Kaggle competition: Home Credit Default Risk
<SOS> metric: AUC Kaggle data source: home-credit-default-risk<save_to_csv>
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
df = pd.DataFrame()
df['filename'] = filenames
df['label'] = predictions
df = df.sort_values(by='filename')
 df.to_csv('results.csv', header=True, index=False )<import_modules>
@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
import pandas as pd
import numpy as np
import cv2
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sys import getsizeof<load_from_csv>
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()
train = pd.read_csv('.. /input/ava/train.csv', index_col=0)
 test = pd.read_csv('.. /input/ava/test.csv', index_col=0)
 train<prepare_x_and_y>
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 )
X_train = train['image'].values
y_train = train['label'].values
X_test = test['image'].values<normalization>
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 )
shapes = []
for i in range(len(X_train)) :
path = '.. /input/ava/dataset/dataset/'+str(X_train[i])+'.jpg'
img = cv2.imread(path)
 shapes.append(img.shape)
 shapes = np.array(shapes[:])
 print(np.mean(shapes[:,0]), np.mean(shapes[:,1]), np.mean(shapes[:,2]))<normalization>
roc_auc_score(train_df.TARGET,GP1(train_df))
def get_feature(img):
img = cv2.resize(img,(32, 32), interpolation = cv2.INTER_AREA)
 return np.array(img ).flatten()<feature_engineering>
roc_auc_score(train_df.TARGET,GP2(train_df))
<train_model><EOS> 
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 )