Kaggle competition: Natural Language Processing with Disaster Tweets
feats = [f for f in df.columns if f not in ['TARGET','SK_ID_CURR','SK_ID_BUREAU','SK_ID_PREV','index']]
for c in feats:
ss = StandardScaler()
df.loc[~np.isfinite(df[c]),c] = np.nan
df.loc[~df[c].isnull() ,c] = ss.fit_transform(df.loc[~df[c].isnull() ,c].values.reshape(-1,1))
df[c].fillna(-99999.,inplace=True )<feature_engineering>
import re
import pandas as pd
from transformers import BertForSequenceClassification, BertTokenizer, Trainer, TrainingArguments, AdamW, BertConfig
import torch
import numpy as np
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
from torch.utils.data import TensorDataset, random_split
from sklearn.model_selection import train_test_split
from sklearn.base import BaseEstimator
from sklearn.base import TransformerMixin
from nltk.corpus import stopwords
def Output(p):
return 1./(1.+np.exp(-p))
def GP1(data):
v = pd.DataFrame()
v["i0"] = 0.005976*np.tanh(((((np.minimum(((((((((np.tanh(( np.minimum(((data["DAYS_EMPLOYED"])) ,(( data["REGION_RATING_CLIENT_W_CITY"])))))) -(data["EXT_SOURCE_3"])))* 2.0)) +(data["NEW_CREDIT_TO_GOODS_RATIO"])))) ,(((-1.0*(( data["NEW_SOURCES_PROD"])))))))* 2.0)) * 2.0))
v["i1"] = 0.040171*np.tanh(((((((((((( data["DAYS_EMPLOYED"])>(((data["EXT_SOURCE_2"])+(np.maximum(((data["EXT_SOURCE_1"])) ,(( data["EXT_SOURCE_3"])))))))*1.))-(np.maximum(((data["EXT_SOURCE_3"])) ,(( data["EXT_SOURCE_2"])))))) * 2.0)) * 2.0)) * 2.0))
v["i2"] = 0.049975*np.tanh(((((((((((((((((np.tanh(( data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"])))-(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) -(np.tanh(( data["CC_AMT_PAYMENT_CURRENT_SUM"])))))* 2.0)) * 2.0)) * 2.0)) -(data["NEW_EXT_SOURCES_MEAN"])))* 2.0))
v["i3"] = 0.049570*np.tanh(((((((((data["NAME_INCOME_TYPE_Working"])+(((((((( -1.0*(( data["NEW_EXT_SOURCES_MEAN"])))) * 2.0)) -(((( data["CC_AMT_PAYMENT_CURRENT_MIN"])<(data["BURO_DAYS_CREDIT_ENDDATE_MAX"])) *1.))))* 2.0)))) * 2.0)) * 2.0)) * 2.0))
v["i4"] = 0.040171*np.tanh(((((( -1.0*(((((((data["APPROVED_DAYS_DECISION_MIN"])<(((( data["NEW_EXT_SOURCES_MEAN"])>(((data["INSTAL_DPD_MEAN"])* 2.0)))*1.))) *1.))+(((data["NEW_EXT_SOURCES_MEAN"])* 2.0)))))))* 2.0)) * 2.0))
v["i5"] = 0.040171*np.tanh(((((data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])-(((data["NEW_EXT_SOURCES_MEAN"])+(((np.tanh(( data["PREV_RATE_DOWN_PAYMENT_MAX"])))+(((((((( data["CODE_GENDER"])>(data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])) *1.))+(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)))))))) * 2.0))
v["i6"] = 0.049088*np.tanh(((((((((((((( -1.0*(( data["NEW_EXT_SOURCES_MEAN"])))) * 2.0)) +(np.tanh(((( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])+(data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])))))))+(((data["DAYS_BIRTH"])/ 2.0)))) * 2.0)) * 2.0)) * 2.0))
v["i7"] = 0.040171*np.tanh(((((data["NEW_EXT_SOURCES_MEAN"])* 2.0)) *(np.minimum(((data["NEW_EXT_SOURCES_MEAN"])) ,(((((((((( data["NEW_EXT_SOURCES_MEAN"])*(data["REFUSED_APP_CREDIT_PERC_MAX"])))+(data["ACTIVE_DAYS_CREDIT_ENDDATE_MEAN"])) /2.0)) +(( -1.0*(((8.0)))))) /2.0)))))))
 v["i8"] = 0.047400*np.tanh(((((((((( -1.0*(((( data["NEW_EXT_SOURCES_MEAN"])+(np.maximum(((np.maximum(((( -1.0*(( data["NAME_EDUCATION_TYPE_Secondary___secondary_special"]))))),(( data["NEW_CAR_TO_BIRTH_RATIO"]))))),(( data["NEW_EMPLOY_TO_BIRTH_RATIO"])))))))))-(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) * 2.0)) * 2.0))
v["i9"] = 0.040171*np.tanh(((((((((((( data["DAYS_EMPLOYED"])-(data["NAME_EDUCATION_TYPE_Higher_education"])))+(data["NEW_CREDIT_TO_GOODS_RATIO"])) /2.0)) -(((data["EXT_SOURCE_2"])-(np.tanh(((( data["ACTIVE_DAYS_CREDIT_MAX"])-(data["BURO_CREDIT_TYPE_Mortgage_MEAN"])))))))))* 2.0)) * 2.0))
v["i10"] = 0.049975*np.tanh(( -1.0*(((((((( data["NEW_EXT_SOURCES_MEAN"])-(( -1.0*(( np.tanh(((((( data["CODE_GENDER"])-(((data["DAYS_EMPLOYED"])+(data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))))-(data["PREV_CNT_PAYMENT_MEAN"])))))))))) * 2.0)) * 2.0)))))
 v["i11"] = 0.049620*np.tanh(((((((((((((((data["NEW_CREDIT_TO_GOODS_RATIO"])-(((( data["EXT_SOURCE_3"])>(np.tanh(( data["CC_AMT_DRAWINGS_CURRENT_MEAN"])))) *1.))))-(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) -(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) * 2.0)) * 2.0))
v["i12"] = 0.050000*np.tanh(((((((((np.minimum(((data["NEW_CREDIT_TO_GOODS_RATIO"])) ,(( data["NEW_DOC_IND_KURT"])))) -(((data["NEW_EXT_SOURCES_MEAN"])-(np.tanh(((( data["DAYS_EMPLOYED"])-(data["NEW_DOC_IND_KURT"])))))))))* 2.0)) * 2.0)) -(data["NAME_EDUCATION_TYPE_Higher_education"])))
 v["i13"] = 0.050000*np.tanh(((((((((np.tanh(((( data["DAYS_EMPLOYED"])+(data["INSTAL_PAYMENT_DIFF_MAX"])))))-(data["NEW_EXT_SOURCES_MEAN"])))+(np.tanh(((( np.tanh(( data["REFUSED_DAYS_DECISION_MEAN"])))+(data["NEW_CREDIT_TO_GOODS_RATIO"])))))))* 2.0)) * 2.0))
v["i14"] = 0.047100*np.tanh(((((((((((data["NEW_CREDIT_TO_GOODS_RATIO"])-(data["NAME_EDUCATION_TYPE_Higher_education"])))-(data["CODE_GENDER"])))-(data["EXT_SOURCE_2"])))+(((((np.tanh(( data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"])))-(data["EXT_SOURCE_3"])))* 2.0)))) * 2.0))
v["i15"] = 0.049950*np.tanh(((((((data["NEW_CREDIT_TO_GOODS_RATIO"])-(data["NEW_EXT_SOURCES_MEAN"])))-(((((data["INSTAL_DBD_SUM"])-(np.tanh(( data["CC_AMT_RECIVABLE_MEAN"])))))-(np.maximum(((data["PREV_CNT_PAYMENT_MEAN"])) ,(( data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))))))) * 2.0))
v["i16"] = 0.046512*np.tanh(((((((data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])-(data["EXT_SOURCE_2"])))+(np.where(data["EXT_SOURCE_3"] < -99998, data["DAYS_EMPLOYED"],(((( data["NAME_INCOME_TYPE_Working"])-(data["CODE_GENDER"])))-(((data["EXT_SOURCE_3"])* 2.0)))))))* 2.0))
v["i17"] = 0.049820*np.tanh(((((np.where(data["APPROVED_AMT_DOWN_PAYMENT_MAX"]>0, data["REFUSED_DAYS_DECISION_MAX"], data["DAYS_LAST_PHONE_CHANGE"])) +(((data["NEW_DOC_IND_KURT"])+(((((data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])-(data["NEW_EXT_SOURCES_MEAN"])))-(data["CODE_GENDER"])))))))-(data["NEW_EXT_SOURCES_MEAN"])))
 v["i18"] = 0.049119*np.tanh(((((data["DEF_30_CNT_SOCIAL_CIRCLE"])+(((((((((data["PREV_CNT_PAYMENT_MEAN"])-(data["POS_MONTHS_BALANCE_SIZE"])))-(data["NEW_EXT_SOURCES_MEAN"])))+(((((((data["INSTAL_DPD_MEAN"])* 2.0)) * 2.0)) * 2.0)))) * 2.0)))) * 2.0))
v["i19"] = 0.048000*np.tanh(((data["PREV_NAME_YIELD_GROUP_high_MEAN"])-(((((data["PREV_NAME_CONTRACT_STATUS_Approved_MEAN"])+(data["NEW_EXT_SOURCES_MEAN"])))+(((((np.maximum(((data["CODE_GENDER"])) ,(( data["APPROVED_RATE_DOWN_PAYMENT_MAX"])))) +(np.tanh(( data["BURO_CREDIT_ACTIVE_Closed_MEAN"])))))-(data["NEW_ANNUITY_TO_INCOME_RATIO"])))))))
 v["i20"] = 0.049970*np.tanh(((((((data["REGION_RATING_CLIENT_W_CITY"])+(np.where(np.maximum(((np.minimum(((data["DAYS_EMPLOYED"])) ,(( data["FLAG_DOCUMENT_3"]))))),(( data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))>0,(-1.0*(( data["EXT_SOURCE_3"]))), data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"])))) * 2.0)) +(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])))
 v["i21"] = 0.049799*np.tanh(((data["NEW_CREDIT_TO_GOODS_RATIO"])+(( -1.0*(((( data["NEW_EXT_SOURCES_MEAN"])+(((data["APPROVED_AMT_GOODS_PRICE_MIN"])-(((((((((data["INSTAL_PAYMENT_DIFF_MAX"])* 2.0)) -(data["POS_MONTHS_BALANCE_SIZE"])))* 2.0)) * 2.0)))))))))))
 v["i22"] = 0.049870*np.tanh(((((np.where(data["NEW_CAR_TO_BIRTH_RATIO"]>0, data["REFUSED_DAYS_DECISION_MAX"], np.maximum(((data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"])) ,(( data["AMT_ANNUITY"])))))-(((data["APPROVED_RATE_DOWN_PAYMENT_MAX"])+(data["CODE_GENDER"])))))-(((data["NEW_EXT_SOURCES_MEAN"])-(data["PREV_NAME_YIELD_GROUP_high_MEAN"])))))
 v["i23"] = 0.047896*np.tanh(((((((np.where(data["CC_AMT_BALANCE_MAX"] < -99998,(( data["DAYS_EMPLOYED"])-(((np.maximum(((data["APPROVED_AMT_DOWN_PAYMENT_MAX"])) ,(( data["FLOORSMAX_AVG"])))) * 2.0))),(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])* 2.0)))* 2.0)) -(data["NEW_EXT_SOURCES_MEAN"])))* 2.0))
v["i24"] = 0.046500*np.tanh(((((((((((((data["PREV_CNT_PAYMENT_MEAN"])-(data["POS_MONTHS_BALANCE_SIZE"])))* 2.0)) -(data["NEW_EXT_SOURCES_MEAN"])))+(((data["PREV_NAME_YIELD_GROUP_high_MEAN"])+(np.tanh(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])))))))+(data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))* 2.0))
v["i25"] = 0.048000*np.tanh(((data["NEW_ANNUITY_TO_INCOME_RATIO"])-(((data["NEW_EXT_SOURCES_MEAN"])-(((data["FLAG_DOCUMENT_3"])-(np.maximum(((((np.maximum(((data["NEW_CAR_TO_BIRTH_RATIO"])) ,(((((( data["INSTAL_AMT_PAYMENT_MIN"])* 2.0)) * 2.0)))))* 2.0))),(( data["BURO_CREDIT_ACTIVE_Closed_MEAN"]))))))))))
v["i26"] = 0.047900*np.tanh(((np.where(data["NAME_EDUCATION_TYPE_Higher_education"]>0, data["CC_AMT_RECIVABLE_MAX"], np.where(data["PREV_NAME_YIELD_GROUP_low_action_MEAN"]>0, data["CC_AMT_RECIVABLE_MEAN"],(( np.where(data["DAYS_EMPLOYED"]<0,(-1.0*(( data["BURO_CREDIT_ACTIVE_Closed_MEAN"]))),(-1.0*(( data["EXT_SOURCE_1"])))))* 2.0)))) * 2.0))
v["i27"] = 0.049976*np.tanh(((((((((((data["PREV_NAME_PRODUCT_TYPE_walk_in_MEAN"])-(((data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])-(data["APPROVED_DAYS_DECISION_MIN"])))))-(data["APPROVED_AMT_ANNUITY_MEAN"])))+(data["NEW_DOC_IND_KURT"])))-(((data["INSTAL_AMT_PAYMENT_MIN"])-(data["INSTAL_AMT_INSTALMENT_MAX"])))))* 2.0))
v["i28"] = 0.049718*np.tanh(((((np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]>0, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"], np.where(data["POS_MONTHS_BALANCE_SIZE"]>0, data["APPROVED_CNT_PAYMENT_MEAN"], np.where(data["INSTAL_AMT_PAYMENT_MIN"]>0, data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"],(-1.0*(( data["NEW_SOURCES_PROD"])))))))* 2.0)) * 2.0))
v["i29"] = 0.045700*np.tanh(((((((((data["INSTAL_PAYMENT_DIFF_SUM"])+(data["PREV_CNT_PAYMENT_MEAN"])))+(((((data["PREV_NAME_CLIENT_TYPE_New_MEAN"])+(((data["NEW_CREDIT_TO_GOODS_RATIO"])+(data["DEF_30_CNT_SOCIAL_CIRCLE"])))))+(data["PREV_NAME_YIELD_GROUP_XNA_MEAN"])))))-(data["CODE_GENDER"])))* 2.0))
v["i30"] = 0.049198*np.tanh(((((((((np.tanh(( data["AMT_ANNUITY"])))+(((data["PREV_CNT_PAYMENT_SUM"])-(np.maximum(((data["NEW_CAR_TO_EMPLOY_RATIO"])) ,(( data["POS_COUNT"])))))))) -(((data["INSTAL_AMT_PAYMENT_MIN"])+(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])))))* 2.0)) * 2.0))
v["i31"] = 0.049397*np.tanh(((((np.where(data["ACTIVE_DAYS_CREDIT_VAR"] < -99998,(((( data["INSTAL_PAYMENT_DIFF_MAX"])* 2.0)) -(data["APPROVED_AMT_ANNUITY_MEAN"])) ,(( data["APPROVED_CNT_PAYMENT_MEAN"])+(((data["ACTIVE_DAYS_CREDIT_MEAN"])-(data["BURO_CREDIT_ACTIVE_Closed_MEAN"])))))) +(data["DEF_60_CNT_SOCIAL_CIRCLE"])))* 2.0))
v["i32"] = 0.048984*np.tanh(((((data["REGION_RATING_CLIENT_W_CITY"])+(np.where(((data["APPROVED_AMT_ANNUITY_MAX"])-(((data["INSTAL_DPD_MEAN"])-(data["CODE_GENDER"])))) <0,(( data["REGION_RATING_CLIENT_W_CITY"])-(data["NEW_CAR_TO_BIRTH_RATIO"])) , data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"])))) -(data["PREV_NAME_PORTFOLIO_POS_MEAN"])))
 v["i33"] = 0.049560*np.tanh(((((data["AMT_ANNUITY"])-(((np.where(data["CC_CNT_DRAWINGS_ATM_CURRENT_MAX"]>0, data["EXT_SOURCE_1"],(( np.maximum(((((data["NAME_FAMILY_STATUS_Married"])+(data["EXT_SOURCE_3"])))) ,(( data["APPROVED_AMT_DOWN_PAYMENT_MAX"])))) * 2.0)))-(data["REGION_RATING_CLIENT_W_CITY"])))))* 2.0))
v["i34"] = 0.049700*np.tanh(((((((np.maximum(((data["NEW_CREDIT_TO_GOODS_RATIO"])) ,(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])))) +(((((((data["PREV_NAME_CLIENT_TYPE_New_MEAN"])+(((data["APPROVED_CNT_PAYMENT_MEAN"])-(data["PREV_NAME_YIELD_GROUP_low_action_MEAN"])))))-(data["PREV_CODE_REJECT_REASON_XAP_MEAN"])))* 2.0)))) * 2.0)) * 2.0))
v["i35"] = 0.049646*np.tanh(np.where(data["POS_SK_DPD_DEF_MAX"]<0,(((((((( data["FLAG_WORK_PHONE"])+(((np.where(data["EXT_SOURCE_1"] < -99998, data["DAYS_EMPLOYED"], data["CC_CNT_DRAWINGS_CURRENT_MEAN"])) * 2.0)))) * 2.0)) * 2.0)) * 2.0), 3.141593))
v["i36"] = 0.049390*np.tanh(((data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])+(((data["BURO_CREDIT_ACTIVE_Active_MEAN"])+(((data["NEW_ANNUITY_TO_INCOME_RATIO"])+(((((np.where(data["CLOSED_DAYS_CREDIT_VAR"] < -99998,(( data["INSTAL_AMT_PAYMENT_SUM"])*(data["CLOSED_DAYS_CREDIT_VAR"])) , data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"])) * 2.0)) * 2.0))))))))
v["i37"] = 0.049750*np.tanh(((((np.where(np.where(data["PREV_NAME_CLIENT_TYPE_Repeater_MEAN"]>0, data["PREV_APP_CREDIT_PERC_MEAN"], data["NEW_CAR_TO_BIRTH_RATIO"])>0,(( data["CC_CNT_DRAWINGS_ATM_CURRENT_VAR"])* 2.0),(-1.0*(( np.where(data["OCCUPATION_TYPE_Core_staff"]<0, data["ENTRANCES_MEDI"], data["OCCUPATION_TYPE_Core_staff"])))))) * 2.0)) * 2.0))
v["i38"] = 0.049300*np.tanh(((((((((np.maximum(((np.minimum(((data["REGION_RATING_CLIENT_W_CITY"])) ,(( data["AMT_ANNUITY"]))))),(((( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])* 2.0)))))-(((data["APPROVED_AMT_ANNUITY_MEAN"])-(data["DEF_30_CNT_SOCIAL_CIRCLE"])))))+(data["INSTAL_PAYMENT_DIFF_MEAN"])))* 2.0)) * 2.0))
v["i39"] = 0.049060*np.tanh(((((data["ORGANIZATION_TYPE_Self_employed"])+(((np.where(data["POS_SK_DPD_DEF_MEAN"]<0, data["REG_CITY_NOT_LIVE_CITY"],(8.0)))-(np.where(data["BURO_AMT_CREDIT_SUM_DEBT_SUM"]<0, np.maximum(((data["NAME_FAMILY_STATUS_Married"])) ,(( data["POS_SK_DPD_DEF_MAX"]))), data["NEW_CAR_TO_EMPLOY_RATIO"])))))) * 2.0))
v["i40"] = 0.048602*np.tanh(((((np.where(np.maximum(((data["CC_AMT_CREDIT_LIMIT_ACTUAL_SUM"])) ,(( np.maximum(((np.where(data["INSTAL_DBD_MEAN"]<0, data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"], data["NAME_FAMILY_STATUS_Married"]))),(( data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])))))) <0,(-1.0*(( data["FLOORSMAX_AVG"]))), data["APPROVED_CNT_PAYMENT_MEAN"])) * 2.0)) * 2.0))
v["i41"] = 0.048139*np.tanh(np.where(np.maximum(((data["INSTAL_DPD_MEAN"])) ,(( data["BURO_CREDIT_TYPE_Microloan_MEAN"])))<0,(( np.where(data["NEW_EXT_SOURCES_MEAN"]>0, data["DAYS_ID_PUBLISH"], data["ACTIVE_DAYS_CREDIT_MEAN"])) -(np.where(data["NEW_CAR_TO_EMPLOY_RATIO"]>0, 3.141593, data["NAME_EDUCATION_TYPE_Higher_education"]))), 3.141593))
v["i42"] = 0.048501*np.tanh(((((((np.maximum(((data["APPROVED_CNT_PAYMENT_MEAN"])) ,(( data["FLAG_WORK_PHONE"])))) -(data["CODE_GENDER"])))+(np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0,(((( data["NEW_CREDIT_TO_ANNUITY_RATIO"])-(data["PREV_AMT_APPLICATION_MEAN"])))* 2.0), data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_MAX"])))) * 2.0))
v["i43"] = 0.049898*np.tanh(((((np.where(data["APPROVED_AMT_CREDIT_MEAN"]>0, data["APPROVED_CNT_PAYMENT_SUM"],(( data["AMT_ANNUITY"])-(data["PREV_PRODUCT_COMBINATION_POS_industry_with_interest_MEAN"])))) -(np.maximum(((data["NEW_DOC_IND_AVG"])) ,(( data["PREV_NAME_YIELD_GROUP_low_action_MEAN"])))))) -(((data["INSTAL_DBD_SUM"])+(data["NAME_INCOME_TYPE_State_servant"])))))
 v["i44"] = 0.049002*np.tanh(((((np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"])) ,(((( np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["NEW_CREDIT_TO_GOODS_RATIO"])))) -(np.maximum(((np.where(data["POS_SK_DPD_DEF_MAX"]<0, data["NAME_FAMILY_STATUS_Married"], data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"]))),(( data["CLOSED_DAYS_CREDIT_MAX"])))))))))* 2.0)) * 2.0))
v["i45"] = 0.049600*np.tanh(((((((((((np.maximum(((np.where(data["CC_AMT_TOTAL_RECEIVABLE_MEAN"] < -99998, data["INSTAL_DPD_MEAN"], data["CC_AMT_RECEIVABLE_PRINCIPAL_MIN"]))),(( data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])))) * 2.0)) * 2.0)) * 2.0)) +(np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["APPROVED_CNT_PAYMENT_SUM"])))))) * 2.0))
v["i46"] = 0.049079*np.tanh(((((((((( -1.0*(( np.maximum(((data["EXT_SOURCE_1"])) ,(( data["POS_COUNT"])))))))+(np.where(np.maximum(((data["APPROVED_CNT_PAYMENT_SUM"])) ,(( data["INSTAL_DBD_SUM"])))<0, data["OCCUPATION_TYPE_Laborers"], data["APPROVED_CNT_PAYMENT_SUM"])))) * 2.0)) * 2.0)) * 2.0))
v["i47"] = 0.049180*np.tanh(((((np.minimum(((((((((data["DAYS_REGISTRATION"])-(data["BURO_CREDIT_TYPE_Mortgage_MEAN"])))-(data["OCCUPATION_TYPE_Core_staff"])))-(data["BURO_CREDIT_TYPE_Mortgage_MEAN"])))) ,(((( data["INSTAL_PAYMENT_DIFF_SUM"])-(data["APPROVED_HOUR_APPR_PROCESS_START_MAX"])))))) -(data["NAME_EDUCATION_TYPE_Incomplete_higher"])))* 2.0))
v["i48"] = 0.049515*np.tanh(((((((np.where(data["CC_AMT_RECEIVABLE_PRINCIPAL_SUM"] < -99998,(( data["AMT_ANNUITY"])-(data["APPROVED_AMT_ANNUITY_MEAN"])) , data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"])) +(np.maximum(((data["OCCUPATION_TYPE_Drivers"])) ,(( data["BURO_CREDIT_TYPE_Microloan_MEAN"])))))) * 2.0)) * 2.0))
v["i49"] = 0.049802*np.tanh(((data["INSTAL_PAYMENT_DIFF_MAX"])+(np.where(data["BURO_CREDIT_ACTIVE_Active_MEAN"]<0, np.where(data["BURO_DAYS_CREDIT_MAX"]<0, data["DAYS_LAST_PHONE_CHANGE"], data["BURO_CREDIT_ACTIVE_Active_MEAN"]),(( data["FLAG_WORK_PHONE"])-(((data["PREV_HOUR_APPR_PROCESS_START_MEAN"])-(((data["ACTIVE_DAYS_CREDIT_MAX"])* 2.0)))))))))
 v["i50"] = 0.049043*np.tanh(( -1.0*(( np.where(data["EXT_SOURCE_1"]>0, data["NEW_EXT_SOURCES_MEAN"],(((((((( data["NEW_EXT_SOURCES_MEAN"])-(((np.tanh(( np.tanh(((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)))))) * 2.0)))) * 2.0)) * 2.0)) * 2.0))))))
v["i51"] = 0.047443*np.tanh(((((data["ORGANIZATION_TYPE_Construction"])+(((data["REG_CITY_NOT_LIVE_CITY"])+(((data["ORGANIZATION_TYPE_Business_Entity_Type_3"])+(np.maximum(((data["NEW_SCORES_STD"])) ,(((( np.maximum(((data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"])) ,(( data["PREV_CODE_REJECT_REASON_SCOFR_MEAN"])))) +(data["PREV_CNT_PAYMENT_MEAN"])))))))))))) * 2.0))
v["i52"] = 0.049973*np.tanh(((data["REGION_RATING_CLIENT_W_CITY"])+(np.where(data["NEW_EXT_SOURCES_MEAN"]<0,(((( np.where(data["POS_SK_DPD_DEF_MAX"]<0, data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"], data["POS_MONTHS_BALANCE_MEAN"])) * 2.0)) * 2.0),(((( data["POS_MONTHS_BALANCE_MEAN"])-(data["NEW_CAR_TO_BIRTH_RATIO"])))* 2.0)))))
 v["i53"] = 0.047044*np.tanh(((((((np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]>0, data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"], data["NEW_CREDIT_TO_ANNUITY_RATIO"])) -(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])))-(((data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])-(data["NEW_ANNUITY_TO_INCOME_RATIO"])))))+(np.maximum(((data["ACTIVE_DAYS_CREDIT_ENDDATE_MAX"])) ,(( data["BURO_CREDIT_TYPE_Microloan_MEAN"]))))))
v["i54"] = 0.049700*np.tanh(((((((((((((data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__high_MEAN"])+(np.maximum(((data["PREV_PRODUCT_COMBINATION_Cash_Street__high_MEAN"])) ,(( data["CC_AMT_RECEIVABLE_PRINCIPAL_MEAN"])))))) -(data["INSTAL_AMT_PAYMENT_SUM"])))* 2.0)) * 2.0)) -(np.maximum(((data["PREV_APP_CREDIT_PERC_MEAN"])) ,(( data["BURO_CREDIT_TYPE_Mortgage_MEAN"])))))) * 2.0))
v["i55"] = 0.049609*np.tanh(((((np.where(data["EXT_SOURCE_3"] < -99998, data["REFUSED_DAYS_DECISION_MAX"],(( data["EXT_SOURCE_3"])*(data["EXT_SOURCE_3"])))) -(data["BURO_DAYS_CREDIT_MAX"])))+(np.where(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"] < -99998, data["EXT_SOURCE_3"], -1.0))))
v["i56"] = 0.049496*np.tanh(((np.maximum(((data["DAYS_ID_PUBLISH"])) ,(( data["ACTIVE_DAYS_CREDIT_MAX"])))) -(np.maximum(((data["BURO_CREDIT_TYPE_Mortgage_MEAN"])) ,(((((((( data["NEW_DOC_IND_AVG"])+(data["NAME_INCOME_TYPE_State_servant"])))+(data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])))-(data["NEW_CREDIT_TO_ANNUITY_RATIO"]))))))))
v["i57"] = 0.048440*np.tanh(((np.where(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"]<0, data["AMT_ANNUITY"], data["APPROVED_CNT_PAYMENT_MEAN"])) +(((np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["POS_NAME_CONTRACT_STATUS_Returned_to_the_store_MEAN"])))) +(((data["ORGANIZATION_TYPE_Construction"])+(((data["EXT_SOURCE_2"])*(data["INSTAL_DBD_SUM"])))))))))
 v["i58"] = 0.044197*np.tanh(((((np.where(data["CODE_GENDER"]<0, np.maximum(((data["WALLSMATERIAL_MODE_Stone__brick"])) ,(( data["FLAG_DOCUMENT_3"]))), np.where(np.where(data["PREV_PRODUCT_COMBINATION_Cash_MEAN"]<0, data["FLAG_WORK_PHONE"], data["CC_CNT_DRAWINGS_CURRENT_VAR"])<0, data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"], data["FLAG_DOCUMENT_3"])))* 2.0)) * 2.0))
v["i59"] = 0.049650*np.tanh(((((data["WALLSMATERIAL_MODE_Stone__brick"])-(np.where(data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"], np.maximum(((np.maximum(((data["NEW_SOURCES_PROD"])) ,(( data["ACTIVE_AMT_CREDIT_SUM_MAX"]))))),(( np.maximum(((data["APPROVED_AMT_CREDIT_MIN"])) ,(( data["BURO_STATUS_0_MEAN_MEAN"])))))))))) -(data["NAME_INCOME_TYPE_Commercial_associate"])))
 v["i60"] = 0.047884*np.tanh(np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_MAX"], np.where(((data["NEW_RATIO_BURO_AMT_CREDIT_SUM_LIMIT_MEAN"])*(data["NEW_EXT_SOURCES_MEAN"])) < -99998, data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"],(((( data["ORGANIZATION_TYPE_Self_employed"])+(((( data["PREV_NAME_GOODS_CATEGORY_Furniture_MEAN"])<(data["NEW_EXT_SOURCES_MEAN"])) *1.))))* 2.0))))
v["i61"] = 0.048710*np.tanh(np.where(data["EXT_SOURCE_3"] < -99998, data["REFUSED_CNT_PAYMENT_SUM"],(((((((( -1.0)-(np.minimum(((((data["EXT_SOURCE_2"])/ 2.0))),(( data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])))))) * 2.0)) -(data["EXT_SOURCE_3"])))-(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"]))))
v["i62"] = 0.049366*np.tanh(np.where(data["AMT_REQ_CREDIT_BUREAU_QRT"]>0, data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"],(( np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"]>0, data["CC_CNT_DRAWINGS_CURRENT_SUM"],(((( data["NEW_RATIO_PREV_AMT_CREDIT_MIN"])*(data["OCCUPATION_TYPE_Accountants"])))-(data["AMT_REQ_CREDIT_BUREAU_YEAR"])))) -(data["NEW_PHONE_TO_EMPLOY_RATIO"]))))
v["i63"] = 0.048000*np.tanh(((np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"]>0, data["BURO_DAYS_CREDIT_MEAN"], np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"] < -99998, data["DEF_30_CNT_SOCIAL_CIRCLE"],(((( data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"])* 2.0)) * 2.0)))) -(np.where(data["ACTIVE_MONTHS_BALANCE_MIN_MIN"]>0, data["ACTIVE_MONTHS_BALANCE_MIN_MIN"], data["NAME_FAMILY_STATUS_Married"]))))
v["i64"] = 0.048498*np.tanh(((np.where(data["AMT_GOODS_PRICE"]<0, np.maximum(((data["AMT_ANNUITY"])) ,(((((((((( data["AMT_ANNUITY"])-(data["PREV_AMT_ANNUITY_MIN"])))* 2.0)) -(data["PREV_AMT_ANNUITY_MIN"])))* 2.0)))) , data["INSTAL_AMT_INSTALMENT_MAX"])) -(data["NEW_DOC_IND_STD"])))
 v["i65"] = 0.045500*np.tanh(((data["APPROVED_CNT_PAYMENT_SUM"])-(((data["INSTAL_COUNT"])-(np.where(data["NEW_CREDIT_TO_GOODS_RATIO"]<0,(( data["INSTAL_DBD_MAX"])-(data["PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN"])) ,(((((data["NEW_ANNUITY_TO_INCOME_RATIO"])-(data["NEW_EXT_SOURCES_MEAN"])))+(data["NEW_ANNUITY_TO_INCOME_RATIO"])) /2.0)))))))
 v["i66"] = 0.049995*np.tanh(((((((np.maximum(((data["EXT_SOURCE_3"])) ,(( data["NEW_EXT_SOURCES_MEAN"])))) +(((data["NEW_EXT_SOURCES_MEAN"])*(((data["NEW_EXT_SOURCES_MEAN"])+(data["INSTAL_PAYMENT_DIFF_MEAN"])))))))+(((data["NEW_EXT_SOURCES_MEAN"])+(data["INSTAL_PAYMENT_DIFF_MEAN"])))))* 2.0))
v["i67"] = 0.039797*np.tanh(((((np.maximum(((data["PREV_CHANNEL_TYPE_Contact_center_MEAN"])) ,(( np.where(data["PREV_DAYS_DECISION_MIN"]>0,(( np.maximum(((((np.maximum(((data["INSTAL_DPD_MEAN"])) ,(( data["BURO_CREDIT_TYPE_Microloan_MEAN"])))) * 2.0))),(( data["CC_AMT_RECIVABLE_VAR"])))) * 2.0), data["NEW_CREDIT_TO_ANNUITY_RATIO"])))))* 2.0)) * 2.0))
v["i68"] = 0.049954*np.tanh(((((((data["NAME_FAMILY_STATUS_Separated"])+(np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(((( np.where(data["INSTAL_COUNT"]>0, data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"], data["PREV_DAYS_DECISION_MEAN"])) -(((( data["INSTAL_DPD_MAX"])<(data["PREV_NAME_TYPE_SUITE_Other_B_MEAN"])) *1.))))))))) * 2.0)) * 2.0))
v["i69"] = 0.045722*np.tanh(((((np.where(data["ACTIVE_MONTHS_BALANCE_SIZE_SUM"]>0, -1.0,(( np.where(data["INSTAL_AMT_INSTALMENT_SUM"]>0, data["ACTIVE_AMT_CREDIT_SUM_SUM"],(-1.0*(( np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0, data["PREV_NAME_YIELD_GROUP_low_action_MEAN"], data["PREV_NAME_PRODUCT_TYPE_XNA_MEAN"])))))) * 2.0)))* 2.0)) * 2.0))
v["i70"] = 0.048797*np.tanh(((np.where(np.maximum(((data["APPROVED_AMT_CREDIT_MAX"])) ,(( data["AMT_ANNUITY"])))<0, data["AMT_ANNUITY"],(-1.0*(( data["PREV_AMT_ANNUITY_MEAN"])))))+(((data["POS_SK_DPD_DEF_MAX"])+(np.maximum(((data["ACTIVE_DAYS_CREDIT_MAX"])) ,(( data["POS_NAME_CONTRACT_STATUS_Returned_to_the_store_MEAN"]))))))))
v["i71"] = 0.047984*np.tanh(( -1.0*(( np.where(data["REGION_RATING_CLIENT_W_CITY"]>0, data["BURO_CREDIT_TYPE_Mortgage_MEAN"], np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"]>0, data["CC_AMT_PAYMENT_TOTAL_CURRENT_SUM"],(( np.maximum(((np.maximum(((data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"])) ,(( data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"]))))),(( data["CC_AMT_PAYMENT_TOTAL_CURRENT_SUM"])))) +(data["NEW_PHONE_TO_EMPLOY_RATIO"]))))))))
v["i72"] = 0.049980*np.tanh(np.where(data["EXT_SOURCE_3"] < -99998, data["REFUSED_AMT_APPLICATION_MAX"],(( np.maximum(((np.maximum(((((( data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])>(((data["OCCUPATION_TYPE_Core_staff"])/ 2.0)))*1.))) ,(((( data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])*(data["BURO_CREDIT_TYPE_Microloan_MEAN"]))))))),(( data["BURO_CREDIT_TYPE_Microloan_MEAN"])))) * 2.0)))
 v["i73"] = 0.049350*np.tanh(((((((((((data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])-(((( data["BURO_AMT_CREDIT_SUM_MEAN"])>(data["ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN"])) *1.))))-(data["BURO_AMT_CREDIT_SUM_MEAN"])))+(np.tanh(( data["BURO_AMT_CREDIT_SUM_DEBT_MEAN"])))))-(data["BURO_AMT_CREDIT_SUM_MEAN"])))-(data["BURO_AMT_CREDIT_SUM_MEAN"])))
 v["i74"] = 0.049056*np.tanh(np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0, np.where(data["PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN"]<0, data["DAYS_REGISTRATION"], data["NEW_CREDIT_TO_ANNUITY_RATIO"]), np.where(data["OWN_CAR_AGE"] < -99998,(((( data["REGION_POPULATION_RELATIVE"])-(data["PREV_NAME_CONTRACT_TYPE_Cash_loans_MEAN"])))-(data["INSTAL_AMT_PAYMENT_SUM"])) , -2.0)))
 v["i75"] = 0.041842*np.tanh(np.maximum(((data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])) ,(( np.where(data["PREV_NAME_PAYMENT_TYPE_XNA_MEAN"]<0, data["APPROVED_AMT_GOODS_PRICE_MAX"],(((((( data["PREV_CNT_PAYMENT_MEAN"])+(data["ORGANIZATION_TYPE_Construction"])))+(data["NEW_CREDIT_TO_GOODS_RATIO"])))+(((data["ORGANIZATION_TYPE_Transport__type_3"])+(data["PREV_PRODUCT_COMBINATION_Cash_Street__middle_MEAN"])))))))))
 v["i76"] = 0.049296*np.tanh(((((( data["NEW_EXT_SOURCES_MEAN"])>(data["NAME_INCOME_TYPE_State_servant"])) *1.))+(((((((data["OCCUPATION_TYPE_Drivers"])-(data["NEW_DOC_IND_AVG"])))-(data["INSTAL_AMT_INSTALMENT_SUM"])))+(np.where(data["PREV_AMT_CREDIT_MEAN"]<0, data["REGION_POPULATION_RELATIVE"], data["OBS_60_CNT_SOCIAL_CIRCLE"]))))))
v["i77"] = 0.048658*np.tanh(((((data["CC_AMT_RECEIVABLE_PRINCIPAL_MEAN"])-(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"])))+(np.where(data["POS_SK_DPD_MEAN"] < -99998, data["APPROVED_CNT_PAYMENT_SUM"],(((((( data["APPROVED_AMT_APPLICATION_MAX"])-(data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])))-(data["WEEKDAY_APPR_PROCESS_START_MONDAY"])))-(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"]))))))
v["i78"] = 0.049800*np.tanh(np.where(data["EXT_SOURCE_1"] < -99998,(((( data["DAYS_BIRTH"])* 2.0)) * 2.0),(((((( data["ORGANIZATION_TYPE_Medicine"])-(data["EXT_SOURCE_1"])))-(((((data["DAYS_BIRTH"])* 2.0)) * 2.0)))) -(data["EXT_SOURCE_1"]))))
v["i79"] = 0.048000*np.tanh(np.maximum(((data["CC_CNT_DRAWINGS_ATM_CURRENT_MAX"])) ,(( np.where(data["ACTIVE_MONTHS_BALANCE_SIZE_SUM"]<0,(( np.where(data["CLOSED_AMT_CREDIT_MAX_OVERDUE_MEAN"] < -99998,(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MAX"])-(data["CC_AMT_PAYMENT_CURRENT_SUM"])) , data["CC_NAME_CONTRACT_STATUS_Active_SUM"])) -(data["NEW_PHONE_TO_EMPLOY_RATIO"])) , data["EXT_SOURCE_2"])))))
 v["i80"] = 0.045128*np.tanh(np.where(data["APPROVED_AMT_APPLICATION_MAX"] < -99998, data["NEW_DOC_IND_AVG"], np.where(data["WEEKDAY_APPR_PROCESS_START_SATURDAY"]<0, np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0,(((data["POS_COUNT"])<(data["PREV_AMT_GOODS_PRICE_MAX"])) *1.) , np.maximum(((data["APPROVED_AMT_APPLICATION_MAX"])) ,(( data["APARTMENTS_MEDI"])))) , data["REFUSED_DAYS_DECISION_MAX"])))
 v["i81"] = 0.049278*np.tanh(((((((((( data["NEW_CREDIT_TO_ANNUITY_RATIO"])<(((( data["NAME_HOUSING_TYPE_Rented_apartment"])+(data["NEW_DOC_IND_KURT"])) /2.0)))*1.))+(np.minimum(((np.minimum(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])) ,(( data["REG_CITY_NOT_LIVE_CITY"]))))),(( data["REGION_RATING_CLIENT_W_CITY"])))))) * 2.0)) * 2.0))
v["i82"] = 0.049000*np.tanh(((data["BURO_CREDIT_TYPE_Credit_card_MEAN"])-(np.where(data["PREV_NAME_CONTRACT_STATUS_Approved_MEAN"]>0, data["ACTIVE_MONTHS_BALANCE_SIZE_MEAN"], np.maximum(((data["BURO_CREDIT_TYPE_Car_loan_MEAN"])) ,(((( data["BURO_CREDIT_TYPE_Credit_card_MEAN"])+(((np.maximum(((data["DAYS_BIRTH"])) ,(( data["ORGANIZATION_TYPE_School"])))) +(data["PREV_NAME_CONTRACT_STATUS_Approved_MEAN"])))))))))))
 v["i83"] = 0.049400*np.tanh(np.where(data["POS_SK_DPD_DEF_MAX"]>0, data["INSTAL_DPD_MAX"],(((( np.where(data["AMT_ANNUITY"]<0,(-1.0*(((((data["INSTAL_AMT_INSTALMENT_SUM"])>(np.maximum(((data["AMT_CREDIT"])) ,(( data["PREV_AMT_GOODS_PRICE_MAX"])))))*1.)))), data["NEW_CREDIT_TO_GOODS_RATIO"])) * 2.0)) * 2.0)))
 v["i84"] = 0.046006*np.tanh(np.where(data["OWN_CAR_AGE"]>0,(( data["PREV_AMT_CREDIT_MEAN"])-(data["DAYS_BIRTH"])) ,(( data["FLAG_WORK_PHONE"])*(((data["ORGANIZATION_TYPE_Kindergarten"])+(((data["WEEKDAY_APPR_PROCESS_START_SUNDAY"])+(np.maximum(((data["DAYS_BIRTH"])) ,(( data["NEW_ANNUITY_TO_INCOME_RATIO"])))))))))))
 v["i85"] = 0.009576*np.tanh(((((np.where(data["EXT_SOURCE_3"] < -99998, data["NEW_EXT_SOURCES_MEAN"], np.where(data["NEW_DOC_IND_KURT"]<0, data["ORGANIZATION_TYPE_Self_employed"],(( data["PREV_NAME_SELLER_INDUSTRY_XNA_MEAN"])*(np.where(data["CC_CNT_DRAWINGS_CURRENT_VAR"]<0, data["CLOSED_DAYS_CREDIT_MEAN"], data["CC_AMT_BALANCE_MEAN"])))))) * 2.0)) * 2.0))
v["i86"] = 0.042600*np.tanh(((((((( data["APPROVED_AMT_APPLICATION_MIN"])<(data["NEW_EXT_SOURCES_MEAN"])) *1.))-(((data["NEW_DOC_IND_AVG"])+(((data["NEW_EXT_SOURCES_MEAN"])-(np.tanh(((((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)) * 2.0)))))))))) -(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])))
 v["i87"] = 0.048500*np.tanh(((( -1.0*(( np.where(data["REFUSED_AMT_GOODS_PRICE_MAX"]>0, data["DAYS_LAST_PHONE_CHANGE"],(-1.0*(((((((( data["CC_CNT_DRAWINGS_CURRENT_MAX"])-(np.where(data["BURO_CREDIT_TYPE_Car_loan_MEAN"]<0, data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"], data["POS_MONTHS_BALANCE_MAX"])))) * 2.0)) * 2.0)))))))))* 2.0))
v["i88"] = 0.049749*np.tanh(np.where(((data["PREV_DAYS_DECISION_MEAN"])+(np.tanh(((((( data["PREV_NAME_PAYMENT_TYPE_XNA_MEAN"])+(data["PREV_NAME_PAYMENT_TYPE_XNA_MEAN"])))+(data["REFUSED_AMT_ANNUITY_MIN"])))))) <0, data["NEW_CREDIT_TO_ANNUITY_RATIO"],(-1.0*(((( data["REFUSED_AMT_ANNUITY_MIN"])+(data["NEW_CREDIT_TO_ANNUITY_RATIO"])))))))
 v["i89"] = 0.039400*np.tanh(np.where(data["INSTAL_DPD_MEAN"]<0, np.where(data["BURO_AMT_CREDIT_SUM_DEBT_SUM"]<0, np.where(data["ACTIVE_CREDIT_DAY_OVERDUE_MAX"] < -99998, data["PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN"], np.where(data["PREV_AMT_DOWN_PAYMENT_MEAN"]>0, data["INSTAL_AMT_PAYMENT_MIN"], data["PREV_CNT_PAYMENT_SUM"])) , data["ORGANIZATION_TYPE_Business_Entity_Type_3"]), data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"]))
v["i90"] = 0.045801*np.tanh(((((np.where(data["ACTIVE_AMT_CREDIT_SUM_SUM"]>0,(((( data["ACTIVE_AMT_CREDIT_SUM_SUM"])-(data["ACTIVE_AMT_CREDIT_SUM_MAX"])))* 2.0), np.where(data["DEF_60_CNT_SOCIAL_CIRCLE"]<0, data["POS_SK_DPD_DEF_MAX"],(-1.0*(( data["ACTIVE_AMT_CREDIT_SUM_MAX"])))))) +(data["NAME_EDUCATION_TYPE_Lower_secondary"])))* 2.0))
v["i91"] = 0.049800*np.tanh(((((data["PREV_CNT_PAYMENT_MEAN"])-(((( data["ACTIVE_DAYS_CREDIT_ENDDATE_MEAN"])<(np.tanh(( data["PREV_AMT_CREDIT_MEAN"])))) *1.))))*(((((data["PREV_DAYS_DECISION_MAX"])+(data["PREV_NAME_YIELD_GROUP_high_MEAN"])))+(((data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])+(data["OBS_60_CNT_SOCIAL_CIRCLE"])))))))
 v["i92"] = 0.049738*np.tanh(np.where(data["NEW_DOC_IND_STD"]>0, np.where(data["CLOSED_MONTHS_BALANCE_SIZE_SUM"] < -99998, data["INSTAL_PAYMENT_DIFF_MAX"],(((( np.where(data["BURO_STATUS_1_MEAN_MEAN"]<0, data["PREV_NAME_CONTRACT_TYPE_Revolving_loans_MEAN"], data["PREV_NAME_PRODUCT_TYPE_XNA_MEAN"])) -(data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])))-(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]))), data["INSTAL_PAYMENT_DIFF_MAX"]))
v["i93"] = 0.049512*np.tanh(((np.maximum(((data["ORGANIZATION_TYPE_Transport__type_3"])) ,(( data["INSTAL_AMT_PAYMENT_MIN"])))) +(((data["NAME_EDUCATION_TYPE_Lower_secondary"])+(((data["INSTAL_AMT_PAYMENT_MIN"])+(((data["ORGANIZATION_TYPE_Construction"])-(np.where(data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"]>0, data["INSTAL_DAYS_ENTRY_PAYMENT_SUM"], data["APPROVED_AMT_CREDIT_MIN"]))))))))))
v["i94"] = 0.047402*np.tanh(np.where(data["NEW_DOC_IND_STD"]<0, data["APPROVED_DAYS_DECISION_MIN"], np.maximum(((data["CC_CNT_DRAWINGS_CURRENT_VAR"])) ,(( np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( np.maximum(((((data["POS_SK_DPD_MAX"])/ 2.0))),(((((data["PREV_NAME_CASH_LOAN_PURPOSE_Medicine_MEAN"])<(((data["NEW_RATIO_PREV_DAYS_DECISION_MAX"])/ 2.0)))*1.))))))))))))
 v["i95"] = 0.044002*np.tanh(np.where(data["CC_AMT_PAYMENT_TOTAL_CURRENT_SUM"]>0, data["NEW_RATIO_BURO_MONTHS_BALANCE_MIN_MIN"],(( data["NAME_HOUSING_TYPE_Municipal_apartment"])+(np.maximum(((data["INSTAL_PAYMENT_DIFF_SUM"])) ,(( np.maximum(((data["ORGANIZATION_TYPE_Transport__type_3"])) ,(( np.where(data["REFUSED_AMT_CREDIT_MIN"] < -99998, data["NEW_SCORES_STD"], data["ACTIVE_AMT_CREDIT_SUM_SUM"])))))))))))
 v["i96"] = 0.050000*np.tanh(np.where(data["ACTIVE_DAYS_CREDIT_MEAN"]<0, np.where(data["BURO_DAYS_CREDIT_MAX"]>0, data["CC_AMT_BALANCE_VAR"], np.maximum(((data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])) ,(((((data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])<(data["NEW_EXT_SOURCES_MEAN"])) *1.))))) , np.where(data["BURO_AMT_CREDIT_SUM_DEBT_MEAN"]<0, data["POS_NAME_CONTRACT_STATUS_Signed_MEAN"], data["BURO_DAYS_CREDIT_MAX"])))
 v["i97"] = 0.048906*np.tanh(((((( np.maximum(((data["INSTAL_DBD_MAX"])) ,(( data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"])))) +(np.where(data["OBS_60_CNT_SOCIAL_CIRCLE"]<0, data["ORGANIZATION_TYPE_Transport__type_3"],(( data["ACTIVE_DAYS_CREDIT_ENDDATE_MAX"])+(data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"])))))/2.0)) -(((( data["NEW_RATIO_PREV_AMT_CREDIT_MIN"])>(data["PREV_PRODUCT_COMBINATION_POS_household_without_interest_MEAN"])) *1.))))
 v["i98"] = 0.049860*np.tanh(((np.where(data["CODE_GENDER"]<0, data["NEW_ANNUITY_TO_INCOME_RATIO"], np.where(((data["DAYS_BIRTH"])-(data["NEW_DOC_IND_AVG"])) <0, data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"], data["DAYS_ID_PUBLISH"])))-(((( data["DAYS_BIRTH"])+(((data["OCCUPATION_TYPE_Medicine_staff"])* 2.0)))/2.0))))
v["i99"] = 0.041003*np.tanh(np.maximum(((data["BURO_STATUS_1_MEAN_MEAN"])) ,(((( data["DAYS_REGISTRATION"])*(((((data["ACTIVE_DAYS_CREDIT_MIN"])-(data["INSTAL_AMT_INSTALMENT_MAX"])))*(np.where(((data["BURO_CREDIT_ACTIVE_Closed_MEAN"])+(data["DAYS_REGISTRATION"])) < -99998, data["APPROVED_AMT_CREDIT_MAX"], data["PREV_AMT_CREDIT_MIN"])))))))))
 v["i100"] = 0.049800*np.tanh(np.where(data["EXT_SOURCE_3"] < -99998, np.where(data["PREV_AMT_APPLICATION_MEAN"] < -99998, data["PREV_NAME_PAYMENT_TYPE_XNA_MEAN"],(( data["NEW_EXT_SOURCES_MEAN"])* 2.0)) ,(( data["EXT_SOURCE_3"])*(((((((data["INSTAL_PAYMENT_DIFF_MAX"])+(data["NEW_EXT_SOURCES_MEAN"])) /2.0)) +(data["INSTAL_PAYMENT_DIFF_MEAN"])) /2.0)))))
 v["i101"] = 0.039360*np.tanh(((((data["PREV_NAME_CASH_LOAN_PURPOSE_XNA_MEAN"])*(np.maximum(((((( data["BURO_CREDIT_TYPE_Credit_card_MEAN"])+(data["ORGANIZATION_TYPE_Military"])) /2.0))),(( np.where(((data["BURO_CREDIT_TYPE_Credit_card_MEAN"])*(data["PREV_CNT_PAYMENT_MEAN"])) < -99998, data["PREV_CNT_PAYMENT_SUM"], data["REFUSED_RATE_DOWN_PAYMENT_MAX"])))))))* 2.0))
v["i102"] = 0.047204*np.tanh(( -1.0*(( np.maximum(((((data["YEARS_BUILD_MEDI"])-(data["NEW_DOC_IND_AVG"])))) ,(((( data["OCCUPATION_TYPE_High_skill_tech_staff"])+(np.maximum(((data["CLOSED_AMT_CREDIT_SUM_MEAN"])) ,(( np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])) ,(((( data["PREV_CHANNEL_TYPE_Channel_of_corporate_sales_MEAN"])+(data["OCCUPATION_TYPE_High_skill_tech_staff"])))))))))))))))))
 v["i103"] = 0.049790*np.tanh(np.where(data["PREV_NAME_GOODS_CATEGORY_Audio_Video_MEAN"]>0, data["INSTAL_PAYMENT_DIFF_SUM"], np.where(data["ACTIVE_MONTHS_BALANCE_MAX_MAX"]>0, data["NEW_RATIO_BURO_AMT_ANNUITY_MAX"],(-1.0*(((( data["ORGANIZATION_TYPE_Medicine"])+(np.maximum(((data["BURO_CREDIT_TYPE_Mortgage_MEAN"])) ,(((((data["CC_AMT_PAYMENT_TOTAL_CURRENT_MAX"])>(data["BURO_AMT_CREDIT_SUM_DEBT_MAX"])) *1.))))))))))))
 v["i104"] = 0.049046*np.tanh(( -1.0*(((((data["ORGANIZATION_TYPE_School"])>(np.where(((( data["AMT_GOODS_PRICE"])+(data["NEW_CREDIT_TO_ANNUITY_RATIO"])) /2.0)>0, data["PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN"],(( data["NEW_CREDIT_TO_ANNUITY_RATIO"])+(((( data["NEW_CREDIT_TO_ANNUITY_RATIO"])<(np.tanh(( data["PREV_PRODUCT_COMBINATION_Cash_MEAN"])))) *1.))))))*1.)))))
v["i105"] = 0.049750*np.tanh(np.where(data["AMT_REQ_CREDIT_BUREAU_QRT"]>0, data["DAYS_LAST_PHONE_CHANGE"],(( np.where(data["INSTAL_DPD_MEAN"]>0, data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"], np.maximum(((((data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"])*(data["CC_AMT_BALANCE_MEAN"])))) ,(((( data["PREV_DAYS_DECISION_MAX"])-(data["APPROVED_APP_CREDIT_PERC_MIN"])))))))* 2.0)))
 v["i106"] = 0.045968*np.tanh(np.where(((data["DAYS_ID_PUBLISH"])-(data["DAYS_BIRTH"])) <0,(( data["DAYS_ID_PUBLISH"])*(((((( data["CNT_FAM_MEMBERS"])+(((data["INSTAL_DAYS_ENTRY_PAYMENT_SUM"])-(data["DAYS_BIRTH"])))) /2.0)) * 2.0))), data["DAYS_ID_PUBLISH"]))
v["i107"] = 0.049699*np.tanh(((np.maximum(((data["APPROVED_CNT_PAYMENT_SUM"])) ,(( np.where(data["NEW_CAR_TO_EMPLOY_RATIO"] < -99998, data["DAYS_BIRTH"], data["INSTAL_AMT_INSTALMENT_MEAN"])))))-(((data["INSTAL_AMT_PAYMENT_SUM"])+(((( np.minimum(((data["INSTAL_AMT_PAYMENT_SUM"])) ,(( data["PREV_AMT_CREDIT_MAX"])))) <(data["INSTAL_AMT_PAYMENT_SUM"])) *1.))))))
 v["i108"] = 0.007000*np.tanh(np.where(np.where(data["CLOSED_DAYS_CREDIT_ENDDATE_MAX"]<0, data["ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN"], data["BURO_AMT_CREDIT_SUM_SUM"])>0,(10.0),(( np.where(data["PREV_NAME_GOODS_CATEGORY_Photo___Cinema_Equipment_MEAN"]<0, np.where(data["NEW_RATIO_PREV_HOUR_APPR_PROCESS_START_MAX"]>0, data["WALLSMATERIAL_MODE_Stone__brick"], data["POS_SK_DPD_DEF_MAX"]), data["NEW_RATIO_PREV_HOUR_APPR_PROCESS_START_MEAN"])) * 2.0)))
 v["i109"] = 0.046106*np.tanh(np.where(data["ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN"]<0,(-1.0*(((((( data["NEW_LIVE_IND_SUM"])-(((np.maximum(((data["PREV_AMT_APPLICATION_MIN"])) ,(((( data["BURO_CREDIT_ACTIVE_Sold_MEAN"])+(data["BURO_CREDIT_TYPE_Microloan_MEAN"])))))) * 2.0)))) -(data["POS_SK_DPD_MAX"]))))), data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]))
v["i110"] = 0.042843*np.tanh(( -1.0*(( np.where(data["AMT_INCOME_TOTAL"]>0, data["BURO_AMT_CREDIT_SUM_MEAN"], np.where(data["CC_AMT_RECEIVABLE_PRINCIPAL_VAR"]>0, data["ACTIVE_DAYS_CREDIT_ENDDATE_MAX"], np.where(data["FLAG_WORK_PHONE"]>0, data["ACTIVE_DAYS_CREDIT_ENDDATE_MEAN"], np.where(data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"]>0, data["CC_AMT_DRAWINGS_POS_CURRENT_MAX"], data["NEW_CREDIT_TO_GOODS_RATIO"]))))))))
v["i111"] = 0.049018*np.tanh(np.where(data["BURO_STATUS_X_MEAN_MEAN"] < -99998,(( np.where(data["PREV_NAME_YIELD_GROUP_high_MEAN"]>0, data["FLAG_WORK_PHONE"], np.tanh(( data["LIVINGAREA_AVG"])))) -(((data["EXT_SOURCE_2"])+(data["NEW_INC_BY_ORG"])))) , np.tanh(((( data["EXT_SOURCE_2"])* 2.0)))))
 v["i112"] = 0.047002*np.tanh(np.where(data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"]<0, np.where(data["BURO_DAYS_CREDIT_MAX"]<0, np.where(data["NEW_RATIO_PREV_DAYS_DECISION_MIN"]<0, data["INSTAL_PAYMENT_DIFF_SUM"], data["BURO_DAYS_CREDIT_MAX"]), data["ACTIVE_AMT_CREDIT_SUM_SUM"]),(( data["ACTIVE_AMT_CREDIT_SUM_SUM"])+(( -1.0*(( data["NEW_RATIO_PREV_DAYS_DECISION_MIN"])))))))
 v["i113"] = 0.040984*np.tanh(np.where(data["REFUSED_DAYS_DECISION_MEAN"]>0,(( data["PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN"])* 2.0), np.maximum(((data["CC_AMT_INST_MIN_REGULARITY_VAR"])) ,(((( np.maximum(((data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"])) ,(( np.where(data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"]>0, data["INSTAL_PAYMENT_DIFF_SUM"], data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])))))* 2.0))))))
v["i114"] = 0.045032*np.tanh(((( np.maximum(((data["BURO_AMT_CREDIT_SUM_DEBT_MAX"])) ,(((( data["BURO_AMT_CREDIT_SUM_MAX"])* 2.0)))))>(np.maximum(((((((data["PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN"])+(((data["BURO_AMT_CREDIT_SUM_MEAN"])* 2.0)))) +(((data["BURO_AMT_CREDIT_SUM_DEBT_MAX"])* 2.0))))),(( data["BURO_AMT_CREDIT_SUM_MEAN"])))))*1.))
 v["i115"] = 0.045600*np.tanh(np.where(((( data["NEW_EXT_SOURCES_MEAN"])+(data["NEW_PHONE_TO_BIRTH_RATIO"])) /2.0)>0, data["APARTMENTS_MEDI"], np.where(((( data["POS_COUNT"])>(data["ORGANIZATION_TYPE_Business_Entity_Type_3"])) *1.) >0, data["NEW_EXT_SOURCES_MEAN"],(( data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"])*(data["NEW_PHONE_TO_BIRTH_RATIO"])))))
 v["i116"] = 0.042206*np.tanh(((data["DAYS_BIRTH"])*(((((((((((( data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])>(((data["DAYS_BIRTH"])/ 2.0)))*1.))+(data["PREV_CHANNEL_TYPE_Stone_MEAN"])))+(data["PREV_NAME_GOODS_CATEGORY_Consumer_Electronics_MEAN"])))+(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])))+(data["PREV_NAME_GOODS_CATEGORY_Consumer_Electronics_MEAN"])))))
 v["i117"] = 0.042064*np.tanh(np.where(data["INSTAL_AMT_INSTALMENT_MEAN"]>0, data["PREV_WEEKDAY_APPR_PROCESS_START_THURSDAY_MEAN"],(( data["PREV_CODE_REJECT_REASON_SCO_MEAN"])*(np.where(data["CC_AMT_DRAWINGS_POS_CURRENT_MAX"] < -99998, data["NEW_EMPLOY_TO_BIRTH_RATIO"], np.where(data["CLOSED_MONTHS_BALANCE_SIZE_MEAN"] < -99998, data["ACTIVE_CREDIT_DAY_OVERDUE_MAX"],(((data["NEW_SCORES_STD"])>(data["CLOSED_MONTHS_BALANCE_SIZE_MEAN"])) *1.)))))))
v["i118"] = 0.046301*np.tanh(np.where(data["AMT_INCOME_TOTAL"]>0, data["NEW_DOC_IND_KURT"], np.where(data["NAME_EDUCATION_TYPE_Higher_education"]>0, data["AMT_CREDIT"], np.minimum(((((((data["DAYS_BIRTH"])* 2.0)) * 2.0))),(((((((data["DAYS_BIRTH"])<(data["PREV_NAME_PORTFOLIO_POS_MEAN"])) *1.))* 2.0)))))))
 v["i119"] = 0.049880*np.tanh(np.where(((( -1.0)>(data["AMT_GOODS_PRICE"])) *1.) >0, data["CC_AMT_DRAWINGS_ATM_CURRENT_MAX"],(( np.where(data["ACTIVE_DAYS_CREDIT_ENDDATE_MEAN"]>0, data["INSTAL_AMT_PAYMENT_MAX"],(((((data["WEEKDAY_APPR_PROCESS_START_SUNDAY"])<(((data["NEW_EXT_SOURCES_MEAN"])/ 2.0)))*1.))* 2.0)))* 2.0)))
 v["i120"] = 0.048360*np.tanh(np.where(np.tanh(((((data["APPROVED_DAYS_DECISION_MAX"])+(data["PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN"])) /2.0)))<0, data["PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN"], np.where(data["BURO_AMT_CREDIT_SUM_DEBT_SUM"]>0,(((data["APPROVED_DAYS_DECISION_MAX"])+(data["REFUSED_APP_CREDIT_PERC_VAR"])) /2.0),(( data["PREV_PRODUCT_COMBINATION_Cash_MEAN"])-(data["CC_NAME_CONTRACT_STATUS_Active_VAR"])))))
 v["i121"] = 0.045502*np.tanh(np.where(np.where(((((data["INSTAL_DPD_MAX"])/ 2.0)) -(data["ORGANIZATION_TYPE_Transport__type_3"])) <0, data["NEW_EXT_SOURCES_MEAN"], data["APPROVED_RATE_DOWN_PAYMENT_MEAN"])<0,(((data["PREV_NAME_PORTFOLIO_Cash_MEAN"])<(((data["NEW_EXT_SOURCES_MEAN"])* 2.0)))*1.) , data["REGION_RATING_CLIENT"]))
v["i122"] = 0.048897*np.tanh(((( data["BURO_DAYS_CREDIT_MAX"])>(((np.minimum(((((data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])-(data["ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN"])))) ,(((( data["BURO_DAYS_CREDIT_MAX"])-(data["ORGANIZATION_TYPE_Transport__type_3"])))))) +(((( data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])<(((data["ORGANIZATION_TYPE_Transport__type_3"])* 2.0)))*1.))))) *1.))
 v["i123"] = 0.035168*np.tanh(np.where(data["DAYS_EMPLOYED"]>0, np.minimum(((data["REGION_RATING_CLIENT_W_CITY"])) ,(( data["REGION_POPULATION_RELATIVE"]))),(( np.where(data["HOUR_APPR_PROCESS_START"]<0, data["INSTAL_AMT_PAYMENT_MAX"], np.where(data["CC_AMT_PAYMENT_CURRENT_MEAN"]>0, data["DAYS_EMPLOYED"], data["NEW_DOC_IND_STD"])))* 2.0)))
 v["i124"] = 0.048513*np.tanh(np.where(data["INSTAL_DBD_MEAN"]<0, np.where(data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]<0, np.where(data["PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN"]>0,(( data["PREV_NAME_SELLER_INDUSTRY_Consumer_electronics_MEAN"])* 2.0),(((data["CC_AMT_RECIVABLE_VAR"])>(data["INSTAL_COUNT"])) *1.)), data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]),(-1.0*(( data["APPROVED_DAYS_DECISION_MIN"])))))
 v["i125"] = 0.004000*np.tanh(( -1.0*(( np.maximum(((np.maximum(((((((((data["ORGANIZATION_TYPE_Industry__type_9"])+(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])) /2.0)) +(data["AMT_GOODS_PRICE"])) /2.0))),(( np.where(data["PREV_AMT_GOODS_PRICE_MIN"]<0, data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"], data["PREV_WEEKDAY_APPR_PROCESS_START_SATURDAY_MEAN"])))))) ,(( data["NAME_HOUSING_TYPE_Office_apartment"])))))))
 v["i126"] = 0.028997*np.tanh(np.where(data["INSTAL_PAYMENT_DIFF_MAX"]>0, data["PREV_CODE_REJECT_REASON_XAP_MEAN"],(((( data["PREV_CODE_REJECT_REASON_HC_MEAN"])-(((( data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"])+(((( data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"])>(data["INSTAL_PAYMENT_DIFF_MAX"])) *1.))) /2.0)))) -(((( data["PREV_CHANNEL_TYPE_Channel_of_corporate_sales_MEAN"])+(data["NAME_FAMILY_STATUS_Married"])) /2.0)))))
 v["i127"] = 0.002994*np.tanh(( -1.0*(((((((data["PREV_CHANNEL_TYPE_Regional___Local_MEAN"])>(((((( data["PREV_CHANNEL_TYPE_Regional___Local_MEAN"])>(((((-1.0*(( data["WEEKDAY_APPR_PROCESS_START_MONDAY"])))) >(((( data["REFUSED_CNT_PAYMENT_MEAN"])>(data["WEEKDAY_APPR_PROCESS_START_MONDAY"])) *1.))) *1.))) *1.))-(data["AMT_GOODS_PRICE"])))) *1.))* 2.0)))))
 v["i128"] = 0.005997*np.tanh(np.where(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"]>0, data["POS_NAME_CONTRACT_STATUS_Active_MEAN"], np.where(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"]<0, data["NEW_DOC_IND_KURT"],(((((( data["REFUSED_DAYS_DECISION_MAX"])>(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])) *1.))>(np.where(data["NAME_CONTRACT_TYPE_Cash_loans"]>0, data["NAME_EDUCATION_TYPE_Secondary___secondary_special"], data["APPROVED_AMT_CREDIT_MEAN"])))*1.))))
 v["i129"] = 0.046680*np.tanh(np.where(data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]>0,(14.66528892517089844),(( data["NEW_DOC_IND_KURT"])*(np.where(data["NAME_HOUSING_TYPE_Office_apartment"]>0, -3.0,(((( data["CLOSED_DAYS_CREDIT_MIN"])*(data["POS_COUNT"])))-(data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"])))))))
 v["i130"] = 0.019768*np.tanh(np.where(data["APPROVED_APP_CREDIT_PERC_VAR"] < -99998, data["PREV_WEEKDAY_APPR_PROCESS_START_FRIDAY_MEAN"], np.where(data["PREV_NAME_GOODS_CATEGORY_Photo___Cinema_Equipment_MEAN"]<0, np.where(data["APARTMENTS_MEDI"] < -99998, data["APPROVED_CNT_PAYMENT_MEAN"], np.where(np.tanh(( data["PREV_WEEKDAY_APPR_PROCESS_START_FRIDAY_MEAN"])) <0, data["NAME_CONTRACT_TYPE_Cash_loans"], data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"])) , data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])))
 v["i131"] = 0.006600*np.tanh(((data["ACTIVE_AMT_CREDIT_SUM_SUM"])*(np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]>0, data["CC_MONTHS_BALANCE_VAR"], np.where(data["CC_AMT_RECEIVABLE_PRINCIPAL_VAR"]>0, data["REFUSED_AMT_CREDIT_MIN"],(((((data["PREV_WEEKDAY_APPR_PROCESS_START_FRIDAY_MEAN"])<(np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_SUM"])) ,(( data["REFUSED_AMT_CREDIT_MIN"])))))*1.))* 2.0))))))
v["i132"] = 0.049586*np.tanh(( -1.0*(( np.where(data["INSTAL_AMT_PAYMENT_MAX"] < -99998, data["YEARS_BEGINEXPLUATATION_MEDI"], np.where(data["INSTAL_AMT_PAYMENT_MAX"]<0, np.maximum(((np.maximum(((data["AMT_CREDIT"])) ,(((((data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"])>(data["CC_AMT_RECIVABLE_VAR"])) *1.)))))),(( data["EXT_SOURCE_2"]))), data["DEF_60_CNT_SOCIAL_CIRCLE"]))))))
v["i133"] = 0.049999*np.tanh(np.where(data["PREV_NAME_CLIENT_TYPE_Refreshed_MEAN"]>0, data["BURO_CREDIT_TYPE_Credit_card_MEAN"],(((( np.where(( -1.0*(((((data["ACTIVE_DAYS_CREDIT_ENDDATE_MAX"])>(data["AMT_REQ_CREDIT_BUREAU_QRT"])) *1.))))<0, data["BURO_DAYS_CREDIT_MAX"],(( data["PREV_NAME_GOODS_CATEGORY_Computers_MEAN"])*(data["BURO_DAYS_CREDIT_MAX"])))) * 2.0)) * 2.0)))
 v["i134"] = 0.047500*np.tanh(np.where(data["REFUSED_HOUR_APPR_PROCESS_START_MEAN"]<0, np.where(data["ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN"]>0, 3.0, np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0, data["FLAG_WORK_PHONE"],(-1.0*(( data["FLAG_WORK_PHONE"]))))), np.where(data["ACTIVE_MONTHS_BALANCE_MAX_MAX"]<0, data["PREV_CODE_REJECT_REASON_HC_MEAN"], data["INSTAL_AMT_PAYMENT_MIN"])))
 v["i135"] = 0.046005*np.tanh(np.where(data["NEW_DOC_IND_STD"]<0, data["INSTAL_PAYMENT_DIFF_SUM"], np.maximum(((np.maximum(((((data["BURO_STATUS_1_MEAN_MEAN"])*(( -1.0*(( data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"]))))))),(( data["CC_CNT_DRAWINGS_CURRENT_MEAN"]))))),(( np.where(data["REGION_RATING_CLIENT_W_CITY"]<0, data["CLOSED_AMT_CREDIT_SUM_DEBT_SUM"], data["NEW_DOC_IND_STD"]))))))
v["i136"] = 0.045999*np.tanh(np.where(data["CC_AMT_BALANCE_MAX"]<0, np.where(data["POS_SK_DPD_DEF_MEAN"]>0, data["NEW_ANNUITY_TO_INCOME_RATIO"], np.where(data["BURO_CREDIT_TYPE_Car_loan_MEAN"] < -99998,(-1.0*(( data["NEW_CREDIT_TO_INCOME_RATIO"]))),(( data["DEF_30_CNT_SOCIAL_CIRCLE"])*(( -1.0*(( data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__middle_MEAN"]))))))), data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"]))
v["i137"] = 0.049992*np.tanh(((((data["NEW_ANNUITY_TO_INCOME_RATIO"])*(np.tanh(( np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"])) ,(( data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))))))) -(np.maximum(((np.maximum(((data["CC_AMT_PAYMENT_TOTAL_CURRENT_SUM"])) ,(( data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"]))))),(( np.maximum(((data["NEW_SOURCES_PROD"])) ,(( data["INSTAL_AMT_PAYMENT_SUM"])))))))))
 v["i138"] = 0.050000*np.tanh(((((( data["NEW_RATIO_PREV_DAYS_DECISION_MAX"])>(data["POS_SK_DPD_MEAN"])) *1.))+(((((data["CC_AMT_DRAWINGS_ATM_CURRENT_SUM"])-(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"])))+(((( data["APPROVED_APP_CREDIT_PERC_MIN"])<(np.where(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"]<0, data["POS_SK_DPD_MEAN"], data["APPROVED_APP_CREDIT_PERC_MIN"])))*1.))))))
 v["i139"] = 0.036994*np.tanh(((data["AMT_REQ_CREDIT_BUREAU_QRT"])*(((((data["ORGANIZATION_TYPE_Military"])* 2.0)) -(((( data["AMT_CREDIT"])+(np.maximum(((data["AMT_INCOME_TOTAL"])) ,(((( np.maximum(((((data["INSTAL_DPD_MEAN"])* 2.0))),(( data["CC_AMT_RECIVABLE_VAR"])))) * 2.0)))))) /2.0))))))
v["i140"] = 0.039200*np.tanh(np.where(data["INSTAL_AMT_PAYMENT_MIN"]>0, np.where(data["PREV_RATE_DOWN_PAYMENT_MAX"]<0, data["PREV_CODE_REJECT_REASON_LIMIT_MEAN"], data["INSTAL_AMT_PAYMENT_MIN"]),(( data["NEW_RATIO_BURO_AMT_CREDIT_SUM_LIMIT_MEAN"])*(np.where(data["LIVINGAPARTMENTS_AVG"]>0, data["ACTIVE_DAYS_CREDIT_UPDATE_MEAN"],(((data["PREV_AMT_ANNUITY_MEAN"])>(data["APPROVED_CNT_PAYMENT_MEAN"])) *1.))))))
 v["i141"] = 0.044997*np.tanh(np.where(data["PREV_AMT_ANNUITY_MIN"] < -99998, data["NEW_DOC_IND_AVG"],(( data["PREV_NAME_YIELD_GROUP_high_MEAN"])*(((((((data["INSTAL_DBD_SUM"])* 2.0)) * 2.0)) *(((( data["APPROVED_DAYS_DECISION_MEAN"])<(np.maximum(((data["AMT_INCOME_TOTAL"])) ,(( data["INSTAL_DBD_SUM"])))))*1.)))))))
v["i142"] = 0.044000*np.tanh(((data["DAYS_ID_PUBLISH"])*(np.where(data["POS_MONTHS_BALANCE_MEAN"]<0, data["NEW_RATIO_PREV_DAYS_DECISION_MIN"], np.where(data["INSTAL_AMT_PAYMENT_MIN"]<0, data["CC_AMT_BALANCE_VAR"],(( data["FLOORSMIN_MODE"])*(data["NEW_RATIO_PREV_APP_CREDIT_PERC_MIN"])))))))
 v["i143"] = 0.017800*np.tanh(np.where(data["LANDAREA_MEDI"]>0, data["CC_MONTHS_BALANCE_VAR"],(((data["DAYS_ID_PUBLISH"])+(np.where(data["REFUSED_CNT_PAYMENT_MEAN"]<0,(-1.0*(( np.maximum(((data["ORGANIZATION_TYPE_Industry__type_9"])) ,(( data["ORGANIZATION_TYPE_School"])))))) ,(-1.0*(( data["BASEMENTAREA_MODE"])))))) /2.0)))
 v["i144"] = 0.049495*np.tanh(((((((((( data["DAYS_BIRTH"])*(data["NAME_FAMILY_STATUS_Married"])))+(((((np.minimum(((((data["DAYS_BIRTH"])*(data["CODE_GENDER"])))) ,(((-1.0*(( data["BURO_CREDIT_TYPE_Car_loan_MEAN"])))))))* 2.0)) * 2.0)))/2.0)) * 2.0)) * 2.0))
v["i145"] = 0.047997*np.tanh(((np.maximum(((data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"])) ,(((((data["NAME_FAMILY_STATUS_Married"])<(np.maximum(((data["ACTIVE_DAYS_CREDIT_MAX"])) ,(( np.where(data["APPROVED_AMT_APPLICATION_MAX"]>0, np.where(data["ACTIVE_DAYS_CREDIT_MAX"]>0, data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"], data["APPROVED_AMT_APPLICATION_MAX"]), data["CLOSED_AMT_CREDIT_SUM_DEBT_MAX"])))))) *1.))))) * 2.0))
v["i146"] = 0.007402*np.tanh(((( -1.0*(( np.where(data["ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN"]>0, data["NEW_RATIO_PREV_RATE_DOWN_PAYMENT_MEAN"], np.where(data["ACTIVE_AMT_CREDIT_SUM_SUM"]>0, np.maximum(((data["ACTIVE_MONTHS_BALANCE_SIZE_MEAN"])) ,(( data["LIVE_CITY_NOT_WORK_CITY"]))),(((data["CLOSED_AMT_CREDIT_SUM_MEAN"])>(np.tanh(( data["DEF_60_CNT_SOCIAL_CIRCLE"])))) *1.))))))) * 2.0))
v["i147"] = 0.034997*np.tanh(np.where(data["INSTAL_AMT_PAYMENT_MIN"]<0,(( data["CODE_GENDER"])*(data["PREV_CNT_PAYMENT_MEAN"])) , np.where(data["CODE_GENDER"]<0,(( data["BURO_DAYS_CREDIT_MEAN"])*(data["PREV_CNT_PAYMENT_MEAN"])) , data["BURO_DAYS_CREDIT_MEAN"])))
 v["i148"] = 0.047001*np.tanh(((data["OBS_30_CNT_SOCIAL_CIRCLE"])*(((((np.maximum(((np.where(data["REGION_RATING_CLIENT"]<0,(( np.maximum(((data["ORGANIZATION_TYPE_School"])) ,(( data["REFUSED_CNT_PAYMENT_SUM"])))) * 2.0),(-1.0*(( data["APPROVED_AMT_GOODS_PRICE_MEAN"])))))) ,(( data["INSTAL_AMT_INSTALMENT_SUM"])))) * 2.0)) * 2.0))))
v["i149"] = 0.047705*np.tanh(np.where(data["REGION_POPULATION_RELATIVE"]>0, np.where(data["NEW_INC_BY_ORG"]>0,(( data["NEW_DOC_IND_KURT"])-(data["WEEKDAY_APPR_PROCESS_START_SUNDAY"])) , data["WEEKDAY_APPR_PROCESS_START_WEDNESDAY"]),(((((( data["PREV_PRODUCT_COMBINATION_POS_household_without_interest_MEAN"])* 2.0)) * 2.0)) *(data["NEW_DOC_IND_STD"]))))
return Output(v.sum(axis=1)-2.432490)
 def GP2(data):
v = pd.DataFrame()
v["i0"] = 0.040171*np.tanh(((((((np.where(data["EXT_SOURCE_3"] < -99998, np.tanh(( data["DAYS_EMPLOYED"])) ,(((-1.0*(( data["EXT_SOURCE_3"])))) +(np.minimum(((data["DAYS_BIRTH"])) ,(( data["NEW_CREDIT_TO_GOODS_RATIO"])))))))-(data["EXT_SOURCE_2"])))* 2.0)) * 2.0))
v["i1"] = 0.049975*np.tanh(((((((((((-1.0)-(np.where(data["CC_AMT_PAYMENT_CURRENT_VAR"] < -99998,(( data["NEW_EXT_SOURCES_MEAN"])* 2.0),(((((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)) * 2.0)) * 2.0)))))* 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i2"] = 0.040171*np.tanh(((((((((((np.where(data["EXT_SOURCE_2"]>0, data["NEW_RATIO_PREV_DAYS_DECISION_MAX"],(( -2.0)/ 2.0)))-(data["EXT_SOURCE_3"])))-(data["EXT_SOURCE_2"])))* 2.0)) * 2.0)) * 2.0))
v["i3"] = 0.040171*np.tanh(((((((( data["OCCUPATION_TYPE_Laborers"])+(data["NEW_CREDIT_TO_GOODS_RATIO"])) /2.0)) +(((((((data["NEW_EXT_SOURCES_MEAN"])*(-2.0)))+(np.minimum(((data["NEW_CREDIT_TO_GOODS_RATIO"])) ,(((-1.0*(( data["EXT_SOURCE_3"])))))))))* 2.0)))) * 2.0))
v["i4"] = 0.047400*np.tanh(((((data["NEW_EXT_SOURCES_MEAN"])* 2.0)) *(np.minimum(((np.minimum(((data["NEW_RATIO_BURO_DAYS_CREDIT_ENDDATE_MAX"])) ,(((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)))))) ,(((((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)) *(np.minimum(((data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"])) ,(( data["NEW_RATIO_PREV_AMT_DOWN_PAYMENT_MAX"])))))))))))
 v["i5"] = 0.040181*np.tanh(((((data["DAYS_BIRTH"])+(((( -1.0*(( np.maximum(((data["NAME_EDUCATION_TYPE_Higher_education"])) ,(( data["EXT_SOURCE_3"])))))))-(((data["EXT_SOURCE_2"])+(np.where(data["EXT_SOURCE_3"] < -99998, data["EXT_SOURCE_2"], data["EXT_SOURCE_3"])))))))) * 2.0))
v["i6"] = 0.047400*np.tanh(((((data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])-(((((((((((data["NEW_EXT_SOURCES_MEAN"])* 2.0)) -(np.tanh(( data["CC_AMT_DRAWINGS_POS_CURRENT_MAX"])))))-(data["NEW_CREDIT_TO_GOODS_RATIO"])))-(np.tanh(( data["DAYS_EMPLOYED"])))))* 2.0)))) * 2.0))
v["i7"] = 0.040171*np.tanh(((((np.tanh(( data["DAYS_EMPLOYED"])))+(((((np.where(data["AMT_ANNUITY"]<0, data["AMT_ANNUITY"], np.tanh(( np.tanh(( data["NEW_CREDIT_TO_GOODS_RATIO"])))))) +(( -1.0*(( data["NEW_EXT_SOURCES_MEAN"])))))) * 2.0)))) * 2.0))
v["i8"] = 0.049660*np.tanh(((((((((( -1.0*(((((((data["DAYS_EMPLOYED"])<(((((data["PREV_AMT_DOWN_PAYMENT_MAX"])* 2.0)) -(data["NEW_CREDIT_TO_GOODS_RATIO"])))) *1.))+(data["NEW_EXT_SOURCES_MEAN"])))))) * 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i9"] = 0.049975*np.tanh(((((data["NEW_CREDIT_TO_GOODS_RATIO"])+(((((np.tanh(((( data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])+(((np.tanh(((((data["DAYS_EMPLOYED"])+(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])) /2.0)))) * 2.0)))))) -(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)))) * 2.0))
v["i10"] = 0.048200*np.tanh(((( -1.0*(((((( data["NEW_EXT_SOURCES_MEAN"])+(((np.where(data["REFUSED_DAYS_DECISION_MAX"]<0, data["NEW_EXT_SOURCES_MEAN"], data["EXT_SOURCE_3"])) +(data["CODE_GENDER"])))))+(np.maximum(((data["APPROVED_APP_CREDIT_PERC_MAX"])) ,(( data["NAME_EDUCATION_TYPE_Higher_education"])))))))))* 2.0))
v["i11"] = 0.050000*np.tanh(((((((data["PREV_NAME_CLIENT_TYPE_New_MEAN"])+(data["PREV_CNT_PAYMENT_MEAN"])))+(data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))+(((data["NEW_CREDIT_TO_GOODS_RATIO"])-(((np.maximum(((data["NEW_EXT_SOURCES_MEAN"])) ,(((( data["NEW_EXT_SOURCES_MEAN"])+(data["BURO_CREDIT_ACTIVE_Closed_MEAN"])))))) * 2.0))))))
v["i12"] = 0.049996*np.tanh(((data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])+(((data["NEW_EXT_SOURCES_MEAN"])*(np.minimum(((data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])) ,(( np.minimum(((((data["CC_CNT_INSTALMENT_MATURE_CUM_SUM"])+(np.minimum(((data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])) ,(((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)))))))) ,(( data["NEW_EXT_SOURCES_MEAN"])))))))))))
 v["i13"] = 0.049540*np.tanh(((((((np.maximum(((data["DAYS_EMPLOYED"])) ,(( data["APPROVED_APP_CREDIT_PERC_MAX"])))) -(data["NEW_EXT_SOURCES_MEAN"])))+(((( -1.0*(((( data["APPROVED_APP_CREDIT_PERC_MAX"])-(data["PREV_NAME_PRODUCT_TYPE_walk_in_MEAN"])))))) +(np.tanh(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])))))))* 2.0))
v["i14"] = 0.049550*np.tanh(((np.tanh(( data["PREV_CNT_PAYMENT_MEAN"])))+(((((data["NEW_CREDIT_TO_GOODS_RATIO"])-(data["CODE_GENDER"])))-(((((data["NEW_EXT_SOURCES_MEAN"])* 2.0)) +(np.maximum(((data["NAME_EDUCATION_TYPE_Higher_education"])) ,(( data["NEW_CREDIT_TO_ANNUITY_RATIO"]))))))))))
v["i15"] = 0.048505*np.tanh(((( -1.0*(((( data["NEW_EXT_SOURCES_MEAN"])-(((((np.tanh(( data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))+(np.maximum(((data["DAYS_EMPLOYED"])) ,(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])))))) +(((data["INSTAL_PAYMENT_DIFF_MAX"])-(data["APPROVED_AMT_ANNUITY_MAX"])))))))))) * 2.0))
v["i16"] = 0.049643*np.tanh(((data["FLAG_DOCUMENT_3"])+(( -1.0*(((( data["CODE_GENDER"])+(((((((data["NEW_EXT_SOURCES_MEAN"])+(((( data["CC_CNT_DRAWINGS_ATM_CURRENT_SUM"])<(data["NEW_CAR_TO_EMPLOY_RATIO"])) *1.))))* 2.0)) +(data["BURO_CREDIT_ACTIVE_Closed_MEAN"]))))))))))
v["i17"] = 0.049376*np.tanh(((((((data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])+(np.where(data["NEW_DOC_IND_KURT"]>0, data["DAYS_EMPLOYED"], data["NEW_DOC_IND_KURT"])))) -(((((( data["NEW_EXT_SOURCES_MEAN"])+(((data["APPROVED_RATE_DOWN_PAYMENT_MAX"])-(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])))) /2.0)) * 2.0)))) * 2.0))
v["i18"] = 0.049800*np.tanh(((((((((data["PREV_CNT_PAYMENT_MEAN"])-(((( data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])<(data["EXT_SOURCE_2"])) *1.))))-(np.where(data["DAYS_EMPLOYED"]>0, data["EXT_SOURCE_1"],(((data["PREV_DAYS_DECISION_MIN"])<(data["EXT_SOURCE_3"])) *1.))))) * 2.0)) * 2.0))
v["i19"] = 0.049904*np.tanh(((((data["NEW_DOC_IND_KURT"])-(((data["NEW_EXT_SOURCES_MEAN"])-(np.where(data["INSTAL_AMT_PAYMENT_MIN"]<0, data["PREV_DAYS_DECISION_MIN"], data["NEW_RATIO_PREV_DAYS_DECISION_MAX"])))))) -(((data["POS_COUNT"])-(((data["APPROVED_CNT_PAYMENT_MEAN"])-(data["INSTAL_AMT_PAYMENT_MIN"])))))))
 v["i20"] = 0.049787*np.tanh(((((((((((((data["NEW_ANNUITY_TO_INCOME_RATIO"])-(data["CODE_GENDER"])))+(((data["NEW_CREDIT_TO_GOODS_RATIO"])+(data["PREV_NAME_YIELD_GROUP_high_MEAN"])))))-(data["PREV_CODE_REJECT_REASON_XAP_MEAN"])))+(data["DEF_30_CNT_SOCIAL_CIRCLE"])))-(data["NEW_EXT_SOURCES_MEAN"])))* 2.0))
v["i21"] = 0.049800*np.tanh(((np.where(data["PREV_AMT_DOWN_PAYMENT_MAX"]>0, data["CC_AMT_BALANCE_MEAN"],(-1.0*(((((((( data["EXT_SOURCE_3"])* 2.0)) -(np.minimum(((( -1.0*(( data["CODE_GENDER"]))))),(( data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])))))) -(data["REGION_RATING_CLIENT"])))))))* 2.0))
v["i22"] = 0.049000*np.tanh(((((((data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])+(((data["PREV_NAME_CLIENT_TYPE_New_MEAN"])* 2.0)))) +(((((data["INSTAL_PAYMENT_DIFF_MAX"])-(data["NEW_EXT_SOURCES_MEAN"])))+(data["INSTAL_PAYMENT_DIFF_MEAN"])))))+(((data["PREV_CNT_PAYMENT_MEAN"])+(data["PREV_NAME_YIELD_GROUP_XNA_MEAN"])))))
 v["i23"] = 0.049944*np.tanh(((((np.where(((data["EXT_SOURCE_3"])-(data["NAME_INCOME_TYPE_Working"])) >0, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"], np.where(data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"]>0, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"], np.where(data["POS_MONTHS_BALANCE_SIZE"]>0, data["REFUSED_CNT_PAYMENT_SUM"], data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])))) * 2.0)) * 2.0))
v["i24"] = 0.050000*np.tanh(((((((np.where(data["INSTAL_DPD_MEAN"]<0,(((data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])+(((data["CC_CNT_INSTALMENT_MATURE_CUM_SUM"])*(data["NEW_EXT_SOURCES_MEAN"])))) /2.0),(-1.0*(( data["NEW_SOURCES_PROD"])))))* 2.0)) * 2.0)) * 2.0))
v["i25"] = 0.048001*np.tanh(((((((np.where(data["NEW_CAR_TO_BIRTH_RATIO"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"], np.maximum(((data["BURO_CREDIT_ACTIVE_Active_MEAN"])) ,(( np.maximum(((data["REG_CITY_NOT_LIVE_CITY"])) ,(( np.where(data["INSTAL_DPD_MEAN"]>0, data["INSTAL_DPD_MEAN"], data["CC_CNT_DRAWINGS_ATM_CURRENT_VAR"])))))))))* 2.0)) * 2.0)) * 2.0))
v["i26"] = 0.049758*np.tanh(((data["REGION_RATING_CLIENT_W_CITY"])+(((data["NEW_CREDIT_TO_GOODS_RATIO"])+(((((((((data["PREV_CNT_PAYMENT_SUM"])-(np.maximum(((data["POS_COUNT"])) ,(( data["EXT_SOURCE_1"])))))) * 2.0)) * 2.0)) +(data["NAME_INCOME_TYPE_Working"])))))))
 v["i27"] = 0.049720*np.tanh(((((data["NEW_CREDIT_TO_GOODS_RATIO"])-(data["CODE_GENDER"])))-(((3.0)*(((data["PREV_AMT_ANNUITY_MIN"])+(((((data["POS_MONTHS_BALANCE_SIZE"])-(data["INSTAL_PAYMENT_DIFF_MAX"])))-(data["INSTAL_PAYMENT_DIFF_MAX"])))))))))
 v["i28"] = 0.049994*np.tanh(((data["FLAG_WORK_PHONE"])+(((((((data["DEF_30_CNT_SOCIAL_CIRCLE"])+(np.maximum(((data["PREV_CNT_PAYMENT_MEAN"])) ,(( data["NEW_CREDIT_TO_GOODS_RATIO"])))))) +(np.where(data["CC_AMT_BALANCE_SUM"] < -99998, data["NEW_ANNUITY_TO_INCOME_RATIO"], data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"])))) * 2.0))))
v["i29"] = 0.049830*np.tanh(np.where(data["EXT_SOURCE_1"] < -99998,(( data["DAYS_BIRTH"])-(np.where(data["CODE_GENDER"]<0, data["NEW_CAR_TO_EMPLOY_RATIO"], data["PREV_NAME_YIELD_GROUP_low_action_MEAN"]))),(((( data["NEW_ANNUITY_TO_INCOME_RATIO"])-(data["NEW_EXT_SOURCES_MEAN"])))-(data["DAYS_BIRTH"]))))
v["i30"] = 0.048802*np.tanh(((((data["INSTAL_PAYMENT_DIFF_MAX"])-(((data["INSTAL_DBD_SUM"])-(data["REGION_RATING_CLIENT_W_CITY"])))))-(((np.maximum(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])) ,(( data["PREV_RATE_DOWN_PAYMENT_MAX"])))) -(((((data["INSTAL_AMT_INSTALMENT_MAX"])-(data["APPROVED_AMT_ANNUITY_MEAN"])))* 2.0))))))
v["i31"] = 0.049376*np.tanh(((((((data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])-(data["CC_MONTHS_BALANCE_VAR"])))+(((((data["INSTAL_PAYMENT_DIFF_MAX"])-(data["APPROVED_AMT_ANNUITY_MEAN"])))+(((data["AMT_ANNUITY"])-(np.maximum(((data["PREV_NAME_YIELD_GROUP_low_action_MEAN"])) ,(( data["NAME_INCOME_TYPE_State_servant"])))))))))) * 2.0))
v["i32"] = 0.049495*np.tanh(np.where(data["NEW_CAR_TO_EMPLOY_RATIO"]>0,(( data["REFUSED_DAYS_DECISION_MIN"])* 2.0),(((((( np.maximum(((data["DEF_60_CNT_SOCIAL_CIRCLE"])) ,(( np.maximum(((data["APPROVED_CNT_PAYMENT_MEAN"])) ,(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])))))))+(( -1.0*(( data["CODE_GENDER"])))))) * 2.0)) * 2.0)))
 v["i33"] = 0.046499*np.tanh(((((((((((np.maximum(((data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])) ,(((-1.0*(( np.maximum(((((( data["NEW_EXT_SOURCES_MEAN"])+(((data["NAME_FAMILY_STATUS_Married"])* 2.0)))/2.0))),(( data["FLOORSMAX_MEDI"])))))))))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i34"] = 0.049768*np.tanh(((np.where(np.maximum(((data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"])) ,(( data["PREV_NAME_YIELD_GROUP_low_action_MEAN"])))>0, data["CC_CNT_DRAWINGS_POS_CURRENT_MEAN"],(((( data["NEW_ANNUITY_TO_INCOME_RATIO"])-(((data["INSTAL_DBD_SUM"])* 2.0)))) +(((data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])-(data["PREV_PRODUCT_COMBINATION_POS_industry_with_interest_MEAN"])))))) * 2.0))
v["i35"] = 0.049610*np.tanh(np.where(data["NEW_CAR_TO_BIRTH_RATIO"]>0, data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"],(((( data["PREV_CNT_PAYMENT_MEAN"])-(data["OCCUPATION_TYPE_Core_staff"])))-(((data["PREV_AMT_ANNUITY_MEAN"])-(((data["DAYS_ID_PUBLISH"])-(((data["PREV_NAME_YIELD_GROUP_low_normal_MEAN"])-(data["PREV_NAME_YIELD_GROUP_high_MEAN"]))))))))))
v["i36"] = 0.048788*np.tanh(((((((((data["APPROVED_CNT_PAYMENT_MEAN"])-(((data["POS_MONTHS_BALANCE_MAX"])-(data["PREV_DAYS_DECISION_MEAN"])))))-(data["APPROVED_AMT_ANNUITY_MEAN"])))-(((data["INSTAL_AMT_PAYMENT_MIN"])-(np.minimum(((data["AMT_ANNUITY"])) ,(( data["NEW_DOC_IND_KURT"])))))))) * 2.0))
v["i37"] = 0.049586*np.tanh(((((np.where(np.maximum(((np.maximum(((np.maximum(((data["INSTAL_DPD_MEAN"])) ,(( np.maximum(((data["POS_NAME_CONTRACT_STATUS_Returned_to_the_store_MEAN"])) ,(( data["REG_CITY_NOT_LIVE_CITY"])))))))) ,(( data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"]))))),(( data["BURO_AMT_CREDIT_SUM_DEBT_MAX"])))<0, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"],(9.0)))* 2.0)) * 2.0))
v["i38"] = 0.049900*np.tanh(((((((((np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]>0,(-1.0*(( data["NEW_CREDIT_TO_ANNUITY_RATIO"]))),(( data["NEW_CREDIT_TO_ANNUITY_RATIO"])+(((data["FLAG_DOCUMENT_3"])* 2.0)))))* 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i39"] = 0.049965*np.tanh(((((data["DAYS_LAST_PHONE_CHANGE"])-(np.maximum(((((data["CODE_GENDER"])-(data["DAYS_REGISTRATION"])))) ,(( data["APPROVED_HOUR_APPR_PROCESS_START_MAX"])))))) -(np.where(data["POS_SK_DPD_DEF_MAX"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_LIMIT_MEAN"], np.maximum(((data["NAME_INCOME_TYPE_State_servant"])) ,(( data["BURO_CREDIT_TYPE_Mortgage_MEAN"])))))))
 v["i40"] = 0.047400*np.tanh(((((( -1.0*(( np.where(np.where(data["POS_SK_DPD_DEF_MEAN"]<0, data["PREV_APP_CREDIT_PERC_MEAN"], data["CC_AMT_DRAWINGS_POS_CURRENT_VAR"])<0, data["LIVINGAREA_AVG"],(((((data["DEF_30_CNT_SOCIAL_CIRCLE"])* 2.0)) <(np.tanh(( data["INSTAL_DBD_SUM"])))) *1.))))))* 2.0)) * 2.0))
v["i41"] = 0.047999*np.tanh(((((data["APPROVED_DAYS_DECISION_MIN"])+(data["INSTAL_PAYMENT_DIFF_MEAN"])))+(((((((data["APPROVED_CNT_PAYMENT_MEAN"])-(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])))* 2.0)) +(((data["ORGANIZATION_TYPE_Self_employed"])+(np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN"]))))))))))
v["i42"] = 0.049985*np.tanh(np.where(data["NAME_EDUCATION_TYPE_Higher_education"]>0, data["CC_CNT_DRAWINGS_POS_CURRENT_MIN"],(((( data["APPROVED_CNT_PAYMENT_SUM"])-(((data["PREV_NAME_YIELD_GROUP_low_action_MEAN"])+(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])))))-(((data["POS_COUNT"])+(((( data["POS_COUNT"])>(data["AMT_ANNUITY"])) *1.)))))))
v["i43"] = 0.049000*np.tanh(((np.where(np.where(((((data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])* 2.0)) +(np.maximum(((data["ACTIVE_DAYS_CREDIT_MAX"])) ,(( data["NEW_EXT_SOURCES_MEAN"])))))<0, data["NEW_EXT_SOURCES_MEAN"], data["ACTIVE_DAYS_CREDIT_MAX"])<0, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"],(-1.0*(( data["NEW_SOURCES_PROD"])))))* 2.0))
v["i44"] = 0.033210*np.tanh(np.where(((( data["BURO_AMT_CREDIT_SUM_LIMIT_MEAN"])+(data["BURO_CREDIT_TYPE_Mortgage_MEAN"])) /2.0)>0, data["CC_CNT_DRAWINGS_CURRENT_MAX"],(( data["AMT_ANNUITY"])+(((np.where(data["CC_CNT_DRAWINGS_CURRENT_MAX"]>0, data["CC_CNT_DRAWINGS_CURRENT_MAX"],(( data["AMT_ANNUITY"])-(data["APPROVED_AMT_ANNUITY_MEAN"])))) * 2.0)))))
 v["i45"] = 0.046000*np.tanh(((data["BURO_CREDIT_ACTIVE_Active_MEAN"])*(((((np.where(data["BURO_CREDIT_ACTIVE_Active_MEAN"]<0,(( np.where(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"] < -99998, data["BURO_DAYS_CREDIT_MAX"], np.maximum(((data["INSTAL_COUNT"])) ,(( data["ACTIVE_DAYS_CREDIT_MAX"])))))* 2.0), data["ACTIVE_DAYS_CREDIT_MAX"])) * 2.0)) * 2.0))))
v["i46"] = 0.049161*np.tanh(((((((np.maximum(((((((np.maximum(((data["PREV_PRODUCT_COMBINATION_Cash_Street__high_MEAN"])) ,(( data["ORGANIZATION_TYPE_Construction"])))) +(np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["INSTAL_PAYMENT_DIFF_MEAN"])))))) +(data["DEF_60_CNT_SOCIAL_CIRCLE"])))) ,(( data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])))) * 2.0)) * 2.0)) * 2.0))
v["i47"] = 0.043843*np.tanh(((np.where(data["ACTIVE_MONTHS_BALANCE_SIZE_MEAN"]>0, data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"],(((( data["OCCUPATION_TYPE_Drivers"])+(np.maximum(((data["INSTAL_DPD_MEAN"])) ,(( data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"])))))) +(((data["FLAG_WORK_PHONE"])-(data["PREV_HOUR_APPR_PROCESS_START_MEAN"])))))) -(data["NEW_EMPLOY_TO_BIRTH_RATIO"])))
 v["i48"] = 0.045000*np.tanh(((((((((np.where(data["INSTAL_AMT_PAYMENT_SUM"]>0, data["ACTIVE_AMT_CREDIT_SUM_SUM"], np.where(data["NAME_FAMILY_STATUS_Married"]>0, data["ORGANIZATION_TYPE_Business_Entity_Type_3"],(-1.0*(( data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])))))) * 2.0)) +(data["BURO_CREDIT_TYPE_Microloan_MEAN"])))* 2.0)) +(data["REGION_RATING_CLIENT_W_CITY"])))
 v["i49"] = 0.046200*np.tanh(((((np.where(data["INSTAL_DAYS_ENTRY_PAYMENT_SUM"]<0, data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"],(( np.maximum(((data["NAME_EDUCATION_TYPE_Lower_secondary"])) ,(( data["PREV_CODE_REJECT_REASON_SCOFR_MEAN"])))) +(((data["ORGANIZATION_TYPE_Self_employed"])+(((( data["INSTAL_DPD_MEAN"])>(data["ORGANIZATION_TYPE_Military"])) *1.))))))) * 2.0)) * 2.0))
v["i50"] = 0.048723*np.tanh(np.where(np.where(data["NEW_EXT_SOURCES_MEAN"]<0, np.where(data["POS_SK_DPD_DEF_MAX"]<0, data["EXT_SOURCE_3"], data["POS_SK_DPD_DEF_MAX"]), data["POS_SK_DPD_DEF_MAX"])< -99998, -3.0,(((( -2.0)-(data["EXT_SOURCE_3"])))-(data["BURO_DAYS_CREDIT_VAR"]))))
v["i51"] = 0.032400*np.tanh(np.where(((( data["REGION_RATING_CLIENT_W_CITY"])<(data["AMT_REQ_CREDIT_BUREAU_QRT"])) *1.) >0, data["NEW_RATIO_PREV_AMT_DOWN_PAYMENT_MEAN"], np.where(data["ACTIVE_MONTHS_BALANCE_MAX_MAX"]>0, data["AMT_ANNUITY"], np.where(data["APPROVED_AMT_ANNUITY_MAX"]>0, data["APPROVED_CNT_PAYMENT_SUM"],(( data["OWN_CAR_AGE"])*(data["OCCUPATION_TYPE_Core_staff"]))))))
v["i52"] = 0.048728*np.tanh(((((((( -1.0*(((((( data["PREV_AMT_ANNUITY_MEAN"])-(((( data["INSTAL_AMT_PAYMENT_SUM"])<(data["PREV_AMT_GOODS_PRICE_MAX"])) *1.))))-(np.where(data["PREV_AMT_GOODS_PRICE_MAX"]<0, data["AMT_CREDIT"], data["INSTAL_AMT_PAYMENT_MAX"])))))))* 2.0)) * 2.0)) * 2.0))
v["i53"] = 0.042920*np.tanh(((np.where(data["EXT_SOURCE_3"] < -99998, data["NEW_RATIO_PREV_DAYS_DECISION_MAX"],(((((((( data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])-(data["BURO_AMT_CREDIT_SUM_MEAN"])))* 2.0)) * 2.0)) -(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])))) -(((data["BURO_CREDIT_TYPE_Mortgage_MEAN"])-(data["FLAG_WORK_PHONE"])))))
 v["i54"] = 0.049915*np.tanh(((np.minimum(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])) ,(((((-1.0*(( data["NEW_CREDIT_TO_ANNUITY_RATIO"])))) * 2.0)))))+(np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( np.where(data["ACTIVE_MONTHS_BALANCE_SIZE_MEAN"] < -99998, np.maximum(((data["INSTAL_PAYMENT_DIFF_MEAN"])) ,(( data["NEW_CREDIT_TO_GOODS_RATIO"]))), data["BURO_CREDIT_TYPE_Microloan_MEAN"])))))))
 v["i55"] = 0.048997*np.tanh(((np.maximum(((data["APPROVED_CNT_PAYMENT_SUM"])) ,(((((((data["INSTAL_PAYMENT_PERC_SUM"])<(data["POS_SK_DPD_DEF_MAX"])) *1.))* 2.0)))))-(np.maximum(((np.maximum(((np.maximum(((data["ORGANIZATION_TYPE_School"])) ,(( data["PREV_NAME_YIELD_GROUP_low_action_MEAN"]))))),(( data["PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN"]))))),(( data["CODE_GENDER"]))))))
v["i56"] = 0.049560*np.tanh(((((((((np.maximum(((((data["NEW_EXT_SOURCES_MEAN"])*(data["PREV_PRODUCT_COMBINATION_Cash_Street__middle_MEAN"])))) ,(((((data["NEW_EXT_SOURCES_MEAN"])>(data["PREV_NAME_CASH_LOAN_PURPOSE_Urgent_needs_MEAN"])) *1.))))) -(((data["NEW_DOC_IND_STD"])-(data["PREV_PRODUCT_COMBINATION_Cash_Street__middle_MEAN"])))))* 2.0)) * 2.0)) * 2.0))
v["i57"] = 0.049800*np.tanh(((((np.where(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"]<0,(((( data["AMT_ANNUITY"])-(data["PREV_NAME_GOODS_CATEGORY_Furniture_MEAN"])))-(data["NEW_DOC_IND_STD"])) , data["APPROVED_CNT_PAYMENT_MEAN"])) -(np.where(data["APPROVED_CNT_PAYMENT_MEAN"]<0, data["INSTAL_AMT_PAYMENT_SUM"], data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])))) * 2.0))
v["i58"] = 0.047195*np.tanh(((data["REGION_RATING_CLIENT_W_CITY"])+(((data["NEW_SCORES_STD"])+(((((((data["ORGANIZATION_TYPE_Construction"])+(data["NAME_FAMILY_STATUS_Separated"])))+(np.maximum(((np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"]))))),(( data["CC_AMT_BALANCE_MIN"])))))) * 2.0))))))
v["i59"] = 0.049000*np.tanh(((data["PREV_CNT_PAYMENT_MEAN"])-(((((((data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])* 2.0)) -(data["PREV_NAME_PAYMENT_TYPE_XNA_MEAN"])))-(((((((((((data["INSTAL_DPD_MEAN"])* 2.0)) * 2.0)) * 2.0)) * 2.0)) *(data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"])))))))
 v["i60"] = 0.048607*np.tanh(((data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__high_MEAN"])+(((((np.minimum(((data["POS_SK_DPD_DEF_MAX"])) ,(((-1.0*(( data["BURO_AMT_CREDIT_SUM_MEAN"])))))))-(((np.where(data["NEW_RATIO_BURO_MONTHS_BALANCE_SIZE_MEAN"] < -99998, data["EXT_SOURCE_2"], data["AMT_REQ_CREDIT_BUREAU_QRT"])) / 2.0)))) -(data["INSTAL_DBD_SUM"])))))
 v["i61"] = 0.049003*np.tanh(((((((((((np.where(data["NEW_DOC_IND_KURT"]>0, np.where(data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"]<0,(( data["CC_AMT_RECEIVABLE_PRINCIPAL_MEAN"])-(data["CC_AMT_PAYMENT_TOTAL_CURRENT_SUM"])) , data["ACTIVE_DAYS_CREDIT_MEAN"]), data["INSTAL_PAYMENT_DIFF_MAX"])) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i62"] = 0.029562*np.tanh(((np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]>0, data["CC_AMT_RECIVABLE_MIN"], np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_MIN"] < -99998, data["DAYS_LAST_PHONE_CHANGE"],(( np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( np.maximum(((data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])) ,(( data["CC_AMT_RECIVABLE_MIN"])))))))+(data["REGION_POPULATION_RELATIVE"])))))* 2.0))
v["i63"] = 0.049969*np.tanh(np.where(data["EXT_SOURCE_1"] < -99998, np.maximum(((( -1.0*(( data["CODE_GENDER"]))))),(( data["DAYS_BIRTH"]))), np.where(data["ACTIVE_DAYS_CREDIT_VAR"] < -99998, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"], data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])))
 v["i64"] = 0.034802*np.tanh(((((data["APPROVED_AMT_APPLICATION_MAX"])-(np.where(data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]>0, -1.0,(((data["POS_COUNT"])>(data["APPROVED_CNT_PAYMENT_SUM"])) *1.))))) -(np.maximum(((((((data["INSTAL_AMT_INSTALMENT_MEAN"])* 2.0)) * 2.0))),(( data["NEW_CAR_TO_EMPLOY_RATIO"]))))))
v["i65"] = 0.048998*np.tanh(((((((( data["APPROVED_AMT_GOODS_PRICE_MIN"])<(data["NEW_EXT_SOURCES_MEAN"])) *1.))+(((((data["NEW_EXT_SOURCES_MEAN"])*(np.maximum(((data["POS_MONTHS_BALANCE_SIZE"])) ,(( data["CLOSED_MONTHS_BALANCE_MIN_MIN"])))))) -(np.maximum(((data["NEW_DOC_IND_STD"])) ,(( data["DAYS_BIRTH"])))))))) * 2.0))
v["i66"] = 0.046998*np.tanh(((((np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]>0, data["APPROVED_CNT_PAYMENT_SUM"], np.where(data["INSTAL_AMT_PAYMENT_SUM"]>0, data["OCCUPATION_TYPE_Laborers"],(((data["INSTAL_AMT_PAYMENT_SUM"])<(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])/ 2.0)))*1.))))-(np.tanh(( data["APPROVED_APP_CREDIT_PERC_MIN"])))))* 2.0))
v["i67"] = 0.039784*np.tanh(np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_MAX"],(( np.maximum(((data["OCCUPATION_TYPE_Drivers"])) ,(( np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["CC_CNT_DRAWINGS_CURRENT_MAX"])))))))+(np.where(data["DAYS_LAST_PHONE_CHANGE"]>0, data["ORGANIZATION_TYPE_Business_Entity_Type_3"], data["AMT_ANNUITY"])))))
 v["i68"] = 0.049100*np.tanh(np.where(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_SUM"]<0,(((((((((((( np.tanh(( data["NEW_CREDIT_TO_GOODS_RATIO"])))* 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) *(((data["AMT_ANNUITY"])-(data["OCCUPATION_TYPE_High_skill_tech_staff"])))) , data["CC_AMT_RECEIVABLE_PRINCIPAL_VAR"]))
v["i69"] = 0.037440*np.tanh(np.where(data["BURO_CREDIT_TYPE_Car_loan_MEAN"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"],(( np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( np.maximum(((data["ORGANIZATION_TYPE_Transport__type_3"])) ,(( np.maximum(((data["INSTAL_AMT_PAYMENT_MAX"])) ,(( data["CC_CNT_DRAWINGS_CURRENT_VAR"])))))))))) -(np.maximum(((data["CC_AMT_PAYMENT_TOTAL_CURRENT_SUM"])) ,(( data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])))))))
 v["i70"] = 0.049961*np.tanh(((((np.where(data["APPROVED_CNT_PAYMENT_SUM"] < -99998, data["NEW_DOC_IND_STD"],(( data["APPROVED_CNT_PAYMENT_SUM"])-(np.maximum(((data["REFUSED_HOUR_APPR_PROCESS_START_MIN"])) ,(( np.maximum(((data["CC_AMT_PAYMENT_CURRENT_MEAN"])) ,(( np.maximum(((data["FLOORSMIN_MEDI"])) ,(( data["POS_COUNT"])))))))))))))* 2.0)) * 2.0))
v["i71"] = 0.049982*np.tanh(((data["POS_SK_DPD_MAX"])+(((((((((((data["POS_SK_DPD_DEF_MAX"])+(((data["NAME_EDUCATION_TYPE_Lower_secondary"])+(((( data["OCCUPATION_TYPE_Core_staff"])<(((data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])* 2.0)))*1.))))))* 2.0)) * 2.0)) * 2.0)) * 2.0))))
v["i72"] = 0.049400*np.tanh(((((( data["NEW_EXT_SOURCES_MEAN"])>(data["INSTAL_PAYMENT_PERC_MEAN"])) *1.))-(((((data["AMT_REQ_CREDIT_BUREAU_YEAR"])+(((data["ORGANIZATION_TYPE_Military"])+(data["BURO_CREDIT_TYPE_Mortgage_MEAN"])))))-(np.where(data["INSTAL_PAYMENT_PERC_MEAN"]<0, data["NEW_DOC_IND_KURT"], data["NEW_RATIO_BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"]))))))
v["i73"] = 0.049278*np.tanh(((data["ORGANIZATION_TYPE_Self_employed"])+(((np.maximum(((data["ORGANIZATION_TYPE_Transport__type_3"])) ,(( data["BURO_CREDIT_TYPE_Microloan_MEAN"])))) +(((data["INSTAL_DPD_MEAN"])+(np.maximum(((data["CC_AMT_BALANCE_MIN"])) ,(( np.where(data["INSTAL_AMT_PAYMENT_MIN"]<0, data["INSTAL_DBD_MAX"], data["PREV_CODE_REJECT_REASON_LIMIT_MEAN"])))))))))))
 v["i74"] = 0.049119*np.tanh(((((np.where(data["BURO_AMT_CREDIT_SUM_DEBT_SUM"]>0, data["BURO_DAYS_CREDIT_MEAN"], np.maximum(((((np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_SUM"])) ,(( data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])))) * 2.0))),(((( data["EXT_SOURCE_3"])-(data["BURO_DAYS_CREDIT_MEAN"])))))))* 2.0)) * 2.0))
v["i75"] = 0.045722*np.tanh(((np.maximum(((np.maximum(((data["BURO_CREDIT_ACTIVE_Sold_MEAN"])) ,(( np.maximum(((data["WALLSMATERIAL_MODE_Stone__brick"])) ,(( data["CC_AMT_RECEIVABLE_PRINCIPAL_VAR"])))))))) ,(( data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])))) -(np.maximum(((np.maximum(((data["INSTAL_AMT_INSTALMENT_SUM"])) ,(( data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"]))))),(( data["NEW_CAR_TO_BIRTH_RATIO"]))))))
v["i76"] = 0.049090*np.tanh(np.maximum(((data["CC_CNT_DRAWINGS_CURRENT_MEAN"])) ,(( np.where(data["CLOSED_DAYS_CREDIT_MAX"]<0, np.maximum(((np.where(data["CC_CNT_DRAWINGS_CURRENT_MEAN"] < -99998, data["DEF_30_CNT_SOCIAL_CIRCLE"], data["ORGANIZATION_TYPE_Construction"]))),(((( data["OBS_60_CNT_SOCIAL_CIRCLE"])*(data["APPROVED_AMT_CREDIT_MAX"]))))), data["ACTIVE_AMT_CREDIT_SUM_SUM"])))))
 v["i77"] = 0.049867*np.tanh(((data["NEW_RATIO_PREV_AMT_CREDIT_MIN"])*(((-1.0)+(np.maximum(((data["NEW_EXT_SOURCES_MEAN"])) ,(( np.where(data["NEW_CAR_TO_BIRTH_RATIO"] < -99998, data["PREV_NAME_PORTFOLIO_Cash_MEAN"],(-1.0*(( data["REFUSED_AMT_APPLICATION_MIN"]))))))))))))
v["i78"] = 0.049750*np.tanh(((np.where(data["BURO_DAYS_CREDIT_ENDDATE_MEAN"]>0, data["ACTIVE_DAYS_CREDIT_MAX"], np.where(data["POS_SK_DPD_DEF_MAX"]<0, np.where(data["BURO_DAYS_CREDIT_MEAN"]<0, data["BURO_DAYS_CREDIT_MIN"], np.where(data["PREV_CHANNEL_TYPE_Contact_center_MEAN"]>0, data["PREV_CHANNEL_TYPE_Contact_center_MEAN"], data["POS_NAME_CONTRACT_STATUS_Signed_MEAN"])) , data["POS_MONTHS_BALANCE_MEAN"])))* 2.0))
v["i79"] = 0.049999*np.tanh(np.where(data["ACTIVE_MONTHS_BALANCE_MAX_MAX"]>0, data["NEW_RATIO_BURO_AMT_ANNUITY_MAX"], np.where(data["ACTIVE_MONTHS_BALANCE_MAX_MAX"]>0, data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"], np.where(data["BURO_AMT_CREDIT_SUM_MEAN"]>0, data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"], np.maximum(((np.maximum(((data["NEW_CREDIT_TO_GOODS_RATIO"])) ,(( data["BURO_CREDIT_TYPE_Microloan_MEAN"]))))),(( data["INSTAL_PAYMENT_DIFF_MEAN"])))))))
 v["i80"] = 0.049750*np.tanh(((((((((data["AMT_ANNUITY"])-(data["WEEKDAY_APPR_PROCESS_START_MONDAY"])))-(np.where(data["APPROVED_AMT_GOODS_PRICE_MAX"]>0,(( data["NEW_LIVE_IND_SUM"])* 2.0),(( data["INSTAL_AMT_INSTALMENT_SUM"])/ 2.0)))))* 2.0)) * 2.0))
v["i81"] = 0.049100*np.tanh(((((((np.where(((data["PREV_PRODUCT_COMBINATION_Cash_Street__high_MEAN"])*(np.maximum(((data["APPROVED_AMT_GOODS_PRICE_MIN"])) ,(( data["NAME_CONTRACT_TYPE_Cash_loans"])))))>0, data["POS_MONTHS_BALANCE_MEAN"], np.where(data["PREV_NAME_YIELD_GROUP_high_MEAN"] < -99998, data["REGION_POPULATION_RELATIVE"], data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) * 2.0)) * 2.0))
v["i82"] = 0.048000*np.tanh(((np.where(data["EXT_SOURCE_2"] < -99998, np.tanh(( data["PREV_APP_CREDIT_PERC_VAR"])) , np.where(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__middle_MEAN"] < -99998, data["NEW_DOC_IND_AVG"],(-1.0*(((((((( data["EXT_SOURCE_2"])+(data["OCCUPATION_TYPE_High_skill_tech_staff"])) /2.0)) +(data["NEW_DOC_IND_AVG"])) /2.0)))))))* 2.0))
v["i83"] = 0.039998*np.tanh(np.where(data["CC_AMT_PAYMENT_CURRENT_MEAN"]>0, data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"], np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0, np.maximum(((data["FLAG_WORK_PHONE"])) ,(((( data["POS_MONTHS_BALANCE_MEAN"])-(data["CODE_GENDER"]))))),(-1.0*(( np.maximum(((data["FLAG_WORK_PHONE"])) ,(( data["POS_MONTHS_BALANCE_MEAN"])))))))))
 v["i84"] = 0.046880*np.tanh(((((np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"]<0, np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"] < -99998, np.where(data["PREV_CODE_REJECT_REASON_HC_MEAN"]>0, data["PREV_CODE_REJECT_REASON_HC_MEAN"], data["INSTAL_PAYMENT_DIFF_MAX"]), data["BURO_AMT_CREDIT_SUM_DEBT_SUM"]),(-1.0*(((( data["INSTAL_PAYMENT_DIFF_MAX"])* 2.0)))))) * 2.0)) * 2.0))
v["i85"] = 0.036178*np.tanh(( -1.0*(((((( data["NEW_RATIO_PREV_AMT_CREDIT_MIN"])-(np.minimum(((data["REFUSED_AMT_DOWN_PAYMENT_MIN"])) ,(( data["REGION_POPULATION_RELATIVE"])))))) -(np.where(data["EXT_SOURCE_3"] < -99998, data["REFUSED_DAYS_DECISION_MEAN"],(((data["NEW_RATIO_BURO_DAYS_CREDIT_VAR"])<(data["REGION_POPULATION_RELATIVE"])) *1.))))))))
 v["i86"] = 0.019979*np.tanh(((data["ORGANIZATION_TYPE_Transport__type_3"])-(((( data["EXT_SOURCE_2"])+(((np.where(( -1.0*(( data["EXT_SOURCE_2"])))<0, data["BURO_AMT_CREDIT_SUM_MEAN"],(((data["APPROVED_AMT_CREDIT_MAX"])<(((( data["OCCUPATION_TYPE_High_skill_tech_staff"])<(data["APPROVED_AMT_CREDIT_MAX"])) *1.))) *1.))) * 2.0)))/2.0))))
v["i87"] = 0.047481*np.tanh(((data["ORGANIZATION_TYPE_Transport__type_3"])+(((data["ORGANIZATION_TYPE_Construction"])+(np.maximum(((data["BURO_STATUS_1_MEAN_MEAN"])) ,(((((data["APPROVED_CNT_PAYMENT_SUM"])+(((data["INSTAL_COUNT"])*(np.where(data["APPROVED_CNT_PAYMENT_SUM"]<0, data["ACTIVE_AMT_CREDIT_SUM_DEBT_MAX"], data["FLAG_WORK_PHONE"])))))/2.0)))))))))
 v["i88"] = 0.046499*np.tanh(( -1.0*(( np.maximum(((data["YEARS_BUILD_MEDI"])) ,(( np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]<0, np.where(data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"]<0, np.where(data["PREV_WEEKDAY_APPR_PROCESS_START_SUNDAY_MEAN"]<0, data["NEW_EMPLOY_TO_BIRTH_RATIO"], data["APPROVED_DAYS_DECISION_MEAN"]), data["NEW_RATIO_PREV_DAYS_DECISION_MAX"]), data["BURO_CREDIT_TYPE_Mortgage_MEAN"]))))))))
v["i89"] = 0.047614*np.tanh(np.where(data["BURO_STATUS_1_MEAN_MEAN"]>0, data["NEW_ANNUITY_TO_INCOME_RATIO"], np.where(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"]<0, np.where(data["ACTIVE_DAYS_CREDIT_ENDDATE_MAX"] < -99998,(( data["PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN"])+(data["DAYS_REGISTRATION"])) , data["PREV_NAME_CONTRACT_TYPE_Revolving_loans_MEAN"]),(((( data["WALLSMATERIAL_MODE_Stone__brick"])* 2.0)) * 2.0))))
v["i90"] = 0.049597*np.tanh(np.maximum(((np.where(data["APPROVED_AMT_GOODS_PRICE_MIN"]>0, data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"], np.where(data["FLAG_DOCUMENT_8"]>0, data["CC_AMT_INST_MIN_REGULARITY_VAR"],(( data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])*(data["APPROVED_CNT_PAYMENT_MEAN"])))))) ,(((( data["CC_AMT_BALANCE_MEAN"])-(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"]))))))
v["i91"] = 0.046517*np.tanh(((((((np.where(data["ACTIVE_DAYS_CREDIT_MAX"]>0,(( np.where(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"]>0, data["CC_CNT_DRAWINGS_ATM_CURRENT_MAX"], data["ACTIVE_DAYS_CREDIT_MAX"])) +(data["NAME_HOUSING_TYPE_Municipal_apartment"])) , data["INSTAL_PAYMENT_DIFF_MEAN"])) +(data["NAME_HOUSING_TYPE_Municipal_apartment"])))* 2.0)) * 2.0))
v["i92"] = 0.034976*np.tanh(((np.where(data["PREV_AMT_GOODS_PRICE_MEAN"] < -99998, data["NEW_DOC_IND_STD"],(( np.where(data["CLOSED_DAYS_CREDIT_MAX"]>0, data["INSTAL_AMT_PAYMENT_MIN"], np.where(data["ORGANIZATION_TYPE_Transport__type_3"]>0, data["ORGANIZATION_TYPE_Transport__type_3"],(( data["OBS_60_CNT_SOCIAL_CIRCLE"])*(data["PREV_AMT_GOODS_PRICE_MEAN"])))))* 2.0)))* 2.0))
v["i93"] = 0.015000*np.tanh(((np.maximum(((np.where(data["WEEKDAY_APPR_PROCESS_START_SATURDAY"]<0, data["ORGANIZATION_TYPE_Business_Entity_Type_3"], data["REFUSED_HOUR_APPR_PROCESS_START_MAX"]))),(( data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])))) -(np.maximum(((np.maximum(((data["WEEKDAY_APPR_PROCESS_START_SUNDAY"])) ,(( data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"]))))),(( np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_MEAN"])) ,(( data["OCCUPATION_TYPE_Accountants"])))))))))
 v["i94"] = 0.049002*np.tanh(((( data["NEW_EXT_SOURCES_MEAN"])<(((np.tanh(( np.where(data["NEW_EXT_SOURCES_MEAN"]<0, data["NEW_SOURCES_PROD"], np.tanh(( np.tanh(((((data["ORGANIZATION_TYPE_Construction"])>(np.tanh(( data["APPROVED_AMT_DOWN_PAYMENT_MIN"])))) *1.))))))))) * 2.0)))*1.))
 v["i95"] = 0.041000*np.tanh(((data["FLAG_WORK_PHONE"])+(np.where(data["APPROVED_DAYS_DECISION_MIN"]>0, np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]>0, data["CC_AMT_DRAWINGS_CURRENT_MEAN"],(((( data["INSTAL_DPD_MEAN"])+(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])+(data["APPROVED_DAYS_DECISION_MIN"])))))* 2.0)) , data["NEW_CREDIT_TO_ANNUITY_RATIO"]))))
v["i96"] = 0.048802*np.tanh(np.where(data["EXT_SOURCE_1"] < -99998,(( data["DAYS_BIRTH"])* 2.0),(((((((-1.0*(((((( data["DAYS_BIRTH"])* 2.0)) * 2.0)))))-(data["EXT_SOURCE_1"])))-(data["EXT_SOURCE_1"])))-(data["EXT_SOURCE_1"]))))
v["i97"] = 0.049802*np.tanh(np.where(data["ACTIVE_DAYS_CREDIT_MIN"] < -99998, data["AMT_INCOME_TOTAL"], np.where(data["CC_AMT_RECIVABLE_MEAN"]>0, data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"],(( np.where(data["NEW_INC_BY_ORG"]<0, data["CLOSED_DAYS_CREDIT_MEAN"], data["PREV_NAME_PRODUCT_TYPE_x_sell_MEAN"])) -(data["DAYS_EMPLOYED"])))))
 v["i98"] = 0.011046*np.tanh(((np.where(((( data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"])>(data["CC_CNT_DRAWINGS_ATM_CURRENT_MAX"])) *1.) >0, data["INSTAL_AMT_PAYMENT_MIN"],(( np.maximum(((data["POS_SK_DPD_DEF_MAX"])) ,(((((data["PREV_DAYS_DECISION_MEAN"])>(((( data["POS_SK_DPD_DEF_MAX"])>(data["NEW_RATIO_PREV_DAYS_DECISION_MAX"])) *1.))) *1.))))) * 2.0)))* 2.0))
v["i99"] = 0.049544*np.tanh(((np.maximum(((data["ORGANIZATION_TYPE_Transport__type_3"])) ,(( np.maximum(((data["POS_NAME_CONTRACT_STATUS_Returned_to_the_store_MEAN"])) ,(( np.maximum(((data["CC_CNT_DRAWINGS_CURRENT_VAR"])) ,(( np.where(data["NEW_RATIO_PREV_APP_CREDIT_PERC_MEAN"] < -99998, data["NEW_SCORES_STD"], data["OBS_60_CNT_SOCIAL_CIRCLE"])))))))))))+(np.minimum(((data["REGION_RATING_CLIENT_W_CITY"])) ,(( data["DAYS_ID_PUBLISH"]))))))
v["i100"] = 0.047080*np.tanh(np.where(data["EXT_SOURCE_3"] < -99998,(( data["EXT_SOURCE_2"])* 2.0),(((( data["NAME_FAMILY_STATUS_Married"])*(data["DAYS_BIRTH"])))+(((((( data["EXT_SOURCE_3"])/ 2.0)) <(((data["ORGANIZATION_TYPE_Construction"])-(data["NAME_FAMILY_STATUS_Married"])))) *1.)))))
v["i101"] = 0.049910*np.tanh(np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]>0, data["CC_MONTHS_BALANCE_VAR"], np.where(data["EXT_SOURCE_1"]<0,(((data["NEW_EXT_SOURCES_MEAN"])>(((data["PREV_CHANNEL_TYPE_Credit_and_cash_offices_MEAN"])/ 2.0)))*1.) ,(((( data["REGION_RATING_CLIENT_W_CITY"])-(data["NEW_EXT_SOURCES_MEAN"])))-(data["NAME_INCOME_TYPE_Working"])))))
 v["i102"] = 0.049000*np.tanh(((((np.tanh(( np.tanh(( data["NEW_EXT_SOURCES_MEAN"])))))-(np.maximum(((((( data["WEEKDAY_APPR_PROCESS_START_SUNDAY"])>(((data["NEW_EXT_SOURCES_MEAN"])/ 2.0)))*1.))) ,(( np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])) ,(( data["PREV_CHANNEL_TYPE_Channel_of_corporate_sales_MEAN"])))))))))-(data["NEW_EXT_SOURCES_MEAN"])))
 v["i103"] = 0.047481*np.tanh(((((((((data["FLAG_DOCUMENT_3"])*(np.where(data["BURO_MONTHS_BALANCE_SIZE_SUM"]<0, np.maximum(((((data["PREV_PRODUCT_COMBINATION_POS_household_without_interest_MEAN"])* 2.0))),(( data["REGION_POPULATION_RELATIVE"]))), data["PREV_PRODUCT_COMBINATION_POS_household_without_interest_MEAN"])))) * 2.0)) -(data["WEEKDAY_APPR_PROCESS_START_SUNDAY"])))* 2.0))
v["i104"] = 0.037198*np.tanh(np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(((( data["EXT_SOURCE_3"])*(np.where(((data["EXT_SOURCE_3"])*(data["EXT_SOURCE_2"])) < -99998, data["EXT_SOURCE_2"], np.tanh(((( data["NEW_CREDIT_TO_GOODS_RATIO"])*(data["EXT_SOURCE_2"])))))))))))
 v["i105"] = 0.049050*np.tanh(np.where(data["PREV_AMT_DOWN_PAYMENT_MIN"]>0, data["INSTAL_AMT_PAYMENT_MIN"],(((( np.where(data["PREV_NAME_PRODUCT_TYPE_walk_in_MEAN"]>0, data["NEW_ANNUITY_TO_INCOME_RATIO"], data["PREV_AMT_DOWN_PAYMENT_MIN"])) -(np.where(data["TOTALAREA_MODE"] < -99998, data["PREV_NAME_YIELD_GROUP_middle_MEAN"], data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])))) * 2.0)))
 v["i106"] = 0.043662*np.tanh(np.where(data["NEW_DOC_IND_KURT"]>0, np.where(data["BURO_DAYS_CREDIT_MIN"]>0, np.maximum(((data["PREV_WEEKDAY_APPR_PROCESS_START_FRIDAY_MEAN"])) ,(( data["NEW_RATIO_BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"]))), np.where(data["NEW_INC_PER_CHLD"]<0, data["ACTIVE_MONTHS_BALANCE_MIN_MIN"],(-1.0*(( data["ACTIVE_MONTHS_BALANCE_MIN_MIN"]))))),(( data["NEW_DOC_IND_KURT"])* 2.0)))
 v["i107"] = 0.023067*np.tanh(((((((( data["EXT_SOURCE_2"])<(( -1.0*(((((((( -1.0*(( data["EXT_SOURCE_2"])))) >(((data["PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN"])*(((( data["BURO_DAYS_CREDIT_MAX"])<(data["PREV_AMT_GOODS_PRICE_MEAN"])) *1.))))) *1.))* 2.0)))))) *1.))* 2.0)) * 2.0))
v["i108"] = 0.050000*np.tanh(( -1.0*(( np.where(data["APPROVED_AMT_CREDIT_MAX"]>0, data["NEW_RATIO_PREV_AMT_CREDIT_MAX"], np.where(np.where(data["CLOSED_AMT_CREDIT_SUM_SUM"]>0, data["NEW_EMPLOY_TO_BIRTH_RATIO"],(((data["CODE_GENDER"])+(data["POS_MONTHS_BALANCE_SIZE"])) /2.0)) <0, data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"],(-1.0*(( data["REFUSED_AMT_CREDIT_MIN"])))))))))
 v["i109"] = 0.001990*np.tanh(((data["POS_NAME_CONTRACT_STATUS_Returned_to_the_store_MEAN"])-(np.where(data["APPROVED_AMT_ANNUITY_MEAN"]>0, data["PREV_NAME_SELLER_INDUSTRY_XNA_MEAN"],(( np.maximum(((((np.maximum(((data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])) ,(( np.minimum(((data["BURO_DAYS_CREDIT_UPDATE_MEAN"])) ,(( data["BURO_CREDIT_TYPE_Consumer_credit_MEAN"])))))))* 2.0))),(( data["INSTAL_AMT_INSTALMENT_MEAN"])))) * 2.0)))))
 v["i110"] = 0.035600*np.tanh(((( -1.0*(( np.maximum(((((( data["NAME_FAMILY_STATUS_Married"])+(data["NAME_INCOME_TYPE_Commercial_associate"])) /2.0))),(( data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"])))))))-(np.maximum(((((( data["PREV_WEEKDAY_APPR_PROCESS_START_SATURDAY_MEAN"])+(data["OCCUPATION_TYPE_High_skill_tech_staff"])) /2.0))),(((( data["OCCUPATION_TYPE_Accountants"])+(data["ORGANIZATION_TYPE_Medicine"]))))))))
v["i111"] = 0.026365*np.tanh(((((((((data["ORGANIZATION_TYPE_Medicine"])>(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"])) *1.))>(data["DAYS_BIRTH"])) *1.))-(np.where(data["DAYS_BIRTH"]>0, data["NEW_INC_BY_ORG"],(( data["PREV_NAME_PORTFOLIO_POS_MEAN"])+(((( data["POS_SK_DPD_MEAN"])>(data["CC_AMT_BALANCE_VAR"])) *1.)))))))
v["i112"] = 0.049648*np.tanh(np.where(data["NEW_RATIO_PREV_AMT_APPLICATION_MIN"] < -99998,(((((data["PREV_NAME_CASH_LOAN_PURPOSE_XNA_MEAN"])*(( -1.0*(( data["NAME_HOUSING_TYPE_House___apartment"])))))) +(((data["POS_SK_DPD_MAX"])-(data["NEW_DOC_IND_AVG"])))) /2.0),(( data["PREV_CHANNEL_TYPE_Stone_MEAN"])-(( -1.0*(( data["PREV_NAME_TYPE_SUITE_Spouse__partner_MEAN"])))))))
 v["i113"] = 0.028480*np.tanh(np.where(data["PREV_CHANNEL_TYPE_Credit_and_cash_offices_MEAN"] < -99998, data["NEW_DOC_IND_STD"], np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"] < -99998, data["POS_SK_DPD_DEF_MAX"], np.where(data["PREV_CHANNEL_TYPE_Credit_and_cash_offices_MEAN"]>0, data["BURO_CREDIT_TYPE_Credit_card_MEAN"],(((data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"])>(((( data["PREV_NAME_PAYMENT_TYPE_Cash_through_the_bank_MEAN"])<(data["ACTIVE_MONTHS_BALANCE_MIN_MIN"])) *1.))) *1.)))))
v["i114"] = 0.048956*np.tanh(np.where(data["ACTIVE_AMT_CREDIT_SUM_SUM"]<0, np.where(data["AMT_GOODS_PRICE"]<0, np.where(data["NEW_RATIO_BURO_MONTHS_BALANCE_SIZE_SUM"] < -99998, data["OCCUPATION_TYPE_Laborers"], data["POS_COUNT"]), data["NAME_EDUCATION_TYPE_Higher_education"]),(( data["ACTIVE_AMT_CREDIT_SUM_SUM"])+(( -1.0*(( data["ACTIVE_AMT_CREDIT_SUM_MAX"])))))))
 v["i115"] = 0.039000*np.tanh(np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_MEAN"]>0, np.where(data["NEW_EXT_SOURCES_MEAN"]<0,(( data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])* 2.0), data["PREV_CODE_REJECT_REASON_SCO_MEAN"]),(-1.0*(( np.where(data["NEW_EXT_SOURCES_MEAN"]<0, data["INSTAL_AMT_PAYMENT_SUM"],(( data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])+(data["PREV_CODE_REJECT_REASON_SCO_MEAN"]))))))))
v["i116"] = 0.049997*np.tanh(( -1.0*(((( np.where(data["CC_AMT_RECIVABLE_VAR"]>0, data["ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN"],(-1.0*(((((data["ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN"])>(data["BURO_AMT_CREDIT_SUM_MEAN"])) *1.))))))+(((( data["BURO_AMT_CREDIT_SUM_MEAN"])>(data["ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN"])) *1.)))))))
v["i117"] = 0.049001*np.tanh(np.where(data["CC_CNT_DRAWINGS_ATM_CURRENT_VAR"]>0, data["CLOSED_DAYS_CREDIT_MIN"], np.where(data["NEW_CREDIT_TO_GOODS_RATIO"]>0, np.maximum(((data["NEW_ANNUITY_TO_INCOME_RATIO"])) ,(( data["APPROVED_AMT_APPLICATION_MAX"]))),(((data["NEW_CREDIT_TO_GOODS_RATIO"])<(np.where(data["APPROVED_AMT_APPLICATION_MEAN"]<0, data["APPROVED_AMT_APPLICATION_MEAN"], data["CC_NAME_CONTRACT_STATUS_Active_SUM"])))*1.))))
 v["i118"] = 0.029997*np.tanh(np.where(data["NEW_RATIO_PREV_APP_CREDIT_PERC_MEAN"]>0, data["NEW_RATIO_PREV_APP_CREDIT_PERC_MEAN"], np.where(data["CC_AMT_RECEIVABLE_PRINCIPAL_VAR"] < -99998, np.where(data["ACTIVE_DAYS_CREDIT_UPDATE_MEAN"]<0, data["NEW_CREDIT_TO_GOODS_RATIO"], data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]), np.where(data["CC_CNT_DRAWINGS_CURRENT_MAX"]<0, data["CLOSED_DAYS_CREDIT_VAR"], data["PREV_NAME_PAYMENT_TYPE_XNA_MEAN"]))))
v["i119"] = 0.049942*np.tanh(np.where(data["PREV_NAME_CLIENT_TYPE_Refreshed_MEAN"]>0, data["BURO_CREDIT_TYPE_Credit_card_MEAN"], np.maximum(((data["BURO_CREDIT_ACTIVE_Sold_MEAN"])) ,(( np.where(data["PREV_AMT_DOWN_PAYMENT_MIN"]>0, data["FLOORSMIN_MODE"], np.where(data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"]>0, data["BURO_CREDIT_TYPE_Credit_card_MEAN"],(( data["CC_AMT_RECEIVABLE_PRINCIPAL_MEAN"])-(data["CC_AMT_CREDIT_LIMIT_ACTUAL_MEAN"])))))))))
 v["i120"] = 0.014008*np.tanh(( -1.0*(( np.where(data["NONLIVINGAREA_AVG"]>0, data["NEW_INC_BY_ORG"],(( np.maximum(((data["AMT_REQ_CREDIT_BUREAU_QRT"])) ,(((((np.maximum(((data["WEEKDAY_APPR_PROCESS_START_SATURDAY"])) ,(( data["NEW_RATIO_PREV_AMT_CREDIT_MAX"])))) +(np.maximum(((data["NAME_HOUSING_TYPE_Office_apartment"])) ,(( data["PREV_CHANNEL_TYPE_Channel_of_corporate_sales_MEAN"])))))/2.0)))))* 2.0))))))
v["i121"] = 0.047508*np.tanh(np.where(data["FLAG_PHONE"]<0,(( data["INSTAL_DBD_SUM"])*(np.where(data["NEW_DOC_IND_STD"]>0, np.where(data["BURO_DAYS_CREDIT_MAX"]>0, data["PREV_NAME_YIELD_GROUP_high_MEAN"], data["FLAG_PHONE"]), 3.0))), data["OWN_CAR_AGE"]))
v["i122"] = 0.022005*np.tanh(((np.maximum(((data["CC_AMT_RECIVABLE_VAR"])) ,(( np.maximum(((((( data["POS_MONTHS_BALANCE_MEAN"])<(np.minimum(((data["EXT_SOURCE_3"])) ,(( data["INSTAL_PAYMENT_DIFF_SUM"])))))*1.))) ,(((((data["PREV_NAME_CASH_LOAN_PURPOSE_Medicine_MEAN"])>(((data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"])*(data["REFUSED_AMT_GOODS_PRICE_MAX"])))) *1.))))))))* 2.0))
v["i123"] = 0.024998*np.tanh(np.where(data["LIVE_CITY_NOT_WORK_CITY"]>0, data["ACTIVE_AMT_ANNUITY_MEAN"], np.where(data["ACTIVE_AMT_CREDIT_SUM_DEBT_MAX"]<0, np.where(data["CLOSED_DAYS_CREDIT_VAR"]<0, np.where(data["PREV_NAME_CONTRACT_STATUS_Approved_MEAN"]<0, data["NEW_RATIO_PREV_AMT_GOODS_PRICE_MIN"], data["PREV_NAME_CONTRACT_STATUS_Approved_MEAN"]), data["APPROVED_APP_CREDIT_PERC_VAR"]), data["NEW_CREDIT_TO_INCOME_RATIO"])))
 v["i124"] = 0.037322*np.tanh(np.maximum(((np.where(data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"] < -99998, data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"], data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]))),(( np.where(data["APPROVED_AMT_GOODS_PRICE_MIN"]>0, data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"],(((data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"])<(data["APPROVED_AMT_GOODS_PRICE_MIN"])) *1.))))))
 v["i125"] = 0.049402*np.tanh(((np.where(data["CC_AMT_DRAWINGS_POS_CURRENT_SUM"]>0, data["PREV_AMT_GOODS_PRICE_MIN"], np.where(data["PREV_PRODUCT_COMBINATION_POS_industry_with_interest_MEAN"]>0,(-1.0*(( data["PREV_NAME_YIELD_GROUP_middle_MEAN"]))),(( data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__middle_MEAN"])*(data["APPROVED_CNT_PAYMENT_MEAN"])))))-(((( data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])>(data["REG_CITY_NOT_LIVE_CITY"])) *1.))))
 v["i126"] = 0.049398*np.tanh(np.where(data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"] < -99998, data["DEF_30_CNT_SOCIAL_CIRCLE"], np.where(data["EXT_SOURCE_3"] < -99998, data["EXT_SOURCE_2"],(( np.maximum(((data["DAYS_BIRTH"])) ,(( data["NAME_EDUCATION_TYPE_Higher_education"])))) -(((data["NAME_EDUCATION_TYPE_Higher_education"])*(data["DAYS_BIRTH"])))))))
 v["i127"] = 0.043856*np.tanh(((np.where(data["DAYS_ID_PUBLISH"]<0,(( data["DAYS_BIRTH"])-(data["NEW_EMPLOY_TO_BIRTH_RATIO"])) ,(-1.0*(( np.maximum(((data["PREV_NAME_CONTRACT_TYPE_Consumer_loans_MEAN"])) ,(( data["DAYS_BIRTH"])))))))) +(data["DAYS_ID_PUBLISH"])))
 v["i128"] = 0.047562*np.tanh(((data["NAME_EDUCATION_TYPE_Lower_secondary"])+(((data["POS_SK_DPD_MEAN"])+(np.maximum(((((np.where(data["ACTIVE_DAYS_CREDIT_ENDDATE_MAX"] < -99998, data["INSTAL_PAYMENT_DIFF_SUM"], data["PREV_AMT_ANNUITY_MIN"])) +(data["ORGANIZATION_TYPE_Transport__type_3"])))) ,(((( data["ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN"])+(data["BURO_CREDIT_TYPE_Microloan_MEAN"]))))))))))
v["i129"] = 0.041886*np.tanh(((data["DAYS_REGISTRATION"])*(((((( data["POS_NAME_CONTRACT_STATUS_Signed_MEAN"])+(data["REGION_RATING_CLIENT_W_CITY"])) /2.0)) +(((((((data["PREV_CODE_REJECT_REASON_SCOFR_MEAN"])+(( -1.0*(( data["ORGANIZATION_TYPE_Military"])))))/2.0)) +(((( data["POS_SK_DPD_DEF_MEAN"])>(data["DAYS_BIRTH"])) *1.))) /2.0))))))
v["i130"] = 0.049340*np.tanh(np.where(data["ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN"]<0, np.where(data["CC_AMT_INST_MIN_REGULARITY_SUM"]<0, np.where(data["CC_AMT_INST_MIN_REGULARITY_SUM"] < -99998, data["NAME_HOUSING_TYPE_Rented_apartment"], data["APPROVED_CNT_PAYMENT_MEAN"]), np.where(data["CC_CNT_INSTALMENT_MATURE_CUM_MEAN"]>0, data["FLOORSMIN_AVG"], data["CC_AMT_CREDIT_LIMIT_ACTUAL_MIN"])) , 3.141593))
v["i131"] = 0.044198*np.tanh(np.where(((data["APPROVED_APP_CREDIT_PERC_VAR"])-(data["EXT_SOURCE_3"])) < -99998,(( data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])* 2.0), np.maximum(((((data["EXT_SOURCE_3"])-(data["APPROVED_APP_CREDIT_PERC_VAR"])))) ,(((((( data["CC_AMT_RECEIVABLE_PRINCIPAL_VAR"])-(data["CC_AMT_CREDIT_LIMIT_ACTUAL_MEAN"])))* 2.0))))))
v["i132"] = 0.049514*np.tanh(( -1.0*(( np.where(data["LIVINGAREA_AVG"]>0, data["NEW_RATIO_PREV_APP_CREDIT_PERC_MEAN"], np.maximum(((data["NAME_HOUSING_TYPE_Office_apartment"])) ,(((( np.maximum(((((( data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"])>(np.maximum(((data["REGION_POPULATION_RELATIVE"])) ,(( data["CC_AMT_RECEIVABLE_PRINCIPAL_MEAN"])))))*1.))) ,(( data["ORGANIZATION_TYPE_Industry__type_9"])))) * 2.0)))))))))
 v["i133"] = 0.049862*np.tanh(np.where(data["BURO_AMT_CREDIT_SUM_DEBT_MAX"]>0, data["CC_NAME_CONTRACT_STATUS_Active_SUM"],(( np.where(((( data["ENTRANCES_MEDI"])>(data["INSTAL_DPD_MEAN"])) *1.) >0, data["CC_NAME_CONTRACT_STATUS_Active_SUM"],(((data["NEW_RATIO_PREV_DAYS_DECISION_MAX"])>(data["ORGANIZATION_TYPE_Military"])) *1.))) * 2.0)))
 v["i134"] = 0.045502*np.tanh(( -1.0*(( np.where(data["EXT_SOURCE_3"]>0, data["BURO_DAYS_CREDIT_UPDATE_MEAN"], np.where(data["CLOSED_AMT_CREDIT_MAX_OVERDUE_MEAN"]>0, data["INSTAL_PAYMENT_DIFF_MAX"], np.where(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"]<0, data["CLOSED_AMT_CREDIT_MAX_OVERDUE_MEAN"], np.where(data["ACTIVE_AMT_CREDIT_SUM_SUM"]<0, data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"], data["EXT_SOURCE_3"]))))))))
v["i135"] = 0.020008*np.tanh(( -1.0*(( np.where(data["APPROVED_CNT_PAYMENT_SUM"]>0,(( np.tanh(( data["DAYS_EMPLOYED"])))-(np.where(data["APPROVED_AMT_GOODS_PRICE_MIN"]<0,(((data["INSTAL_DPD_MEAN"])>(data["DAYS_EMPLOYED"])) *1.) , data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"]))), data["OCCUPATION_TYPE_Medicine_staff"])))))
 v["i136"] = 0.035000*np.tanh(np.where(data["NEW_EXT_SOURCES_MEAN"]>0,(-1.0*(( data["NEW_PHONE_TO_BIRTH_RATIO"]))), np.where(data["EXT_SOURCE_3"] < -99998, data["REFUSED_AMT_CREDIT_MAX"],(( data["PREV_NAME_GOODS_CATEGORY_Photo___Cinema_Equipment_MEAN"])*(np.where(data["PREV_NAME_GOODS_CATEGORY_Photo___Cinema_Equipment_MEAN"] < -99998, data["EXT_SOURCE_3"], data["ACTIVE_DAYS_CREDIT_ENDDATE_MEAN"]))))))
v["i137"] = 0.048501*np.tanh(np.where(data["NEW_RATIO_BURO_MONTHS_BALANCE_SIZE_MEAN"]>0, data["PREV_NAME_YIELD_GROUP_middle_MEAN"], np.maximum(((data["PREV_NAME_GOODS_CATEGORY_Audio_Video_MEAN"])) ,(((( np.where(data["INSTAL_PAYMENT_DIFF_MAX"]>0, data["OCCUPATION_TYPE_Drivers"], data["APPROVED_DAYS_DECISION_MAX"])) *(data["NEW_CREDIT_TO_GOODS_RATIO"])))))))
 v["i138"] = 0.048902*np.tanh(np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_VAR"] < -99998,(((np.where(data["BURO_DAYS_CREDIT_MEAN"]>0, data["PREV_CHANNEL_TYPE_Stone_MEAN"], data["PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN"])) +(np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"] < -99998, data["PREV_CHANNEL_TYPE_Stone_MEAN"], data["ACTIVE_DAYS_CREDIT_MEAN"])))/2.0),(( data["NEW_RATIO_PREV_AMT_ANNUITY_MAX"])+(data["BURO_DAYS_CREDIT_MEAN"]))))
v["i139"] = 0.043840*np.tanh(np.where(data["AMT_INCOME_TOTAL"]>0, data["FLAG_DOCUMENT_3"], np.maximum(((data["WEEKDAY_APPR_PROCESS_START_WEDNESDAY"])) ,(( np.maximum(((data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"])) ,(((-1.0*(((((((data["WEEKDAY_APPR_PROCESS_START_WEDNESDAY"])>(data["NEW_EXT_SOURCES_MEAN"])) *1.))+(data["NEW_CREDIT_TO_GOODS_RATIO"])))))))))))))
 v["i140"] = 0.049910*np.tanh(np.where(data["PREV_NAME_PAYMENT_TYPE_Cash_through_the_bank_MEAN"]>0, data["PREV_CODE_REJECT_REASON_XAP_MEAN"], np.where(data["CC_AMT_PAYMENT_CURRENT_SUM"]>0, data["OCCUPATION_TYPE_Drivers"],(((data["OCCUPATION_TYPE_Drivers"])>(np.where(data["APPROVED_AMT_ANNUITY_MIN"] < -99998, data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"],(( data["APPROVED_AMT_GOODS_PRICE_MIN"])/ 2.0)))) *1.))))
 v["i141"] = 0.049610*np.tanh(((np.where(data["LANDAREA_AVG"]>0, data["CC_NAME_CONTRACT_STATUS_Active_SUM"],(( np.where(data["BURO_STATUS_0_MEAN_MEAN"]>0, data["PREV_PRODUCT_COMBINATION_POS_household_without_interest_MEAN"], np.where(data["BASEMENTAREA_AVG"]>0, data["NEW_CREDIT_TO_ANNUITY_RATIO"],(((data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"])>(data["POS_SK_DPD_DEF_MEAN"])) *1.))))* 2.0)))* 2.0))
v["i142"] = 0.042842*np.tanh(np.where(data["CODE_GENDER"]>0, np.minimum(((data["CODE_GENDER"])) ,(((( data["DAYS_BIRTH"])* 2.0)))) ,(-1.0*(((( data["DAYS_BIRTH"])-(( -1.0*(((( data["NEW_EXT_SOURCES_MEAN"])-(( -1.0*(( data["DAYS_BIRTH"])))))))))))))))
 v["i143"] = 0.045501*np.tanh(np.where(data["REGION_RATING_CLIENT"]>0,(( data["PREV_RATE_DOWN_PAYMENT_MIN"])* 2.0), np.where(((data["CC_AMT_PAYMENT_CURRENT_SUM"])/ 2.0)>0, data["PREV_NAME_TYPE_SUITE_Family_MEAN"], np.where(data["NEW_PHONE_TO_EMPLOY_RATIO"]>0, data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"],(((data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])>(data["NEW_PHONE_TO_EMPLOY_RATIO"])) *1.)))))
v["i144"] = 0.000197*np.tanh(((( data["POS_NAME_CONTRACT_STATUS_Completed_MEAN"])<(np.tanh(( np.where(data["PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN"]<0, np.where(data["PREV_AMT_GOODS_PRICE_MAX"]<0, data["FLOORSMIN_MODE"], data["APPROVED_AMT_APPLICATION_MAX"]), np.where(data["BURO_STATUS_1_MEAN_MEAN"]<0, data["CC_AMT_DRAWINGS_ATM_CURRENT_SUM"],(-1.0*(( data["PREV_NAME_SELLER_INDUSTRY_XNA_MEAN"])))))))))*1.))
 v["i145"] = 0.049703*np.tanh(np.maximum(((data["POS_NAME_CONTRACT_STATUS_Returned_to_the_store_MEAN"])) ,(( np.maximum(((((((data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"])* 2.0)) * 2.0))),(((((data["DAYS_EMPLOYED"])<(np.where(np.maximum(((data["BURO_DAYS_CREDIT_ENDDATE_MIN"])) ,(( data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])))>0, data["BURO_CREDIT_ACTIVE_Sold_MEAN"], data["CC_CNT_DRAWINGS_POS_CURRENT_MIN"])))*1.))))))))
 v["i146"] = 0.049482*np.tanh(np.where(data["NEW_CREDIT_TO_GOODS_RATIO"]<0, data["INSTAL_DBD_MAX"],(( np.where(data["BURO_DAYS_CREDIT_ENDDATE_MEAN"]<0, np.where(data["WEEKDAY_APPR_PROCESS_START_WEDNESDAY"]>0, data["APPROVED_AMT_GOODS_PRICE_MAX"],(( data["PREV_NAME_PORTFOLIO_Cash_MEAN"])*(data["POS_NAME_CONTRACT_STATUS_Completed_MEAN"]))), data["NEW_RATIO_BURO_AMT_ANNUITY_MEAN"])) -(data["PREV_NAME_TYPE_SUITE_Children_MEAN"]))))
v["i147"] = 0.040002*np.tanh(np.where(data["APARTMENTS_AVG"]<0, np.where(data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]<0,(((( data["APPROVED_AMT_ANNUITY_MEAN"])*(data["OCCUPATION_TYPE_Laborers"])))/ 2.0), 1.0),(-1.0*(( np.where(data["NAME_FAMILY_STATUS_Married"]<0, data["BURO_STATUS_0_MEAN_MEAN"], data["OCCUPATION_TYPE_Laborers"]))))))
v["i148"] = 0.048200*np.tanh(np.where(data["PREV_NAME_SELLER_INDUSTRY_Clothing_MEAN"]>0, data["CC_CNT_DRAWINGS_POS_CURRENT_VAR"], np.where(data["INSTAL_DBD_MEAN"]>0, data["POS_COUNT"],(( data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"])*(((( np.where(data["REFUSED_AMT_GOODS_PRICE_MEAN"] < -99998, data["PREV_NAME_SELLER_INDUSTRY_Clothing_MEAN"], data["REFUSED_AMT_GOODS_PRICE_MEAN"])) >(data["POS_COUNT"])) *1.))))))
 v["i149"] = 0.047494*np.tanh(( -1.0*(( np.maximum(((((( data["APPROVED_HOUR_APPR_PROCESS_START_MIN"])<(np.where(data["INSTAL_AMT_PAYMENT_MIN"]<0, data["REFUSED_DAYS_DECISION_MIN"], np.where(data["REFUSED_AMT_ANNUITY_MIN"] < -99998, data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"], data["NONLIVINGAPARTMENTS_AVG"])))) *1.))) ,(( np.maximum(((data["ORGANIZATION_TYPE_Industry__type_9"])) ,(( data["PREV_CHANNEL_TYPE_Channel_of_corporate_sales_MEAN"]))))))))))
return Output(v.sum(axis=1)-2.432490 )<compute_test_metric>
MY_TRAIN_FILE = "/kaggle/input/cleaned-data/train.csv"
MY_TEST_FILE = "/kaggle/input/cleaned-data/test.csv"
roc_auc_score(train_df.TARGET,GP1(train_df))<compute_test_metric>
def cleanTweet(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)
 tweet = re.sub(r"I\x89Ûªm", "I am", tweet)
 tweet = re.sub(r"I'm", "I am", tweet)
 tweet = re.sub(r"Isn't", "is not", tweet)
 tweet = re.sub(r"Here's", "Here is", tweet)
 tweet = re.sub(r"you've", "you have", tweet)
 tweet = re.sub(r"you\x89Ûªve", "you have", tweet)
 tweet = re.sub(r"we're", "we are", tweet)
 tweet = re.sub(r"what's", "what is", tweet)
 tweet = re.sub(r"couldn't", "could not", tweet)
 tweet = re.sub(r"we've", "we have", tweet)
 tweet = re.sub(r"it\x89Ûªs", "it is", tweet)
 tweet = re.sub(r"doesn\x89Ûªt", "does not", tweet)
 tweet = re.sub(r"It\x89Ûªs", "It is", tweet)
 tweet = re.sub(r"Here\x89Ûªs", "Here is", tweet)
 tweet = re.sub(r"who's", "who is", tweet)
 tweet = re.sub(r"I\x89Ûªve", "I have", tweet)
 tweet = re.sub(r"y'all", "you all", tweet)
 tweet = re.sub(r"can\x89Ûªt", "cannot", tweet)
 tweet = re.sub(r"would've", "would have", tweet)
 tweet = re.sub(r"it'll", "it will", tweet)
 tweet = re.sub(r"we'll", "we will", tweet)
 tweet = re.sub(r"wouldn\x89Ûªt", "would not", tweet)
 tweet = re.sub(r"We've", "We have", tweet)
 tweet = re.sub(r"he'll", "he will", tweet)
 tweet = re.sub(r"Y'all", "You all", tweet)
 tweet = re.sub(r"Weren't", "Were not", tweet)
 tweet = re.sub(r"Didn't", "Did not", tweet)
 tweet = re.sub(r"they'll", "they will", tweet)
 tweet = re.sub(r"they'd", "they would", tweet)
 tweet = re.sub(r"DON'T", "DO NOT", tweet)
 tweet = re.sub(r"That\x89Ûªs", "That is", tweet)
 tweet = re.sub(r"they've", "they have", tweet)
 tweet = re.sub(r"i'd", "I would", tweet)
 tweet = re.sub(r"should've", "should have", tweet)
 tweet = re.sub(r"You\x89Ûªre", "You are", tweet)
 tweet = re.sub(r"where's", "where is", tweet)
 tweet = re.sub(r"Don\x89Ûªt", "Do not", tweet)
 tweet = re.sub(r"we'd", "we would", tweet)
 tweet = re.sub(r"i'll", "I will", tweet)
 tweet = re.sub(r"weren't", "were not", tweet)
 tweet = re.sub(r"They're", "They are", tweet)
 tweet = re.sub(r"Can\x89Ûªt", "Cannot", tweet)
 tweet = re.sub(r"you\x89Ûªll", "you will", tweet)
 tweet = re.sub(r"I\x89Ûªd", "I would", tweet)
 tweet = re.sub(r"let's", "let us", tweet)
 tweet = re.sub(r"it's", "it is", tweet)
 tweet = re.sub(r"can't", "cannot", tweet)
 tweet = re.sub(r"don't", "do not", tweet)
 tweet = re.sub(r"you're", "you are", tweet)
 tweet = re.sub(r"i've", "I have", tweet)
 tweet = re.sub(r"that's", "that is", tweet)
 tweet = re.sub(r"i'll", "I will", tweet)
 tweet = re.sub(r"doesn't", "does not", tweet)
 tweet = re.sub(r"i'd", "I would", tweet)
 tweet = re.sub(r"didn't", "did not", tweet)
 tweet = re.sub(r"ain't", "am not", tweet)
 tweet = re.sub(r"you'll", "you will", tweet)
 tweet = re.sub(r"I've", "I have", tweet)
 tweet = re.sub(r"Don't", "do not", tweet)
 tweet = re.sub(r"I'll", "I will", tweet)
 tweet = re.sub(r"I'd", "I would", tweet)
 tweet = re.sub(r"Let's", "Let us", tweet)
 tweet = re.sub(r"you'd", "You would", tweet)
 tweet = re.sub(r"It's", "It is", tweet)
 tweet = re.sub(r"Ain't", "am not", tweet)
 tweet = re.sub(r"Haven't", "Have not", tweet)
 tweet = re.sub(r"Could've", "Could have", tweet)
 tweet = re.sub(r"youve", "you have", tweet)
 tweet = re.sub(r"donå«t", "do not", tweet)
 tweet = re.sub(r"&gt;", ">", tweet)
 tweet = re.sub(r"&lt;", "<", tweet)
 tweet = re.sub(r"&amp;", "&", tweet)
 tweet = re.sub(r"w/e", "whatever", tweet)
 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
def cleanDFs() :
files = [TRAIN_FILE, TEST_FILE]
for file in files:
df = pd.read_csv(file)
 df['text'] = df.text.astype(str)
 df['text_cleaned'] = df['text'].apply(lambda s : cleanTweet(s))
if file != TEST_FILE:
df['target_relabeled'] = df['target'].copy()
df.loc[df['text'] == 'like for the music video I want some real action shit like burning buildings and police chases not some weak ben winston shit', 'target_relabeled'] = 0
df.loc[df['text'] == 'Hellfire is surrounded by desires so be careful and donÛªt let your desires control you!
df.loc[df['text'] == 'To fight bioterrorism sir.', 'target_relabeled'] = 0
df.loc[df['text'] == '.POTUS
df.loc[df['text'] == 'CLEARED:incident with injury:I-495 inner loop Exit 31 - MD 97/Georgia Ave Silver Spring', 'target_relabeled'] = 1
df.loc[df['text'] == '
df.loc[df['text'] == 'In
df.loc[df['text'] == 'Who is bringing the tornadoes and floods.Who is bringing the climate change.God is after America He is plaguing her
df.loc[df['text'] == 'RT NotExplained: The only known image of infamous hijacker D.B.Cooper.http://t.co/JlzK2HdeTG', 'target_relabeled'] = 1
df.loc[df['text'] == "Mmmmmm I'm burning.... I'm burning buildings I'm building.... Oooooohhhh oooh ooh...", 'target_relabeled'] = 0
df.loc[df['text'] == "wowo--=== 12000 Nigerian refugees repatriated from Cameroon", 'target_relabeled'] = 0
df.loc[df['text'] == "He came to a land which was engulfed in tribal war and turned it into a land of peace i.e.Madinah.
 df.loc[df['text'] == "Hellfire! We donÛªt even want to think about it or mention it so letÛªs not do anything that leads to it
df.loc[df['text'] == "The Prophet(peace be upon him)said 'Save yourself from Hellfire even if it is by giving half a date in charity.'", 'target_relabeled'] = 0
df.loc[df['text'] == "Caution: breathing may be hazardous to your health.", 'target_relabeled'] = 1
df.loc[df['text'] == "I Pledge Allegiance To The P.O.P.E.And The Burning Buildings of Epic City.??????", 'target_relabeled'] = 0
df.loc[df['text'] == "
df.loc[df['text'] == "that horrible sinking feeling when youÛªve been at home on your phone for a while and you realise its been on 3G this whole time", 'target_relabeled'] = 0
if file == TRAIN_FILE:
df.to_csv(MY_TRAIN_FILE, index=False)
 else:
df.to_csv(MY_TEST_FILE, index=False)
 return
roc_auc_score(train_df.TARGET,GP2(train_df))<save_to_csv>
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True )
x = test_df[['SK_ID_CURR']].copy()
x['TARGET'] =.5*GP1(test_df)+.5*GP2(test_df)
 x.to_csv('pure_submission.csv', index = False )<import_modules>
trainDF = pd.read_csv(MY_TRAIN_FILE)
 trainDF['text_cleaned'] = trainDF.text_cleaned.astype(str)
 trainDF['labels'] = trainDF.target_relabeled.astype(np.int64)
 trainDF = trainDF[['text_cleaned', 'labels']]
train_texts = list(trainDF.text_cleaned.values)
 train_labels = list(trainDF.labels.values )
import gc
import time
import numpy as np
import pandas as pd
from contextlib import contextmanager
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn.preprocessing import StandardScaler<categorify>
testDF = pd.read_csv(MY_TEST_FILE)
 testDF['id'] = testDF.id.astype(int)
 testDF['text_cleaned'] = testDF.text_cleaned.astype(str)
 testDF['labels'] = testDF.target.astype(np.int64)
 testDF = testDF[['id', 'text_cleaned', 'labels']]
test_texts = list(testDF.text_cleaned.values)
 test_labels = list(testDF.labels.values )
@contextmanager
def timer(title):
t0 = time.time()
yield
print("{} - done in {:.0f}s".format(title, time.time() - t0))
def one_hot_encoder(df, nan_as_category = True):
original_columns = list(df.columns)
 categorical_columns = [col for col in df.columns if df[col].dtype == 'object']
df = pd.get_dummies(df, columns= categorical_columns, dummy_na= nan_as_category)
 new_columns = [c for c in df.columns if c not in original_columns]
return df, new_columns
def application_train_test(num_rows = None, nan_as_category = False):
df = pd.read_csv('.. /input/application_train.csv', nrows= num_rows)
 test_df = pd.read_csv('.. /input/application_test.csv', nrows= num_rows)
 print("Train samples: {}, test samples: {}".format(len(df), len(test_df)))
 df = df.append(test_df ).reset_index()
df = df[df['CODE_GENDER'] != 'XNA']
docs = [_f for _f in df.columns if 'FLAG_DOC' in _f]
live = [_f for _f in df.columns if('FLAG_' in _f)&('FLAG_DOC' not in _f)&('_FLAG_' not in _f)]
df['DAYS_EMPLOYED'].replace(365243, np.nan, inplace= True)
 inc_by_org = df[['AMT_INCOME_TOTAL', 'ORGANIZATION_TYPE']].groupby('ORGANIZATION_TYPE' ).median() ['AMT_INCOME_TOTAL']
df['NEW_CREDIT_TO_ANNUITY_RATIO'] = df['AMT_CREDIT'] / df['AMT_ANNUITY']
df['NEW_CREDIT_TO_GOODS_RATIO'] = df['AMT_CREDIT'] / df['AMT_GOODS_PRICE']
df['NEW_DOC_IND_AVG'] = df[docs].mean(axis=1)
 df['NEW_DOC_IND_STD'] = df[docs].std(axis=1)
 df['NEW_DOC_IND_KURT'] = df[docs].kurtosis(axis=1)
 df['NEW_LIVE_IND_SUM'] = df[live].sum(axis=1)
 df['NEW_LIVE_IND_STD'] = df[live].std(axis=1)
 df['NEW_LIVE_IND_KURT'] = df[live].kurtosis(axis=1)
 df['NEW_INC_PER_CHLD'] = df['AMT_INCOME_TOTAL'] /(1 + df['CNT_CHILDREN'])
 df['NEW_INC_BY_ORG'] = df['ORGANIZATION_TYPE'].map(inc_by_org)
 df['NEW_EMPLOY_TO_BIRTH_RATIO'] = df['DAYS_EMPLOYED'] / df['DAYS_BIRTH']
df['NEW_ANNUITY_TO_INCOME_RATIO'] = df['AMT_ANNUITY'] /(1 + df['AMT_INCOME_TOTAL'])
 df['NEW_SOURCES_PROD'] = df['EXT_SOURCE_1'] * df['EXT_SOURCE_2'] * df['EXT_SOURCE_3']
df['NEW_EXT_SOURCES_MEAN'] = df[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].mean(axis=1)
 df['NEW_SCORES_STD'] = df[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].std(axis=1)
 df['NEW_SCORES_STD'] = df['NEW_SCORES_STD'].fillna(df['NEW_SCORES_STD'].mean())
 df['NEW_CAR_TO_BIRTH_RATIO'] = df['OWN_CAR_AGE'] / df['DAYS_BIRTH']
df['NEW_CAR_TO_EMPLOY_RATIO'] = df['OWN_CAR_AGE'] / df['DAYS_EMPLOYED']
df['NEW_PHONE_TO_BIRTH_RATIO'] = df['DAYS_LAST_PHONE_CHANGE'] / df['DAYS_BIRTH']
df['NEW_PHONE_TO_EMPLOY_RATIO'] = df['DAYS_LAST_PHONE_CHANGE'] / df['DAYS_EMPLOYED']
df['NEW_CREDIT_TO_INCOME_RATIO'] = df['AMT_CREDIT'] / df['AMT_INCOME_TOTAL']
for bin_feature in ['CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY']:
df[bin_feature], uniques = pd.factorize(df[bin_feature])
 df, cat_cols = one_hot_encoder(df, nan_as_category)
 del test_df
gc.collect()
return df
def bureau_and_balance(num_rows = None, nan_as_category = True):
bureau = pd.read_csv('.. /input/bureau.csv', nrows = num_rows)
 bb = pd.read_csv('.. /input/bureau_balance.csv', nrows = num_rows)
 bb, bb_cat = one_hot_encoder(bb, nan_as_category)
 bureau, bureau_cat = one_hot_encoder(bureau, nan_as_category)
 bb_aggregations = {'MONTHS_BALANCE': ['min', 'max', 'size']}
for col in bb_cat:
bb_aggregations[col] = ['mean']
bb_agg = bb.groupby('SK_ID_BUREAU' ).agg(bb_aggregations)
 bb_agg.columns = pd.Index([e[0] + "_" + e[1].upper() for e in bb_agg.columns.tolist() ])
 bureau = bureau.join(bb_agg, how='left', on='SK_ID_BUREAU')
 bureau.drop(['SK_ID_BUREAU'], axis=1, inplace= True)
 del bb, bb_agg
gc.collect()
num_aggregations = {
'DAYS_CREDIT': ['min', 'max', 'mean', 'var'],
'DAYS_CREDIT_ENDDATE': ['min', 'max', 'mean'],
'DAYS_CREDIT_UPDATE': ['mean'],
'CREDIT_DAY_OVERDUE': ['max', 'mean'],
'AMT_CREDIT_MAX_OVERDUE': ['mean'],
'AMT_CREDIT_SUM': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_DEBT': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_OVERDUE': ['mean'],
'AMT_CREDIT_SUM_LIMIT': ['mean', 'sum'],
'AMT_ANNUITY': ['max', 'mean'],
'CNT_CREDIT_PROLONG': ['sum'],
'MONTHS_BALANCE_MIN': ['min'],
'MONTHS_BALANCE_MAX': ['max'],
'MONTHS_BALANCE_SIZE': ['mean', 'sum']
}
cat_aggregations = {}
for cat in bureau_cat: cat_aggregations[cat] = ['mean']
for cat in bb_cat: cat_aggregations[cat + "_MEAN"] = ['mean']
bureau_agg = bureau.groupby('SK_ID_CURR' ).agg({**num_aggregations, **cat_aggregations})
 bureau_agg.columns = pd.Index(['BURO_' + e[0] + "_" + e[1].upper() for e in bureau_agg.columns.tolist() ])
 active = bureau[bureau['CREDIT_ACTIVE_Active'] == 1]
active_agg = active.groupby('SK_ID_CURR' ).agg(num_aggregations)
 cols = active_agg.columns.tolist()
active_agg.columns = pd.Index(['ACTIVE_' + e[0] + "_" + e[1].upper() for e in active_agg.columns.tolist() ])
 bureau_agg = bureau_agg.join(active_agg, how='left', on='SK_ID_CURR')
 del active, active_agg
gc.collect()
closed = bureau[bureau['CREDIT_ACTIVE_Closed'] == 1]
closed_agg = closed.groupby('SK_ID_CURR' ).agg(num_aggregations)
 closed_agg.columns = pd.Index(['CLOSED_' + e[0] + "_" + e[1].upper() for e in closed_agg.columns.tolist() ])
 bureau_agg = bureau_agg.join(closed_agg, how='left', on='SK_ID_CURR')
 for e in cols:
bureau_agg['NEW_RATIO_BURO_' + e[0] + "_" + e[1].upper() ] = bureau_agg['ACTIVE_' + e[0] + "_" + e[1].upper() ] / bureau_agg['CLOSED_' + e[0] + "_" + e[1].upper() ]
del closed, closed_agg, bureau
gc.collect()
return bureau_agg
def previous_applications(num_rows = None, nan_as_category = True):
prev = pd.read_csv('.. /input/previous_application.csv', nrows = num_rows)
 prev, cat_cols = one_hot_encoder(prev, nan_as_category= True)
 prev['DAYS_FIRST_DRAWING'].replace(365243, np.nan, inplace= True)
 prev['DAYS_FIRST_DUE'].replace(365243, np.nan, inplace= True)
 prev['DAYS_LAST_DUE_1ST_VERSION'].replace(365243, np.nan, inplace= True)
 prev['DAYS_LAST_DUE'].replace(365243, np.nan, inplace= True)
 prev['DAYS_TERMINATION'].replace(365243, np.nan, inplace= True)
 prev['APP_CREDIT_PERC'] = prev['AMT_APPLICATION'] / prev['AMT_CREDIT']
num_aggregations = {
'AMT_ANNUITY': ['min', 'max', 'mean'],
'AMT_APPLICATION': ['min', 'max', 'mean'],
'AMT_CREDIT': ['min', 'max', 'mean'],
'APP_CREDIT_PERC': ['min', 'max', 'mean', 'var'],
'AMT_DOWN_PAYMENT': ['min', 'max', 'mean'],
'AMT_GOODS_PRICE': ['min', 'max', 'mean'],
'HOUR_APPR_PROCESS_START': ['min', 'max', 'mean'],
'RATE_DOWN_PAYMENT': ['min', 'max', 'mean'],
'DAYS_DECISION': ['min', 'max', 'mean'],
'CNT_PAYMENT': ['mean', 'sum'],
}
cat_aggregations = {}
for cat in cat_cols:
cat_aggregations[cat] = ['mean']
prev_agg = prev.groupby('SK_ID_CURR' ).agg({**num_aggregations, **cat_aggregations})
 prev_agg.columns = pd.Index(['PREV_' + e[0] + "_" + e[1].upper() for e in prev_agg.columns.tolist() ])
 approved = prev[prev['NAME_CONTRACT_STATUS_Approved'] == 1]
approved_agg = approved.groupby('SK_ID_CURR' ).agg(num_aggregations)
 cols = approved_agg.columns.tolist()
approved_agg.columns = pd.Index(['APPROVED_' + e[0] + "_" + e[1].upper() for e in approved_agg.columns.tolist() ])
 prev_agg = prev_agg.join(approved_agg, how='left', on='SK_ID_CURR')
 refused = prev[prev['NAME_CONTRACT_STATUS_Refused'] == 1]
refused_agg = refused.groupby('SK_ID_CURR' ).agg(num_aggregations)
 refused_agg.columns = pd.Index(['REFUSED_' + e[0] + "_" + e[1].upper() for e in refused_agg.columns.tolist() ])
 prev_agg = prev_agg.join(refused_agg, how='left', on='SK_ID_CURR')
 del refused, refused_agg, approved, approved_agg, prev
for e in cols:
prev_agg['NEW_RATIO_PREV_' + e[0] + "_" + e[1].upper() ] = prev_agg['APPROVED_' + e[0] + "_" + e[1].upper() ] / prev_agg['REFUSED_' + e[0] + "_" + e[1].upper() ]
gc.collect()
return prev_agg
def pos_cash(num_rows = None, nan_as_category = True):
pos = pd.read_csv('.. /input/POS_CASH_balance.csv', nrows = num_rows)
 pos, cat_cols = one_hot_encoder(pos, nan_as_category= True)
 aggregations = {
'MONTHS_BALANCE': ['max', 'mean', 'size'],
'SK_DPD': ['max', 'mean'],
'SK_DPD_DEF': ['max', 'mean']
}
for cat in cat_cols:
aggregations[cat] = ['mean']
pos_agg = pos.groupby('SK_ID_CURR' ).agg(aggregations)
 pos_agg.columns = pd.Index(['POS_' + e[0] + "_" + e[1].upper() for e in pos_agg.columns.tolist() ])
 pos_agg['POS_COUNT'] = pos.groupby('SK_ID_CURR' ).size()
del pos
gc.collect()
return pos_agg
def installments_payments(num_rows = None, nan_as_category = True):
ins = pd.read_csv('.. /input/installments_payments.csv', nrows = num_rows)
 ins, cat_cols = one_hot_encoder(ins, nan_as_category= True)
 ins['PAYMENT_PERC'] = ins['AMT_PAYMENT'] / ins['AMT_INSTALMENT']
ins['PAYMENT_DIFF'] = ins['AMT_INSTALMENT'] - ins['AMT_PAYMENT']
ins['DPD'] = ins['DAYS_ENTRY_PAYMENT'] - ins['DAYS_INSTALMENT']
ins['DBD'] = ins['DAYS_INSTALMENT'] - ins['DAYS_ENTRY_PAYMENT']
ins['DPD'] = ins['DPD'].apply(lambda x: x if x > 0 else 0)
 ins['DBD'] = ins['DBD'].apply(lambda x: x if x > 0 else 0)
 aggregations = {
'NUM_INSTALMENT_VERSION': ['nunique'],
'DPD': ['max', 'mean', 'sum'],
'DBD': ['max', 'mean', 'sum'],
'PAYMENT_PERC': ['max', 'mean', 'sum', 'var'],
'PAYMENT_DIFF': ['max', 'mean', 'sum', 'var'],
'AMT_INSTALMENT': ['max', 'mean', 'sum'],
'AMT_PAYMENT': ['min', 'max', 'mean', 'sum'],
'DAYS_ENTRY_PAYMENT': ['max', 'mean', 'sum']
}
for cat in cat_cols:
aggregations[cat] = ['mean']
ins_agg = ins.groupby('SK_ID_CURR' ).agg(aggregations)
 ins_agg.columns = pd.Index(['INSTAL_' + e[0] + "_" + e[1].upper() for e in ins_agg.columns.tolist() ])
 ins_agg['INSTAL_COUNT'] = ins.groupby('SK_ID_CURR' ).size()
del ins
gc.collect()
return ins_agg
def credit_card_balance(num_rows = None, nan_as_category = True):
cc = pd.read_csv('.. /input/credit_card_balance.csv', nrows = num_rows)
 cc, cat_cols = one_hot_encoder(cc, nan_as_category= True)
 cc.drop(['SK_ID_PREV'], axis= 1, inplace = True)
 cc_agg = cc.groupby('SK_ID_CURR' ).agg(['min', 'max', 'mean', 'sum', 'var'])
 cc_agg.columns = pd.Index(['CC_' + e[0] + "_" + e[1].upper() for e in cc_agg.columns.tolist() ])
 cc_agg['CC_COUNT'] = cc.groupby('SK_ID_CURR' ).size()
del cc
gc.collect()
return cc_agg<categorify>
sentences = trainDF.text_cleaned.values
print(' Original: ', sentences[0])
 print('Tokenized: ', tokenizer.tokenize(sentences[0]))
print('Token IDs: ', tokenizer.convert_tokens_to_ids(tokenizer.tokenize(sentences[0])) )
warnings.simplefilter(action='ignore', category=FutureWarning)
 debug = None
num_rows = 10000 if debug else None
df = application_train_test(num_rows)
 with timer("Process bureau and bureau_balance"):
bureau = bureau_and_balance(num_rows)
 print("Bureau df shape:", bureau.shape)
 df = df.join(bureau, how='left', on='SK_ID_CURR')
 del bureau
gc.collect()
with timer("Process previous_applications"):
prev = previous_applications(num_rows)
 print("Previous applications df shape:", prev.shape)
 df = df.join(prev, how='left', on='SK_ID_CURR')
 del prev
gc.collect()
with timer("Process POS-CASH balance"):
pos = pos_cash(num_rows)
 print("Pos-cash balance df shape:", pos.shape)
 df = df.join(pos, how='left', on='SK_ID_CURR')
 del pos
gc.collect()
with timer("Process installments payments"):
ins = installments_payments(num_rows)
 print("Installments payments df shape:", ins.shape)
 df = df.join(ins, how='left', on='SK_ID_CURR')
 del ins
gc.collect()
with timer("Process credit card balance"):
cc = credit_card_balance(num_rows)
 print("Credit card balance df shape:", cc.shape)
 df = df.join(cc, how='left', on='SK_ID_CURR')
 del cc
gc.collect()<normalization>
max_len = 0
for sent in sentences:
input_ids = tokenizer.encode(sent, add_special_tokens=True)
 max_len = max(max_len, len(input_ids))
print('Max sentence length: ', max_len )
feats = [f for f in df.columns if f not in ['TARGET','SK_ID_CURR','SK_ID_BUREAU','SK_ID_PREV','index']]
for c in feats:
ss = StandardScaler()
df.loc[~np.isfinite(df[c]),c] = np.nan
df.loc[~df[c].isnull() ,c] = ss.fit_transform(df.loc[~df[c].isnull() ,c].values.reshape(-1,1))
df[c].fillna(-99999.,inplace=True )<feature_engineering>
train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.3)
 train_encodings = tokenizer(train_texts, add_special_tokens = True, max_length = max_len, truncation=True, padding=True)
 val_encodings = tokenizer(val_texts, add_special_tokens = True, max_length = max_len, truncation=True, padding=True)
 test_encodings = tokenizer(test_texts, add_special_tokens = True, max_length = max_len, truncation=True, padding=True )