq='d',axis= 0)
 time_to = train_period[train_period.index>np.datetime64('2016-08-01')].astype('int')//10**9
time_end = target_period.to_series().shift(periods=-45, freq='d',axis= 0)[4:]
df_train['word_count'] = df_train['text'].apply(lambda x: len(str(x ).split()))
df_test['word_count'] = df_test['text'].apply(lambda x: len(str(x ).split()))
df_train['unique_word_count'] = df_train['text'].apply(lambda x: len(set(str(x ).split())))
 df_test['unique_word_count'] = df_test['text'].apply(lambda x: len(set(str(x ).split())))
 df_train['stop_word_count'] = df_train['text'].apply(lambda x: len([w for w in str(x ).lower().split() if w in STOPWORDS]))
df_test['stop_word_count'] = df_test['text'].apply(lambda x: len([w for w in str(x ).lower().split() if w in STOPWORDS]))
df_train['url_count'] = df_train['text'].apply(lambda x: len([w for w in str(x ).lower().split() if 'http' in w or 'https' in w]))
df_test['url_count'] = df_test['text'].apply(lambda x: len([w for w in str(x ).lower().split() if 'http' in w or 'https' in w]))
df_train['mean_word_length'] = df_train['text'].apply(lambda x: np.mean([len(w)for w in str(x ).split() ]))
df_test['mean_word_length'] = df_test['text'].apply(lambda x: np.mean([len(w)for w in str(x ).split() ]))
df_train['char_count'] = df_train['text'].apply(lambda x: len(str(x)))
 df_test['char_count'] = df_test['text'].apply(lambda x: len(str(x)))
 df_train['punctuation_count'] = df_train['text'].apply(lambda x: len([c for c in str(x)if c in string.punctuation]))
df_test['punctuation_count'] = df_test['text'].apply(lambda x: len([c for c in str(x)if c in string.punctuation]))
df_train['hashtag_count'] = df_train['text'].apply(lambda x: len([c for c in str(x)if c == '
df_test['hashtag_count'] = df_test['text'].apply(lambda x: len([c for c in str(x)if c == '
df_train['mention_count'] = df_train['text'].apply(lambda x: len([c for c in str(x)if c == '@']))
df_test['mention_count'] = df_test['text'].apply(lambda x: len([c for c in str(x)if c == '@']))<categorify>
user_x = df.iloc[df_train.shape[0]:,:]
test_x = pd.concat([user_x.groupby('fullVisitorId')[to_median].median().add_suffix('_median'),
user_x.groupby('fullVisitorId')['visitStartTime'].agg(['first','last'] ).add_suffix('_time' ).sub(time_to.values[-1] ).abs() ,
user_x.groupby('fullVisitorId')['visitStartTime'].apply(lambda x: x.max() -x.min() ).rename('time_diff'),
user_x.groupby('fullVisitorId')[to_sum].sum().add_suffix('_sum'),
user_x.groupby('fullVisitorId')[to_mean].mean().add_suffix('_mean'),
user_x.groupby('fullVisitorId')[to_std].std(ddof=0 ).add_suffix('_std')],axis=1 ).reset_index()
test_ID= test_x.fullVisitorId
test_x = test_x.drop(['fullVisitorId'], axis=1,errors='ignore')
 test_x = test_x.astype('int' )
def generate_ngrams(text, n_gram=1):
token = [token for token in text.lower().split(' ')if token != '' if token not in STOPWORDS]
ngrams = zip(*[token[i:] for i in range(n_gram)])
 return [' '.join(ngram)for ngram in ngrams]
N = 100
disaster_unigrams = defaultdict(int)
 nondisaster_unigrams = defaultdict(int)
 for tweet in df_train[DISASTER_TWEETS]['text']:
for word in generate_ngrams(tweet):
disaster_unigrams[word] += 1
for tweet in df_train[~DISASTER_TWEETS]['text']:
for word in generate_ngrams(tweet):
nondisaster_unigrams[word] += 1
df_disaster_unigrams = pd.DataFrame(sorted(disaster_unigrams.items() , key=lambda x: x[1])[::-1])
 df_nondisaster_unigrams = pd.DataFrame(sorted(nondisaster_unigrams.items() , key=lambda x: x[1])[::-1])
 disaster_bigrams = defaultdict(int)
 nondisaster_bigrams = defaultdict(int)
 for tweet in df_train[DISASTER_TWEETS]['text']:
for word in generate_ngrams(tweet, n_gram=2):
disaster_bigrams[word] += 1
for tweet in df_train[~DISASTER_TWEETS]['text']:
for word in generate_ngrams(tweet, n_gram=2):
nondisaster_bigrams[word] += 1
df_disaster_bigrams = pd.DataFrame(sorted(disaster_bigrams.items() , key=lambda x: x[1])[::-1])
 df_nondisaster_bigrams = pd.DataFrame(sorted(nondisaster_bigrams.items() , key=lambda x: x[1])[::-1])
 disaster_trigrams = defaultdict(int)
 nondisaster_trigrams = defaultdict(int)
 for tweet in df_train[DISASTER_TWEETS]['text']:
for word in generate_ngrams(tweet, n_gram=3):
disaster_trigrams[word] += 1
for tweet in df_train[~DISASTER_TWEETS]['text']:
for word in generate_ngrams(tweet, n_gram=3):
nondisaster_trigrams[word] += 1
df_disaster_trigrams = pd.DataFrame(sorted(disaster_trigrams.items() , key=lambda x: x[1])[::-1])
 df_nondisaster_trigrams = pd.DataFrame(sorted(nondisaster_trigrams.items() , key=lambda x: x[1])[::-1] )<load_pretrained>
i=4
user_x = df[(df.visitStartTime>=(time_to.index.astype('int')//10**9)[i])&(df.visitStartTime<(time_end.index.astype('int')//10**9)[i])]
user_y = df[(df.visitStartTime>=time_to.values[i])&(df.visitStartTime<time_to.values[i+1])]
train_x = pd.concat([user_x.groupby('fullVisitorId')[to_median].median().add_suffix('_median'),
user_x.groupby('fullVisitorId')['visitStartTime'].agg(['first','last'] ).add_suffix('_time' ).sub(time_to.values[i] ).abs() ,
user_x.groupby('fullVisitorId')['visitStartTime'].apply(lambda x: x.max() -x.min() ).rename('time_diff'),
user_x.groupby('fullVisitorId')[to_sum].sum().add_suffix('_sum'),
user_x.groupby('fullVisitorId')[to_mean].mean().add_suffix('_mean'),
user_x.groupby('fullVisitorId')[to_std].std(ddof=0 ).add_suffix('_std')],axis=1 ).reset_index()
merged=train_x.merge(user_y.groupby('fullVisitorId')['totals_transactionRevenue'].sum().reset_index() ,\
how='left', on='fullVisitorId')
 val_y = merged.totals_transactionRevenue
val_y.fillna(0, inplace=True)
 val_x = merged.drop(['fullVisitorId','totals_transactionRevenue'], axis=1,errors='ignore')
 val_x = val_x.astype('int' )
%%time
glove_embeddings = np.load('.. /input/pickled-glove840b300d-for-10sec-loading/glove.840B.300d.pkl', allow_pickle=True)
 fasttext_embeddings = np.load('.. /input/pickled-crawl300d2m-for-kernel-competitions/crawl-300d-2M.pkl', allow_pickle=True )<feature_engineering>
import lightgbm as lgb
from xgboost import XGBRegressor
from catboost import CatBoostRegressor
from sklearn.metrics import mean_squared_error
def build_vocab(X):
tweets = X.apply(lambda s: s.split() ).values
vocab = {}
for tweet in tweets:
for word in tweet:
try:
vocab[word] += 1
except KeyError:
vocab[word] = 1
return vocab
def check_embeddings_coverage(X, embeddings):
vocab = build_vocab(X)
 covered = {}
oov = {}
n_covered = 0
n_oov = 0
for word in vocab:
try:
covered[word] = embeddings[word]
n_covered += vocab[word]
except:
oov[word] = vocab[word]
n_oov += vocab[word]
vocab_coverage = len(covered)/ len(vocab)
 text_coverage =(n_covered /(n_covered + n_oov))
sorted_oov = sorted(oov.items() , key=operator.itemgetter(1)) [::-1]
return sorted_oov, vocab_coverage, text_coverage
train_glove_oov, train_glove_vocab_coverage, train_glove_text_coverage = check_embeddings_coverage(df_train['text'], glove_embeddings)
 test_glove_oov, test_glove_vocab_coverage, test_glove_text_coverage = check_embeddings_coverage(df_test['text'], glove_embeddings)
 print('GloVe Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Training Set'.format(train_glove_vocab_coverage, train_glove_text_coverage))
print('GloVe Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Test Set'.format(test_glove_vocab_coverage, test_glove_text_coverage))
train_fasttext_oov, train_fasttext_vocab_coverage, train_fasttext_text_coverage = check_embeddings_coverage(df_train['text'], fasttext_embeddings)
 test_fasttext_oov, test_fasttext_vocab_coverage, test_fasttext_text_coverage = check_embeddings_coverage(df_test['text'], fasttext_embeddings)
 print('FastText Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Training Set'.format(train_fasttext_vocab_coverage, train_fasttext_text_coverage))
print('FastText Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Test Set'.format(test_fasttext_vocab_coverage, test_fasttext_text_coverage))<categorify>
params={'learning_rate': 0.01,
'objective':'regression',
'metric':'rmse',
'num_leaves': 31,
'verbose': 1,
'bagging_fraction': 0.9,
'feature_fraction': 0.9,
"random_state":42,
'max_depth': 5,
"bagging_seed" : 42,
"verbosity" : -1,
"bagging_frequency" : 5,
'lambda_l2': 0.5,
'lambda_l1': 0.5,
'min_child_samples': 36
}
xgb_params = {
'objective': 'reg:linear',
'booster': 'gbtree',
'learning_rate': 0.02,
'max_depth': 22,
'min_child_weight': 57,
'gamma' : 1.45,
'alpha': 0.0,
'lambda': 0.0,
'subsample': 0.67,
'colsample_bytree': 0.054,
'colsample_bylevel': 0.50,
'n_jobs': -1,
'random_state': 456,
'importance_type': 'total_gain'
}
cat_param = {
'learning_rate' :0.03,
'depth' :10,
'eval_metric' :'RMSE',
'od_type' :'Iter',
'metric_period ' : 50,
'od_wait' : 20,
'seed' : 42
}
%%time
def clean(tweet):
tweet = re.sub(r"\x89Û_", "", tweet)
 tweet = re.sub(r"\x89ÛÒ", "", tweet)
 tweet = re.sub(r"\x89ÛÓ", "", tweet)
 tweet = re.sub(r"\x89ÛÏWhen", "When", tweet)
 tweet = re.sub(r"\x89ÛÏ", "", tweet)
 tweet = re.sub(r"China\x89Ûªs", "China's", tweet)
 tweet = re.sub(r"let\x89Ûªs", "let's", tweet)
 tweet = re.sub(r"\x89Û÷", "", tweet)
 tweet = re.sub(r"\x89Ûª", "", tweet)
 tweet = re.sub(r"\x89Û\x9d", "", tweet)
 tweet = re.sub(r"å_", "", tweet)
 tweet = re.sub(r"\x89Û¢", "", tweet)
 tweet = re.sub(r"\x89Û¢åÊ", "", tweet)
 tweet = re.sub(r"fromåÊwounds", "from wounds", tweet)
 tweet = re.sub(r"åÊ", "", tweet)
 tweet = re.sub(r"åÈ", "", tweet)
 tweet = re.sub(r"JapÌ_n", "Japan", tweet)
 tweet = re.sub(r"Ì©", "e", tweet)
 tweet = re.sub(r"å¨", "", tweet)
 tweet = re.sub(r"SuruÌ¤", "Suruc", tweet)
 tweet = re.sub(r"åÇ", "", tweet)
 tweet = re.sub(r"å£3million", "3 million", tweet)
 tweet = re.sub(r"åÀ", "", tweet)
 tweet = re.sub(r"he's", "he is", tweet)
 tweet = re.sub(r"there's", "there is", tweet)
 tweet = re.sub(r"We're", "We are", tweet)
 tweet = re.sub(r"That's", "That is", tweet)
 tweet = re.sub(r"won't", "will not", tweet)
 tweet = re.sub(r"they're", "they are", tweet)
 tweet = re.sub(r"Can't", "Cannot", tweet)
 tweet = re.sub(r"wasn't", "was not", tweet)
 tweet = re.sub(r"don\x89Ûªt", "do not", tweet)
 tweet = re.sub(r"aren't", "are not", tweet)
 tweet = re.sub(r"isn't", "is not", tweet)
 tweet = re.sub(r"What's", "What is", tweet)
 tweet = re.sub(r"haven't", "have not", tweet)
 tweet = re.sub(r"hasn't", "has not", tweet)
 tweet = re.sub(r"There's", "There is", tweet)
 tweet = re.sub(r"He's", "He is", tweet)
 tweet = re.sub(r"It's", "It is", tweet)
 tweet = re.sub(r"You're", "You are", tweet)
 tweet = re.sub(r"I'M", "I am", tweet)
 tweet = re.sub(r"shouldn't", "should not", tweet)
 tweet = re.sub(r"wouldn't", "would not", tweet)
 tweet = re.sub(r"i'm", "I am", tweet)
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 tweet = re.sub(r"I'm", "I am", tweet)
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 tweet = re.sub(r"youve", "you have", tweet)
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 tweet = re.sub(r"w/", "with", tweet)
 tweet = re.sub(r"USAgov", "USA government", tweet)
 tweet = re.sub(r"recentlu", "recently", tweet)
 tweet = re.sub(r"Ph0tos", "Photos", tweet)
 tweet = re.sub(r"amirite", "am I right", tweet)
 tweet = re.sub(r"exp0sed", "exposed", tweet)
 tweet = re.sub(r"<3", "love", tweet)
 tweet = re.sub(r"amageddon", "armageddon", tweet)
 tweet = re.sub(r"Trfc", "Traffic", tweet)
 tweet = re.sub(r"8/5/2015", "2015-08-05", tweet)
 tweet = re.sub(r"WindStorm", "Wind Storm", tweet)
 tweet = re.sub(r"8/6/2015", "2015-08-06", tweet)
 tweet = re.sub(r"10:38PM", "10:38 PM", tweet)
 tweet = re.sub(r"10:30pm", "10:30 PM", tweet)
 tweet = re.sub(r"16yr", "16 year", tweet)
 tweet = re.sub(r"lmao", "laughing my ass off", tweet)
 tweet = re.sub(r"TRAUMATISED", "traumatized", tweet)
 tweet = re.sub(r"IranDeal", "Iran Deal", tweet)
 tweet = re.sub(r"ArianaGrande", "Ariana Grande", tweet)
 tweet = re.sub(r"camilacabello97", "camila cabello", tweet)
 tweet = re.sub(r"RondaRousey", "Ronda Rousey", tweet)
 tweet = re.sub(r"MTVHottest", "MTV Hottest", tweet)
 tweet = re.sub(r"TrapMusic", "Trap Music", tweet)
 tweet = re.sub(r"ProphetMuhammad", "Prophet Muhammad", tweet)
 tweet = re.sub(r"PantherAttack", "Panther Attack", tweet)
 tweet = re.sub(r"StrategicPatience", "Strategic Patience", tweet)
 tweet = re.sub(r"socialnews", "social news", tweet)
 tweet = re.sub(r"NASAHurricane", "NASA Hurricane", tweet)
 tweet = re.sub(r"onlinecommunities", "online communities", tweet)
 tweet = re.sub(r"humanconsumption", "human consumption", tweet)
 tweet = re.sub(r"Typhoon-Devastated", "Typhoon Devastated", tweet)
 tweet = re.sub(r"Meat-Loving", "Meat Loving", tweet)
 tweet = re.sub(r"facialabuse", "facial abuse", tweet)
 tweet = re.sub(r"LakeCounty", "Lake County", tweet)
 tweet = re.sub(r"BeingAuthor", "Being Author", tweet)
 tweet = re.sub(r"withheavenly", "with heavenly", tweet)
 tweet = re.sub(r"thankU", "thank you", tweet)
 tweet = re.sub(r"iTunesMusic", "iTunes Music", tweet)
 tweet = re.sub(r"OffensiveContent", "Offensive Content", tweet)
 tweet = re.sub(r"WorstSummerJob", "Worst Summer Job", tweet)
 tweet = re.sub(r"HarryBeCareful", "Harry Be Careful", tweet)
 tweet = re.sub(r"NASASolarSystem", "NASA Solar System", tweet)
 tweet = re.sub(r"animalrescue", "animal rescue", tweet)
 tweet = re.sub(r"KurtSchlichter", "Kurt Schlichter", tweet)
 tweet = re.sub(r"aRmageddon", "armageddon", tweet)
 tweet = re.sub(r"Throwingknifes", "Throwing knives", tweet)
 tweet = re.sub(r"GodsLove", "God's Love", tweet)
 tweet = re.sub(r"bookboost", "book boost", tweet)
 tweet = re.sub(r"ibooklove", "I book love", tweet)
 tweet = re.sub(r"NestleIndia", "Nestle India", tweet)
 tweet = re.sub(r"realDonaldTrump", "Donald Trump", tweet)
 tweet = re.sub(r"DavidVonderhaar", "David Vonderhaar", tweet)
 tweet = re.sub(r"CecilTheLion", "Cecil The Lion", tweet)
 tweet = re.sub(r"weathernetwork", "weather network", tweet)
 tweet = re.sub(r"withBioterrorism&use", "with Bioterrorism & use", tweet)
 tweet = re.sub(r"Hostage&2", "Hostage & 2", tweet)
 tweet = re.sub(r"GOPDebate", "GOP Debate", tweet)
 tweet = re.sub(r"RickPerry", "Rick Perry", tweet)
 tweet = re.sub(r"frontpage", "front page", tweet)
 tweet = re.sub(r"NewsInTweets", "News In Tweets", tweet)
 tweet = re.sub(r"ViralSpell", "Viral Spell", tweet)
 tweet = re.sub(r"til_now", "until now", tweet)
 tweet = re.sub(r"volcanoinRussia", "volcano in Russia", tweet)
 tweet = re.sub(r"ZippedNews", "Zipped News", tweet)
 tweet = re.sub(r"MicheleBachman", "Michele Bachman", tweet)
 tweet = re.sub(r"53inch", "53 inch", tweet)
 tweet = re.sub(r"KerrickTrial", "Kerrick Trial", tweet)
 tweet = re.sub(r"abstorm", "Alberta Storm", tweet)
 tweet = re.sub(r"Beyhive", "Beyonce hive", tweet)
 tweet = re.sub(r"IDFire", "Idaho Fire", tweet)
 tweet = re.sub(r"DETECTADO", "Detected", tweet)
 tweet = re.sub(r"RockyFire", "Rocky Fire", tweet)
 tweet = re.sub(r"Listen/Buy", "Listen / Buy", tweet)
 tweet = re.sub(r"NickCannon", "Nick Cannon", tweet)
 tweet = re.sub(r"FaroeIslands", "Faroe Islands", tweet)
 tweet = re.sub(r"yycstorm", "Calgary Storm", tweet)
 tweet = re.sub(r"IDPs:", "Internally Displaced People :", tweet)
 tweet = re.sub(r"ArtistsUnited", "Artists United", tweet)
 tweet = re.sub(r"ClaytonBryant", "Clayton Bryant", tweet)
 tweet = re.sub(r"jimmyfallon", "jimmy fallon", tweet)
 tweet = re.sub(r"justinbieber", "justin bieber", tweet)
 tweet = re.sub(r"UTC2015", "UTC 2015", tweet)
 tweet = re.sub(r"Time2015", "Time 2015", tweet)
 tweet = re.sub(r"djicemoon", "dj icemoon", tweet)
 tweet = re.sub(r"LivingSafely", "Living Safely", tweet)
 tweet = re.sub(r"FIFA16", "Fifa 2016", tweet)
 tweet = re.sub(r"thisiswhywecanthavenicethings", "this is why we cannot have nice things", tweet)
 tweet = re.sub(r"bbcnews", "bbc news", tweet)
 tweet = re.sub(r"UndergroundRailraod", "Underground Railraod", tweet)
 tweet = re.sub(r"c4news", "c4 news", tweet)
 tweet = re.sub(r"OBLITERATION", "obliteration", tweet)
 tweet = re.sub(r"MUDSLIDE", "mudslide", tweet)
 tweet = re.sub(r"NoSurrender", "No Surrender", tweet)
 tweet = re.sub(r"NotExplained", "Not Explained", tweet)
 tweet = re.sub(r"greatbritishbakeoff", "great british bake off", tweet)
 tweet = re.sub(r"LondonFire", "London Fire", tweet)
 tweet = re.sub(r"KOTAWeather", "KOTA Weather", tweet)
 tweet = re.sub(r"LuchaUnderground", "Lucha Underground", tweet)
 tweet = re.sub(r"KOIN6News", "KOIN 6 News", tweet)
 tweet = re.sub(r"LiveOnK2", "Live On K2", tweet)
 tweet = re.sub(r"9NewsGoldCoast", "9 News Gold Coast", tweet)
 tweet = re.sub(r"nikeplus", "nike plus", tweet)
 tweet = re.sub(r"david_cameron", "David Cameron", tweet)
 tweet = re.sub(r"peterjukes", "Peter Jukes", tweet)
 tweet = re.sub(r"JamesMelville", "James Melville", tweet)
 tweet = re.sub(r"megynkelly", "Megyn Kelly", tweet)
 tweet = re.sub(r"cnewslive", "C News Live", tweet)
 tweet = re.sub(r"JamaicaObserver", "Jamaica Observer", tweet)
 tweet = re.sub(r"TweetLikeItsSeptember11th2001", "Tweet like it is september 11th 2001", tweet)
 tweet = re.sub(r"cbplawyers", "cbp lawyers", tweet)
 tweet = re.sub(r"fewmoretweets", "few more tweets", tweet)
 tweet = re.sub(r"BlackLivesMatter", "Black Lives Matter", tweet)
 tweet = re.sub(r"cjoyner", "Chris Joyner", tweet)
 tweet = re.sub(r"ENGvAUS", "England vs Australia", tweet)
 tweet = re.sub(r"ScottWalker", "Scott Walker", tweet)
 tweet = re.sub(r"MikeParrActor", "Michael Parr", tweet)
 tweet = re.sub(r"4PlayThursdays", "Foreplay Thursdays", tweet)
 tweet = re.sub(r"TGF2015", "Tontitown Grape Festival", tweet)
 tweet = re.sub(r"realmandyrain", "Mandy Rain", tweet)
 tweet = re.sub(r"GraysonDolan", "Grayson Dolan", tweet)
 tweet = re.sub(r"ApolloBrown", "Apollo Brown", tweet)
 tweet = re.sub(r"saddlebrooke", "Saddlebrooke", tweet)
 tweet = re.sub(r"TontitownGrape", "Tontitown Grape", tweet)
 tweet = re.sub(r"AbbsWinston", "Abbs Winston", tweet)
 tweet = re.sub(r"ShaunKing", "Shaun King", tweet)
 tweet = re.sub(r"MeekMill", "Meek Mill", tweet)
 tweet = re.sub(r"TornadoGiveaway", "Tornado Giveaway", tweet)
 tweet = re.sub(r"GRupdates", "GR updates", tweet)
 tweet = re.sub(r"SouthDowns", "South Downs", tweet)
 tweet = re.sub(r"braininjury", "brain injury", tweet)
 tweet = re.sub(r"auspol", "Australian politics", tweet)
 tweet = re.sub(r"PlannedParenthood", "Planned Parenthood", tweet)
 tweet = re.sub(r"calgaryweather", "Calgary Weather", tweet)
 tweet = re.sub(r"weallheartonedirection", "we all heart one direction", tweet)
 tweet = re.sub(r"edsheeran", "Ed Sheeran", tweet)
 tweet = re.sub(r"TrueHeroes", "True Heroes", tweet)
 tweet = re.sub(r"S3XLEAK", "sex leak", tweet)
 tweet = re.sub(r"ComplexMag", "Complex Magazine", tweet)
 tweet = re.sub(r"TheAdvocateMag", "The Advocate Magazine", tweet)
 tweet = re.sub(r"CityofCalgary", "City of Calgary", tweet)
 tweet = re.sub(r"EbolaOutbreak", "Ebola Outbreak", tweet)
 tweet = re.sub(r"SummerFate", "Summer Fate", tweet)
 tweet = re.sub(r"RAmag", "Royal Academy Magazine", tweet)
 tweet = re.sub(r"offers2go", "offers to go", tweet)
 tweet = re.sub(r"foodscare", "food scare", tweet)
 tweet = re.sub(r"MNPDNashville", "Metropolitan Nashville Police Department", tweet)
 tweet = re.sub(r"TfLBusAlerts", "TfL Bus Alerts", tweet)
 tweet = re.sub(r"GamerGate", "Gamer Gate", tweet)
 tweet = re.sub(r"IHHen", "Humanitarian Relief", tweet)
 tweet = re.sub(r"spinningbot", "spinning bot", tweet)
 tweet = re.sub(r"ModiMinistry", "Modi Ministry", tweet)
 tweet = re.sub(r"TAXIWAYS", "taxi ways", tweet)
 tweet = re.sub(r"Calum5SOS", "Calum Hood", tweet)
 tweet = re.sub(r"po_st", "po.st", tweet)
 tweet = re.sub(r"scoopit", "scoop.it", tweet)
 tweet = re.sub(r"UltimaLucha", "Ultima Lucha", tweet)
 tweet = re.sub(r"JonathanFerrell", "Jonathan Ferrell", tweet)
 tweet = re.sub(r"aria_ahrary", "Aria Ahrary", tweet)
 tweet = re.sub(r"rapidcity", "Rapid City", tweet)
 tweet = re.sub(r"OutBid", "outbid", tweet)
 tweet = re.sub(r"lavenderpoetrycafe", "lavender poetry cafe", tweet)
 tweet = re.sub(r"EudryLantiqua", "Eudry Lantiqua", tweet)
 tweet = re.sub(r"15PM", "15 PM", tweet)
 tweet = re.sub(r"OriginalFunko", "Funko", tweet)
 tweet = re.sub(r"rightwaystan", "Richard Tan", tweet)
 tweet = re.sub(r"CindyNoonan", "Cindy Noonan", tweet)
 tweet = re.sub(r"RT_America", "RT America", tweet)
 tweet = re.sub(r"narendramodi", "Narendra Modi", tweet)
 tweet = re.sub(r"BakeOffFriends", "Bake Off Friends", tweet)
 tweet = re.sub(r"TeamHendrick", "Hendrick Motorsports", tweet)
 tweet = re.sub(r"alexbelloli", "Alex Belloli", tweet)
 tweet = re.sub(r"itsjustinstuart", "Justin Stuart", tweet)
 tweet = re.sub(r"gunsense", "gun sense", tweet)
 tweet = re.sub(r"DebateQuestionsWeWantToHear", "debate questions we want to hear", tweet)
 tweet = re.sub(r"RoyalCarribean", "Royal Carribean", tweet)
 tweet = re.sub(r"samanthaturne19", "Samantha Turner", tweet)
 tweet = re.sub(r"JonVoyage", "Jon Stewart", tweet)
 tweet = re.sub(r"renew911health", "renew 911 health", tweet)
 tweet = re.sub(r"SuryaRay", "Surya Ray", tweet)
 tweet = re.sub(r"pattonoswalt", "Patton Oswalt", tweet)
 tweet = re.sub(r"minhazmerchant", "Minhaz Merchant", tweet)
 tweet = re.sub(r"TLVFaces", "Israel Diaspora Coalition", tweet)
 tweet = re.sub(r"pmarca", "Marc Andreessen", tweet)
 tweet = re.sub(r"pdx911", "Portland Police", tweet)
 tweet = re.sub(r"jamaicaplain", "Jamaica Plain", tweet)
 tweet = re.sub(r"Japton", "Arkansas", tweet)
 tweet = re.sub(r"RouteComplex", "Route Complex", tweet)
 tweet = re.sub(r"INSubcontinent", "Indian Subcontinent", tweet)
 tweet = re.sub(r"NJTurnpike", "New Jersey Turnpike", tweet)
 tweet = re.sub(r"Politifiact", "PolitiFact", tweet)
 tweet = re.sub(r"Hiroshima70", "Hiroshima", tweet)
 tweet = re.sub(r"GMMBC", "Greater Mt Moriah Baptist Church", tweet)
 tweet = re.sub(r"versethe", "verse the", tweet)
 tweet = re.sub(r"TubeStrike", "Tube Strike", tweet)
 tweet = re.sub(r"MissionHills", "Mission Hills", tweet)
 tweet = re.sub(r"ProtectDenaliWolves", "Protect Denali Wolves", tweet)
 tweet = re.sub(r"NANKANA", "Nankana", tweet)
 tweet = re.sub(r"SAHIB", "Sahib", tweet)
 tweet = re.sub(r"PAKPATTAN", "Pakpattan", tweet)
 tweet = re.sub(r"Newz_Sacramento", "News Sacramento", tweet)
 tweet = re.sub(r"gofundme", "go fund me", tweet)
 tweet = re.sub(r"pmharper", "Stephen Harper", tweet)
 tweet = re.sub(r"IvanBerroa", "Ivan Berroa", tweet)
 tweet = re.sub(r"LosDelSonido", "Los Del Sonido", tweet)
 tweet = re.sub(r"bancodeseries", "banco de series", tweet)
 tweet = re.sub(r"timkaine", "Tim Kaine", tweet)
 tweet = re.sub(r"IdentityTheft", "Identity Theft", tweet)
 tweet = re.sub(r"AllLivesMatter", "All Lives Matter", tweet)
 tweet = re.sub(r"mishacollins", "Misha Collins", tweet)
 tweet = re.sub(r"BillNeelyNBC", "Bill Neely", tweet)
 tweet = re.sub(r"BeClearOnCancer", "be clear on cancer", tweet)
 tweet = re.sub(r"Kowing", "Knowing", tweet)
 tweet = re.sub(r"ScreamQueens", "Scream Queens", tweet)
 tweet = re.sub(r"AskCharley", "Ask Charley", tweet)
 tweet = re.sub(r"BlizzHeroes", "Heroes of the Storm", tweet)
 tweet = re.sub(r"BradleyBrad47", "Bradley Brad", tweet)
 tweet = re.sub(r"HannaPH", "Typhoon Hanna", tweet)
 tweet = re.sub(r"meinlcymbals", "MEINL Cymbals", tweet)
 tweet = re.sub(r"Ptbo", "Peterborough", tweet)
 tweet = re.sub(r"cnnbrk", "CNN Breaking News", tweet)
 tweet = re.sub(r"IndianNews", "Indian News", tweet)
 tweet = re.sub(r"savebees", "save bees", tweet)
 tweet = re.sub(r"GreenHarvard", "Green Harvard", tweet)
 tweet = re.sub(r"StandwithPP", "Stand with planned parenthood", tweet)
 tweet = re.sub(r"hermancranston", "Herman Cranston", tweet)
 tweet = re.sub(r"WMUR9", "WMUR-TV", tweet)
 tweet = re.sub(r"RockBottomRadFM", "Rock Bottom Radio", tweet)
 tweet = re.sub(r"ameenshaikh3", "Ameen Shaikh", tweet)
 tweet = re.sub(r"ProSyn", "Project Syndicate", tweet)
 tweet = re.sub(r"Daesh", "ISIS", tweet)
 tweet = re.sub(r"s2g", "swear to god", tweet)
 tweet = re.sub(r"listenlive", "listen live", tweet)
 tweet = re.sub(r"CDCgov", "Centers for Disease Control and Prevention", tweet)
 tweet = re.sub(r"FoxNew", "Fox News", tweet)
 tweet = re.sub(r"CBSBigBrother", "Big Brother", tweet)
 tweet = re.sub(r"JulieDiCaro", "Julie DiCaro", tweet)
 tweet = re.sub(r"theadvocatemag", "The Advocate Magazine", tweet)
 tweet = re.sub(r"RohnertParkDPS", "Rohnert Park Police Department", tweet)
 tweet = re.sub(r"THISIZBWRIGHT", "Bonnie Wright", tweet)
 tweet = re.sub(r"Popularmmos", "Popular MMOs", tweet)
 tweet = re.sub(r"WildHorses", "Wild Horses", tweet)
 tweet = re.sub(r"FantasticFour", "Fantastic Four", tweet)
 tweet = re.sub(r"HORNDALE", "Horndale", tweet)
 tweet = re.sub(r"PINER", "Piner", tweet)
 tweet = re.sub(r"BathAndNorthEastSomerset", "Bath and North East Somerset", tweet)
 tweet = re.sub(r"thatswhatfriendsarefor", "that is what friends are for", tweet)
 tweet = re.sub(r"residualincome", "residual income", tweet)
 tweet = re.sub(r"YahooNewsDigest", "Yahoo News Digest", tweet)
 tweet = re.sub(r"MalaysiaAirlines", "Malaysia Airlines", tweet)
 tweet = re.sub(r"AmazonDeals", "Amazon Deals", tweet)
 tweet = re.sub(r"MissCharleyWebb", "Charley Webb", tweet)
 tweet = re.sub(r"shoalstraffic", "shoals traffic", tweet)
 tweet = re.sub(r"GeorgeFoster72", "George Foster", tweet)
 tweet = re.sub(r"pop2015", "pop 2015", tweet)
 tweet = re.sub(r"_PokemonCards_", "Pokemon Cards", tweet)
 tweet = re.sub(r"DianneG", "Dianne Gallagher", tweet)
 tweet = re.sub(r"KashmirConflict", "Kashmir Conflict", tweet)
 tweet = re.sub(r"BritishBakeOff", "British Bake Off", tweet)
 tweet = re.sub(r"FreeKashmir", "Free Kashmir", tweet)
 tweet = re.sub(r"mattmosley", "Matt Mosley", tweet)
 tweet = re.sub(r"BishopFred", "Bishop Fred", tweet)
 tweet = re.sub(r"EndConflict", "End Conflict", tweet)
 tweet = re.sub(r"EndOccupation", "End Occupation", tweet)
 tweet = re.sub(r"UNHEALED", "unhealed", tweet)
 tweet = re.sub(r"CharlesDagnall", "Charles Dagnall", tweet)
 tweet = re.sub(r"Latestnews", "Latest news", tweet)
 tweet = re.sub(r"KindleCountdown", "Kindle Countdown", tweet)
 tweet = re.sub(r"NoMoreHandouts", "No More Handouts", tweet)
 tweet = re.sub(r"datingtips", "dating tips", tweet)
 tweet = re.sub(r"charlesadler", "Charles Adler", tweet)
 tweet = re.sub(r"twia", "Texas Windstorm Insurance Association", tweet)
 tweet = re.sub(r"txlege", "Texas Legislature", tweet)
 tweet = re.sub(r"WindstormInsurer", "Windstorm Insurer", tweet)
 tweet = re.sub(r"Newss", "News", tweet)
 tweet = re.sub(r"hempoil", "hemp oil", tweet)
 tweet = re.sub(r"CommoditiesAre", "Commodities are", tweet)
 tweet = re.sub(r"tubestrike", "tube strike", tweet)
 tweet = re.sub(r"JoeNBC", "Joe Scarborough", tweet)
 tweet = re.sub(r"LiteraryCakes", "Literary Cakes", tweet)
 tweet = re.sub(r"TI5", "The International 5", tweet)
 tweet = re.sub(r"thehill", "the hill", tweet)
 tweet = re.sub(r"3others", "3 others", tweet)
 tweet = re.sub(r"stighefootball", "Sam Tighe", tweet)
 tweet = re.sub(r"whatstheimportantvideo", "what is the important video", tweet)
 tweet = re.sub(r"ClaudioMeloni", "Claudio Meloni", tweet)
 tweet = re.sub(r"DukeSkywalker", "Duke Skywalker", tweet)
 tweet = re.sub(r"carsonmwr", "Fort Carson", tweet)
 tweet = re.sub(r"offdishduty", "off dish duty", tweet)
 tweet = re.sub(r"andword", "and word", tweet)
 tweet = re.sub(r"rhodeisland", "Rhode Island", tweet)
 tweet = re.sub(r"easternoregon", "Eastern Oregon", tweet)
 tweet = re.sub(r"WAwildfire", "Washington Wildfire", tweet)
 tweet = re.sub(r"fingerrockfire", "Finger Rock Fire", tweet)
 tweet = re.sub(r"57am", "57 am", tweet)
 tweet = re.sub(r"fingerrockfire", "Finger Rock Fire", tweet)
 tweet = re.sub(r"JacobHoggard", "Jacob Hoggard", tweet)
 tweet = re.sub(r"newnewnew", "new new new", tweet)
 tweet = re.sub(r"under50", "under 50", tweet)
 tweet = re.sub(r"getitbeforeitsgone", "get it before it is gone", tweet)
 tweet = re.sub(r"freshoutofthebox", "fresh out of the box", tweet)
 tweet = re.sub(r"amwriting", "am writing", tweet)
 tweet = re.sub(r"Bokoharm", "Boko Haram", tweet)
 tweet = re.sub(r"Nowlike", "Now like", tweet)
 tweet = re.sub(r"seasonfrom", "season from", tweet)
 tweet = re.sub(r"epicente", "epicenter", tweet)
 tweet = re.sub(r"epicenterr", "epicenter", tweet)
 tweet = re.sub(r"sicklife", "sick life", tweet)
 tweet = re.sub(r"yycweather", "Calgary Weather", tweet)
 tweet = re.sub(r"calgarysun", "Calgary Sun", tweet)
 tweet = re.sub(r"approachng", "approaching", tweet)
 tweet = re.sub(r"evng", "evening", tweet)
 tweet = re.sub(r"Sumthng", "something", tweet)
 tweet = re.sub(r"EllenPompeo", "Ellen Pompeo", tweet)
 tweet = re.sub(r"shondarhimes", "Shonda Rhimes", tweet)
 tweet = re.sub(r"ABCNetwork", "ABC Network", tweet)
 tweet = re.sub(r"SushmaSwaraj", "Sushma Swaraj", tweet)
 tweet = re.sub(r"pray4japan", "Pray for Japan", tweet)
 tweet = re.sub(r"hope4japan", "Hope for Japan", tweet)
 tweet = re.sub(r"Illusionimagess", "Illusion images", tweet)
 tweet = re.sub(r"SummerUnderTheStars", "Summer Under The Stars", tweet)
 tweet = re.sub(r"ShallWeDance", "Shall We Dance", tweet)
 tweet = re.sub(r"TCMParty", "TCM Party", tweet)
 tweet = re.sub(r"marijuananews", "marijuana news", tweet)
 tweet = re.sub(r"onbeingwithKristaTippett", "on being with Krista Tippett", tweet)
 tweet = re.sub(r"Beingtweets", "Being tweets", tweet)
 tweet = re.sub(r"newauthors", "new authors", tweet)
 tweet = re.sub(r"remedyyyy", "remedy", tweet)
 tweet = re.sub(r"44PM", "44 PM", tweet)
 tweet = re.sub(r"HeadlinesApp", "Headlines App", tweet)
 tweet = re.sub(r"40PM", "40 PM", tweet)
 tweet = re.sub(r"myswc", "Severe Weather Center", tweet)
 tweet = re.sub(r"ithats", "that is", tweet)
 tweet = re.sub(r"icouldsitinthismomentforever", "I could sit in this moment forever", tweet)
 tweet = re.sub(r"FatLoss", "Fat Loss", tweet)
 tweet = re.sub(r"02PM", "02 PM", tweet)
 tweet = re.sub(r"MetroFmTalk", "Metro Fm Talk", tweet)
 tweet = re.sub(r"Bstrd", "bastard", tweet)
 tweet = re.sub(r"bldy", "bloody", tweet)
 tweet = re.sub(r"MetrofmTalk", "Metro Fm Talk", tweet)
 tweet = re.sub(r"terrorismturn", "terrorism turn", tweet)
 tweet = re.sub(r"BBCNewsAsia", "BBC News Asia", tweet)
 tweet = re.sub(r"BehindTheScenes", "Behind The Scenes", tweet)
 tweet = re.sub(r"GeorgeTakei", "George Takei", tweet)
 tweet = re.sub(r"WomensWeeklyMag", "Womens Weekly Magazine", tweet)
 tweet = re.sub(r"SurvivorsGuidetoEarth", "Survivors Guide to Earth", tweet)
 tweet = re.sub(r"incubusband", "incubus band", tweet)
 tweet = re.sub(r"Babypicturethis", "Baby picture this", tweet)
 tweet = re.sub(r"BombEffects", "Bomb Effects", tweet)
 tweet = re.sub(r"win10", "Windows 10", tweet)
 tweet = re.sub(r"idkidk", "I do not know I do not know", tweet)
 tweet = re.sub(r"TheWalkingDead", "The Walking Dead", tweet)
 tweet = re.sub(r"amyschumer", "Amy Schumer", tweet)
 tweet = re.sub(r"crewlist", "crew list", tweet)
 tweet = re.sub(r"Erdogans", "Erdogan", tweet)
 tweet = re.sub(r"BBCLive", "BBC Live", tweet)
 tweet = re.sub(r"TonyAbbottMHR", "Tony Abbott", tweet)
 tweet = re.sub(r"paulmyerscough", "Paul Myerscough", tweet)
 tweet = re.sub(r"georgegallagher", "George Gallagher", tweet)
 tweet = re.sub(r"JimmieJohnson", "Jimmie Johnson", tweet)
 tweet = re.sub(r"pctool", "pc tool", tweet)
 tweet = re.sub(r"DoingHashtagsRight", "Doing Hashtags Right", tweet)
 tweet = re.sub(r"ThrowbackThursday", "Throwback Thursday", tweet)
 tweet = re.sub(r"SnowBackSunday", "Snowback Sunday", tweet)
 tweet = re.sub(r"LakeEffect", "Lake Effect", tweet)
 tweet = re.sub(r"RTphotographyUK", "Richard Thomas Photography UK", tweet)
 tweet = re.sub(r"BigBang_CBS", "Big Bang CBS", tweet)
 tweet = re.sub(r"writerslife", "writers life", tweet)
 tweet = re.sub(r"NaturalBirth", "Natural Birth", tweet)
 tweet = re.sub(r"UnusualWords", "Unusual Words", tweet)
 tweet = re.sub(r"wizkhalifa", "Wiz Khalifa", tweet)
 tweet = re.sub(r"acreativedc", "a creative DC", tweet)
 tweet = re.sub(r"vscodc", "vsco DC", tweet)
 tweet = re.sub(r"VSCOcam", "vsco camera", tweet)
 tweet = re.sub(r"TheBEACHDC", "The beach DC", tweet)
 tweet = re.sub(r"buildingmuseum", "building museum", tweet)
 tweet = re.sub(r"WorldOil", "World Oil", tweet)
 tweet = re.sub(r"redwedding", "red wedding", tweet)
 tweet = re.sub(r"AmazingRaceCanada", "Amazing Race Canada", tweet)
 tweet = re.sub(r"WakeUpAmerica", "Wake Up America", tweet)
 tweet = re.sub(r"\\Allahuakbar\", "Allahu Akbar", tweet)
 tweet = re.sub(r"bleased", "blessed", tweet)
 tweet = re.sub(r"nigeriantribune", "Nigerian Tribune", tweet)
 tweet = re.sub(r"HIDEO_KOJIMA_EN", "Hideo Kojima", tweet)
 tweet = re.sub(r"FusionFestival", "Fusion Festival", tweet)
 tweet = re.sub(r"50Mixed", "50 Mixed", tweet)
 tweet = re.sub(r"NoAgenda", "No Agenda", tweet)
 tweet = re.sub(r"WhiteGenocide", "White Genocide", tweet)
 tweet = re.sub(r"dirtylying", "dirty lying", tweet)
 tweet = re.sub(r"SyrianRefugees", "Syrian Refugees", tweet)
 tweet = re.sub(r"changetheworld", "change the world", tweet)
 tweet = re.sub(r"Ebolacase", "Ebola case", tweet)
 tweet = re.sub(r"mcgtech", "mcg technologies", tweet)
 tweet = re.sub(r"withweapons", "with weapons", tweet)
 tweet = re.sub(r"advancedwarfare", "advanced warfare", tweet)
 tweet = re.sub(r"letsFootball", "let us Football", tweet)
 tweet = re.sub(r"LateNiteMix", "late night mix", tweet)
 tweet = re.sub(r"PhilCollinsFeed", "Phil Collins", tweet)
 tweet = re.sub(r"RudyHavenstein", "Rudy Havenstein", tweet)
 tweet = re.sub(r"22PM", "22 PM", tweet)
 tweet = re.sub(r"54am", "54 AM", tweet)
 tweet = re.sub(r"38am", "38 AM", tweet)
 tweet = re.sub(r"OldFolkExplainStuff", "Old Folk Explain Stuff", tweet)
 tweet = re.sub(r"BlacklivesMatter", "Black Lives Matter", tweet)
 tweet = re.sub(r"InsaneLimits", "Insane Limits", tweet)
 tweet = re.sub(r"youcantsitwithus", "you cannot sit with us", tweet)
 tweet = re.sub(r"2k15", "2015", tweet)
 tweet = re.sub(r"TheIran", "Iran", tweet)
 tweet = re.sub(r"JimmyFallon", "Jimmy Fallon", tweet)
 tweet = re.sub(r"AlbertBrooks", "Albert Brooks", tweet)
 tweet = re.sub(r"defense_news", "defense news", tweet)
 tweet = re.sub(r"nuclearrcSA", "Nuclear Risk Control Self Assessment", tweet)
 tweet = re.sub(r"Auspol", "Australia Politics", tweet)
 tweet = re.sub(r"NuclearPower", "Nuclear Power", tweet)
 tweet = re.sub(r"WhiteTerrorism", "White Terrorism", tweet)
 tweet = re.sub(r"truthfrequencyradio", "Truth Frequency Radio", tweet)
 tweet = re.sub(r"ErasureIsNotEquality", "Erasure is not equality", tweet)
 tweet = re.sub(r"ProBonoNews", "Pro Bono News", tweet)
 tweet = re.sub(r"JakartaPost", "Jakarta Post", tweet)
 tweet = re.sub(r"toopainful", "too painful", tweet)
 tweet = re.sub(r"melindahaunton", "Melinda Haunton", tweet)
 tweet = re.sub(r"NoNukes", "No Nukes", tweet)
 tweet = re.sub(r"curryspcworld", "Currys PC World", tweet)
 tweet = re.sub(r"ineedcake", "I need cake", tweet)
 tweet = re.sub(r"blackforestgateau", "black forest gateau", tweet)
 tweet = re.sub(r"BBCOne", "BBC One", tweet)
 tweet = re.sub(r"AlexxPage", "Alex Page", tweet)
 tweet = re.sub(r"jonathanserrie", "Jonathan Serrie", tweet)
 tweet = re.sub(r"SocialJerkBlog", "Social Jerk Blog", tweet)
 tweet = re.sub(r"ChelseaVPeretti", "Chelsea Peretti", tweet)
 tweet = re.sub(r"irongiant", "iron giant", tweet)
 tweet = re.sub(r"RonFunches", "Ron Funches", tweet)
 tweet = re.sub(r"TimCook", "Tim Cook", tweet)
 tweet = re.sub(r"sebastianstanisaliveandwell", "Sebastian Stan is alive and well", tweet)
 tweet = re.sub(r"Madsummer", "Mad summer", tweet)
 tweet = re.sub(r"NowYouKnow", "Now you know", tweet)
 tweet = re.sub(r"concertphotography", "concert photography", tweet)
 tweet = re.sub(r"TomLandry", "Tom Landry", tweet)
 tweet = re.sub(r"showgirldayoff", "show girl day off", tweet)
 tweet = re.sub(r"Yougslavia", "Yugoslavia", tweet)
 tweet = re.sub(r"QuantumDataInformatics", "Quantum Data Informatics", tweet)
 tweet = re.sub(r"FromTheDesk", "From The Desk", tweet)
 tweet = re.sub(r"TheaterTrial", "Theater Trial", tweet)
 tweet = re.sub(r"CatoInstitute", "Cato Institute", tweet)
 tweet = re.sub(r"EmekaGift", "Emeka Gift", tweet)
 tweet = re.sub(r"LetsBe_Rational", "Let us be rational", tweet)
 tweet = re.sub(r"Cynicalreality", "Cynical reality", tweet)
 tweet = re.sub(r"FredOlsenCruise", "Fred Olsen Cruise", tweet)
 tweet = re.sub(r"NotSorry", "not sorry", tweet)
 tweet = re.sub(r"UseYourWords", "use your words", tweet)
 tweet = re.sub(r"WordoftheDay", "word of the day", tweet)
 tweet = re.sub(r"Dictionarycom", "Dictionary.com", tweet)
 tweet = re.sub(r"TheBrooklynLife", "The Brooklyn Life", tweet)
 tweet = re.sub(r"jokethey", "joke they", tweet)
 tweet = re.sub(r"nflweek1picks", "NFL week 1 picks", tweet)
 tweet = re.sub(r"uiseful", "useful", tweet)
 tweet = re.sub(r"JusticeDotOrg", "The American Association for Justice", tweet)
 tweet = re.sub(r"autoaccidents", "auto accidents", tweet)
 tweet = re.sub(r"SteveGursten", "Steve Gursten", tweet)
 tweet = re.sub(r"MichiganAutoLaw", "Michigan Auto Law", tweet)
 tweet = re.sub(r"birdgang", "bird gang", tweet)
 tweet = re.sub(r"nflnetwork", "NFL Network", tweet)
 tweet = re.sub(r"NYDNSports", "NY Daily News Sports", tweet)
 tweet = re.sub(r"RVacchianoNYDN", "Ralph Vacchiano NY Daily News", tweet)
 tweet = re.sub(r"EdmontonEsks", "Edmonton Eskimos", tweet)
 tweet = re.sub(r"david_brelsford", "David Brelsford", tweet)
 tweet = re.sub(r"TOI_India", "The Times of India", tweet)
 tweet = re.sub(r"hegot", "he got", tweet)
 tweet = re.sub(r"SkinsOn9", "Skins on 9", tweet)
 tweet = re.sub(r"sothathappened", "so that happened", tweet)
 tweet = re.sub(r"LCOutOfDoors", "LC Out Of Doors", tweet)
 tweet = re.sub(r"NationFirst", "Nation First", tweet)
 tweet = re.sub(r"IndiaToday", "India Today", tweet)
 tweet = re.sub(r"HLPS", "helps", tweet)
 tweet = re.sub(r"HOSTAGESTHROSW", "hostages throw", tweet)
 tweet = re.sub(r"SNCTIONS", "sanctions", tweet)
 tweet = re.sub(r"BidTime", "Bid Time", tweet)
 tweet = re.sub(r"crunchysensible", "crunchy sensible", tweet)
 tweet = re.sub(r"RandomActsOfRomance", "Random acts of romance", tweet)
 tweet = re.sub(r"MomentsAtHill", "Moments at hill", tweet)
 tweet = re.sub(r"eatshit", "eat shit", tweet)
 tweet = re.sub(r"liveleakfun", "live leak fun", tweet)
 tweet = re.sub(r"SahelNews", "Sahel News", tweet)
 tweet = re.sub(r"abc7newsbayarea", "ABC 7 News Bay Area", tweet)
 tweet = re.sub(r"facilitiesmanagement", "facilities management", tweet)
 tweet = re.sub(r"facilitydude", "facility dude", tweet)
 tweet = re.sub(r"CampLogistics", "Camp logistics", tweet)
 tweet = re.sub(r"alaskapublic", "Alaska public", tweet)
 tweet = re.sub(r"MarketResearch", "Market Research", tweet)
 tweet = re.sub(r"AccuracyEsports", "Accuracy Esports", tweet)
 tweet = re.sub(r"TheBodyShopAust", "The Body Shop Australia", tweet)
 tweet = re.sub(r"yychail", "Calgary hail", tweet)
 tweet = re.sub(r"yyctraffic", "Calgary traffic", tweet)
 tweet = re.sub(r"eliotschool", "eliot school", tweet)
 tweet = re.sub(r"TheBrokenCity", "The Broken City", tweet)
 tweet = re.sub(r"OldsFireDept", "Olds Fire Department", tweet)
 tweet = re.sub(r"RiverComplex", "River Complex", tweet)
 tweet = re.sub(r"fieldworksmells", "field work smells", tweet)
 tweet = re.sub(r"IranElection", "Iran Election", tweet)
 tweet = re.sub(r"glowng", "glowing", tweet)
 tweet = re.sub(r"kindlng", "kindling", tweet)
 tweet = re.sub(r"riggd", "rigged", tweet)
 tweet = re.sub(r"slownewsday", "slow news day", tweet)
 tweet = re.sub(r"MyanmarFlood", "Myanmar Flood", tweet)
 tweet = re.sub(r"abc7chicago", "ABC 7 Chicago", tweet)
 tweet = re.sub(r"copolitics", "Colorado Politics", tweet)
 tweet = re.sub(r"AdilGhumro", "Adil Ghumro", tweet)
 tweet = re.sub(r"netbots", "net bots", tweet)
 tweet = re.sub(r"byebyeroad", "bye bye road", tweet)
 tweet = re.sub(r"massiveflooding", "massive flooding", tweet)
 tweet = re.sub(r"EndofUS", "End of United States", tweet)
 tweet = re.sub(r"35PM", "35 PM", tweet)
 tweet = re.sub(r"greektheatrela", "Greek Theatre Los Angeles", tweet)
 tweet = re.sub(r"76mins", "76 minutes", tweet)
 tweet = re.sub(r"publicsafetyfirst", "public safety first", tweet)
 tweet = re.sub(r"livesmatter", "lives matter", tweet)
 tweet = re.sub(r"myhometown", "my hometown", tweet)
 tweet = re.sub(r"tankerfire", "tanker fire", tweet)
 tweet = re.sub(r"MEMORIALDAY", "memorial day", tweet)
 tweet = re.sub(r"MEMORIAL_DAY", "memorial day", tweet)
 tweet = re.sub(r"instaxbooty", "instagram booty", tweet)
 tweet = re.sub(r"Jerusalem_Post", "Jerusalem Post", tweet)
 tweet = re.sub(r"WayneRooney_INA", "Wayne Rooney", tweet)
 tweet = re.sub(r"VirtualReality", "Virtual Reality", tweet)
 tweet = re.sub(r"OculusRift", "Oculus Rift", tweet)
 tweet = re.sub(r"OwenJones84", "Owen Jones", tweet)
 tweet = re.sub(r"jeremycorbyn", "Jeremy Corbyn", tweet)
 tweet = re.sub(r"paulrogers002", "Paul Rogers", tweet)
 tweet = re.sub(r"mortalkombatx", "Mortal Kombat X", tweet)
 tweet = re.sub(r"mortalkombat", "Mortal Kombat", tweet)
 tweet = re.sub(r"FilipeCoelho92", "Filipe Coelho", tweet)
 tweet = re.sub(r"OnlyQuakeNews", "Only Quake News", tweet)
 tweet = re.sub(r"kostumes", "costumes", tweet)
 tweet = re.sub(r"YEEESSSS", "yes", tweet)
 tweet = re.sub(r"ToshikazuKatayama", "Toshikazu Katayama", tweet)
 tweet = re.sub(r"IntlDevelopment", "Intl Development", tweet)
 tweet = re.sub(r"ExtremeWeather", "Extreme Weather", tweet)
 tweet = re.sub(r"WereNotGruberVoters", "We are not gruber voters", tweet)
 tweet = re.sub(r"NewsThousands", "News Thousands", tweet)
 tweet = re.sub(r"EdmundAdamus", "Edmund Adamus", tweet)
 tweet = re.sub(r"EyewitnessWV", "Eye witness WV", tweet)
 tweet = re.sub(r"PhiladelphiaMuseu", "Philadelphia Museum", tweet)
 tweet = re.sub(r"DublinComicCon", "Dublin Comic Con", tweet)
 tweet = re.sub(r"NicholasBrendon", "Nicholas Brendon", tweet)
 tweet = re.sub(r"Alltheway80s", "All the way 80s", tweet)
 tweet = re.sub(r"FromTheField", "From the field", tweet)
 tweet = re.sub(r"NorthIowa", "North Iowa", tweet)
 tweet = re.sub(r"WillowFire", "Willow Fire", tweet)
 tweet = re.sub(r"MadRiverComplex", "Mad River Complex", tweet)
 tweet = re.sub(r"feelingmanly", "feeling manly", tweet)
 tweet = re.sub(r"stillnotoverit", "still not over it", tweet)
 tweet = re.sub(r"FortitudeValley", "Fortitude Valley", tweet)
 tweet = re.sub(r"CoastpowerlineTramTr", "Coast powerline", tweet)
 tweet = re.sub(r"ServicesGold", "Services Gold", tweet)
 tweet = re.sub(r"NewsbrokenEmergency", "News broken emergency", tweet)
 tweet = re.sub(r"Evaucation", "evacuation", tweet)
 tweet = re.sub(r"leaveevacuateexitbe", "leave evacuate exit be", tweet)
 tweet = re.sub(r"P_EOPLE", "PEOPLE", tweet)
 tweet = re.sub(r"Tubestrike", "tube strike", tweet)
 tweet = re.sub(r"CLASS_SICK", "CLASS SICK", tweet)
 tweet = re.sub(r"localplumber", "local plumber", tweet)
 tweet = re.sub(r"awesomejobsiri", "awesome job siri", tweet)
 tweet = re.sub(r"PayForItHow", "Pay for it how", tweet)
 tweet = re.sub(r"ThisIsAfrica", "This is Africa", tweet)
 tweet = re.sub(r"crimeairnetwork", "crime air network", tweet)
 tweet = re.sub(r"KimAcheson", "Kim Acheson", tweet)
 tweet = re.sub(r"cityofcalgary", "City of Calgary", tweet)
 tweet = re.sub(r"prosyndicate", "pro syndicate", tweet)
 tweet = re.sub(r"660NEWS", "660 NEWS", tweet)
 tweet = re.sub(r"BusInsMagazine", "Business Insurance Magazine", tweet)
 tweet = re.sub(r"wfocus", "focus", tweet)
 tweet = re.sub(r"ShastaDam", "Shasta Dam", tweet)
 tweet = re.sub(r"go2MarkFranco", "Mark Franco", tweet)
 tweet = re.sub(r"StephGHinojosa", "Steph Hinojosa", tweet)
 tweet = re.sub(r"Nashgrier", "Nash Grier", tweet)
 tweet = re.sub(r"NashNewVideo", "Nash new video", tweet)
 tweet = re.sub(r"IWouldntGetElectedBecause", "I would not get elected because", tweet)
 tweet = re.sub(r"SHGames", "Sledgehammer Games", tweet)
 tweet = re.sub(r"bedhair", "bed hair", tweet)
 tweet = re.sub(r"JoelHeyman", "Joel Heyman", tweet)
 tweet = re.sub(r"viaYouTube", "via YouTube", tweet)
 tweet = re.sub(r"https?:\/\/t.co\/[A-Za-z0-9]+", "", tweet)
 punctuations = '@
for p in punctuations:
tweet = tweet.replace(p, f' {p} ')
 tweet = tweet.replace('...', '...')
 if '...' not in tweet:
tweet = tweet.replace('.. ', '...')
 tweet = re.sub(r"MH370", "Malaysia Airlines Flight 370", tweet)
 tweet = re.sub(r"mÌ¼sica", "music", tweet)
 tweet = re.sub(r"okwx", "Oklahoma City Weather", tweet)
 tweet = re.sub(r"arwx", "Arkansas Weather", tweet)
 tweet = re.sub(r"gawx", "Georgia Weather", tweet)
 tweet = re.sub(r"scwx", "South Carolina Weather", tweet)
 tweet = re.sub(r"cawx", "California Weather", tweet)
 tweet = re.sub(r"tnwx", "Tennessee Weather", tweet)
 tweet = re.sub(r"azwx", "Arizona Weather", tweet)
 tweet = re.sub(r"alwx", "Alabama Weather", tweet)
 tweet = re.sub(r"wordpressdotcom", "wordpress", tweet)
 tweet = re.sub(r"usNWSgov", "United States National Weather Service", tweet)
 tweet = re.sub(r"Suruc", "Sanliurfa", tweet)
 tweet = re.sub(r"Bestnaijamade", "bestnaijamade", tweet)
 tweet = re.sub(r"SOUDELOR", "Soudelor", tweet)
 return tweet
df_train['text_cleaned'] = df_train['text'].apply(lambda s : clean(s))
df_test['text_cleaned'] = df_test['text'].apply(lambda s : clean(s))
train_glove_oov, train_glove_vocab_coverage, train_glove_text_coverage = check_embeddings_coverage(df_train['text_cleaned'], glove_embeddings)
 test_glove_oov, test_glove_vocab_coverage, test_glove_text_coverage = check_embeddings_coverage(df_test['text_cleaned'], glove_embeddings)
 print('GloVe Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Training Set'.format(train_glove_vocab_coverage, train_glove_text_coverage))
print('GloVe Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Test Set'.format(test_glove_vocab_coverage, test_glove_text_coverage))
train_fasttext_oov, train_fasttext_vocab_coverage, train_fasttext_text_coverage = check_embeddings_coverage(df_train['text_cleaned'], fasttext_embeddings)
 test_fasttext_oov, test_fasttext_vocab_coverage, test_fasttext_text_coverage = check_embeddings_coverage(df_test['text_cleaned'], fasttext_embeddings)
 print('FastText Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Training Set'.format(train_fasttext_vocab_coverage, train_fasttext_text_coverage))
print('FastText Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Test Set'.format(test_fasttext_vocab_coverage, test_fasttext_text_coverage))<drop_column>
oof_reg_preds = np.zeros(val_x.shape[0])
 oof_reg_preds1 = np.zeros(val_x.shape[0])
 oof_reg_preds2 = np.zeros(val_x.shape[0])
 merge_pred = np.zeros(val_x.shape[0])
 merge_preds = np.zeros(val_x.shape[0])
 sub_preds = np.zeros(test_x.shape[0])
 alist = list(range(time_to.shape[0]-1))
alist.remove(4)
 folds = alist
folds=range(len(alist)-1)
 for i in alist:
print(i)
 user_x = df[(df.visitStartTime>=(time_to.index.astype('int')//10**9)[i])&(df.visitStartTime<(time_end.index.astype('int')//10**9)[i])]
user_y = df[(df.visitStartTime>=time_to.values[i])&(df.visitStartTime<time_to.values[i+1])]
train_x = pd.concat([user_x.groupby('fullVisitorId')[to_median].median().add_suffix('_median'),
user_x.groupby('fullVisitorId')['visitStartTime'].agg(['first','last'] ).add_suffix('_time' ).sub(time_to.values[i] ).abs() ,
user_x.groupby('fullVisitorId')['visitStartTime'].apply(lambda x: x.max() -x.min() ).rename('time_diff'),
user_x.groupby('fullVisitorId')[to_sum].sum().add_suffix('_sum'),
user_x.groupby('fullVisitorId')[to_mean].mean().add_suffix('_mean'),
user_x.groupby('fullVisitorId')[to_std].std(ddof=0 ).add_suffix('_std')],axis=1 ).reset_index()
merged=train_x.merge(user_y.groupby('fullVisitorId')['totals_transactionRevenue'].sum().reset_index() ,\
how='left', on='fullVisitorId')
 train_y = merged.totals_transactionRevenue
train_y.fillna(0, inplace=True)
 train_x = merged.drop(['fullVisitorId','totals_transactionRevenue'], axis=1,errors='ignore')
 train_x = train_x.astype('int')
 reg = lgb.LGBMRegressor(**params,n_estimators=1100)
 xgb = XGBRegressor(**xgb_params, n_estimators=1000)
 cat = CatBoostRegressor(iterations=1000,learning_rate=0.03,
depth=10,
eval_metric='RMSE',
random_seed = 42,
bagging_temperature = 0.2,
od_type='Iter',
metric_period = 50,
od_wait=20)
 print("-"* 20 + "LightGBM Training" + "-"* 20)
 reg.fit(train_x, np.log1p(train_y),eval_set=[(val_x, np.log1p(val_y)) ],early_stopping_rounds=50,verbose=100,eval_metric='rmse')
 print("-"* 20 + "XGboost Training" + "-"* 20)
 xgb.fit(train_x, np.log1p(train_y),eval_set=[(val_x, np.log1p(val_y)) ],early_stopping_rounds=50,eval_metric='rmse',verbose=100)
 print("-"* 20 + "Catboost Training" + "-"* 20)
 cat.fit(train_x, np.log1p(train_y), eval_set=[(val_x, np.log1p(val_y)) ],early_stopping_rounds=50,use_best_model=True,verbose=100)
 imp_df = pd.DataFrame()
imp_df['feature'] = train_x.columns
imp_df['gain_reg'] = np.zeros(train_x.shape[1])
 imp_df['gain_xgb'] = np.zeros(train_x.shape[1])
 imp_df['gain_cat'] = np.zeros(train_x.shape[1])
 imp_df['gain_reg'] += reg.booster_.feature_importance(importance_type='gain')/ len(folds)
 imp_df['gain_xgb'] += xgb.feature_importances_/ len(folds)
 imp_df['gain_cat'] += np.array(cat.get_feature_importance())/ len(folds)
 oof_reg_preds = reg.predict(val_x, num_iteration=reg.best_iteration_)
 oof_reg_preds[oof_reg_preds < 0] = 0
lgb_preds = reg.predict(test_x, num_iteration=reg.best_iteration_)
 lgb_preds[lgb_preds < 0] = 0
oof_reg_preds1 = xgb.predict(val_x)
 oof_reg_preds1[oof_reg_preds1 < 0] = 0
xgb_preds = xgb.predict(test_x)
 xgb_preds[xgb_preds < 0] = 0
oof_reg_preds2 = cat.predict(val_x)
 oof_reg_preds2[oof_reg_preds2 < 0] = 0
cat_preds = cat.predict(test_x)
 cat_preds[cat_preds < 0] = 0
merge_pred = oof_reg_preds * 0.4 + oof_reg_preds1 * 0.3 +oof_reg_preds2 * 0.3
merge_preds +=(oof_reg_preds / len(folds)) * 0.4 +(oof_reg_preds1 / len(folds)) * 0.3 +(oof_reg_preds2 / len(folds)) * 0.3
sub_preds +=(lgb_preds / len(folds)) * 0.4 +(xgb_preds / len(folds)) * 0.3 +(cat_preds / len(folds)) * 0.3
print("LGBM ", mean_squared_error(np.log1p(val_y), oof_reg_preds)**.5)
 print("XGBoost ", mean_squared_error(np.log1p(val_y), oof_reg_preds1)**.5)
 print("CatBoost ", mean_squared_error(np.log1p(val_y), oof_reg_preds2)**.5)
 print("merged ", mean_squared_error(np.log1p(val_y), merge_pred)**.5)
 print("stack_merged ", mean_squared_error(np.log1p(val_y), merge_preds)**.5)
 print("Zeros ", mean_squared_error(np.log1p(val_y), np.zeros(val_x.shape[0])) **.5 )
del glove_embeddings, fasttext_embeddings, train_glove_oov, test_glove_oov, train_fasttext_oov, test_fasttext_oov
gc.collect()<count_unique_values>
sub_df = pd.DataFrame(test_ID)
 sub_df["PredictedLogRevenue"] = sub_preds
sub_df.to_csv("stacked_result.csv", index=False )