Kaggle competition: COVID19 Global Forecasting (Week 2)
def get_model(config: dict, weights_path: str):
model = ResNet(**config)
 checkpoint = torch.load(weights_path)
 model.load_state_dict(checkpoint["model_state_dict"])
 device = torch.device("cuda")
 model.to(device)
 model.eval()
return model<create_dataframe>
import plotly.graph_objects as go
import matplotlib.pyplot as plt
from tqdm import tqdm
import time
from datetime import datetime
from pathlib import Path
from sklearn import preprocessing
import keras.backend as K
from keras.models import Sequential
from keras.layers import Dense, LSTM, RNN, Dropout
from keras.callbacks import EarlyStopping
from keras import optimizers
from sklearn.preprocessing import StandardScaler, MinMaxScaler
def prediction_for_clip(test_df: pd.DataFrame,
clip: np.ndarray,
model: ResNet,
mel_params: dict,
threshold=0.5):
dataset = TestDataset(df=test_df,
clip=clip,
img_size=224,
melspectrogram_parameters=mel_params)
 loader = data.DataLoader(dataset, batch_size=1, shuffle=False)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.eval()
prediction_dict = {}
for image, row_id, site in progress_bar(loader):
site = site[0]
row_id = row_id[0]
if site in {"site_1", "site_2"}:
image = image.to(device)
 with torch.no_grad() :
prediction = model(image)
 proba = prediction["multilabel_proba"].detach().cpu().numpy().reshape(-1)
 events = proba >= threshold
labels = np.argwhere(events ).reshape(-1 ).tolist()
else:
image = image.squeeze(0)
 batch_size = 16
whole_size = image.size(0)
 if whole_size % batch_size == 0:
n_iter = whole_size // batch_size
else:
n_iter = whole_size // batch_size + 1
all_events = set()
for batch_i in range(n_iter):
batch = image[batch_i * batch_size:(batch_i + 1)* batch_size]
if batch.ndim == 3:
batch = batch.unsqueeze(0)
 batch = batch.to(device)
 with torch.no_grad() :
prediction = model(batch)
 proba = prediction["multilabel_proba"].detach().cpu().numpy()
events = proba >= threshold
for i in range(len(events)) :
event = events[i, :]
labels = np.argwhere(event ).reshape(-1 ).tolist()
for label in labels:
all_events.add(label)
 labels = list(all_events)
 if len(labels)== 0:
prediction_dict[row_id] = "nocall"
else:
labels_str_list = list(map(lambda x: INV_BIRD_CODE[x], labels))
label_string = " ".join(labels_str_list)
 prediction_dict[row_id] = label_string
return prediction_dict<load_pretrained>
train = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-2/train.csv")
 test = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-2/test.csv")
 train.tail()
def prediction(test_df: pd.DataFrame,
test_audio: Path,
model_config: dict,
mel_params: dict,
weights_path: str,
threshold=0.5):
model = get_model(model_config, weights_path)
 unique_audio_id = test_df.audio_id.unique()
warnings.filterwarnings("ignore")
 prediction_dfs = []
for audio_id in unique_audio_id:
with timer(f"Loading {audio_id}", logger):
clip, _ = librosa.load(test_audio /(audio_id + ".mp3"),
sr=TARGET_SR,
mono=True,
res_type="kaiser_fast")
 test_df_for_audio_id = test_df.query(
 f"audio_id == '{audio_id}'" ).reset_index(drop=True)
 with timer(f"Prediction on {audio_id}", logger):
prediction_dict = prediction_for_clip(test_df_for_audio_id,
clip=clip,
model=model,
mel_params=mel_params,
threshold=threshold)
 row_id = list(prediction_dict.keys())
 birds = list(prediction_dict.values())
 prediction_df = pd.DataFrame({
"row_id": row_id,
"birds": birds
})
 prediction_dfs.append(prediction_df)
 prediction_df = pd.concat(prediction_dfs, axis=0, sort=False ).reset_index(drop=True)
 return prediction_df<save_to_csv>
mask = train['Date'].max()
world_cum_confirmed = sum(train[train['Date'] == mask].ConfirmedCases)
 world_cum_fatal = sum(train[train['Date'] == mask].Fatalities )
submission = prediction(test_df=test,
test_audio=test_audio,
model_config=model_config,
mel_params=melspectrogram_parameters,
weights_path=weights_path,
threshold=0.8)
 submission.to_csv("submission.csv", index=False )<randomize_order>
print('Number of Countires are: ', len(train['Country_Region'].unique()))
print('Training dataset ends at: ', mask)
 print('Number of cumulative confirmed cases worldwide are: ', world_cum_confirmed)
 print('Number of cumulative fatal cases worldwide are: ', world_cum_fatal )
def random_sampler(filename, k):
sample = []
with open(filename, 'rb')as f:
f.seek(0, 2)
 filesize = f.tell()
random_set = sorted(random.sample(range(filesize), k))
for i in range(k):
f.seek(random_set[i])
 f.readline()
sample.append(f.readline().rstrip())
 return sample<randomize_order>
cum_per_country = train[train['Date'] == mask].groupby(['Date','Country_Region'] ).sum().sort_values(['ConfirmedCases'], ascending=False)
 cum_per_country[:10]
TRAIN_SAMPLES = 5*10**6
train_sample = random_sampler('.. /input/train.csv', TRAIN_SAMPLES )<string_transform>
date = train['Date'].unique()
cc_us = train[train['Country_Region'] == 'US'].groupby(['Date'] ).sum().ConfirmedCases
ft_us = train[train['Country_Region'] == 'US'].groupby(['Date'] ).sum().Fatalities
cc_ity = train[train['Country_Region'] == 'Italy'].groupby(['Date'] ).sum().ConfirmedCases
ft_ity = train[train['Country_Region'] == 'Italy'].groupby(['Date'] ).sum().Fatalities
cc_spn = train[train['Country_Region'] == 'Spain'].groupby(['Date'] ).sum().ConfirmedCases
ft_spn = train[train['Country_Region'] == 'Spain'].groupby(['Date'] ).sum().Fatalities
cc_gmn = train[train['Country_Region'] == 'Germany'].groupby(['Date'] ).sum().ConfirmedCases
ft_gmn = train[train['Country_Region'] == 'Germany'].groupby(['Date'] ).sum().Fatalities
cc_frc = train[train['Country_Region'] == 'France'].groupby(['Date'] ).sum().ConfirmedCases
ft_frc = train[train['Country_Region'] == 'France'].groupby(['Date'] ).sum().Fatalities
fig = go.Figure()
fig.add_trace(go.Scatter(x=date, y=cc_us, name='US'))
fig.add_trace(go.Scatter(x=date, y=cc_ity, name='Italy'))
fig.add_trace(go.Scatter(x=date, y=cc_spn, name='Spain'))
fig.add_trace(go.Scatter(x=date, y=cc_gmn, name='Germany'))
fig.add_trace(go.Scatter(x=date, y=cc_frc, name='France'))
fig.update_layout(title="Plot of Cumulative Cases for Top 5 countires(except China)",
xaxis_title="Date",
yaxis_title="Cases")
 fig.update_xaxes(nticks=30)
 fig.show()
train_sample[0].decode().split(',' )<string_transform>
train['Date'] = pd.to_datetime(train['Date'])
 test['Date'] = pd.to_datetime(test['Date'])
 train['Country_Region'] = train['Country_Region'].astype(str)
 test['Country_Region'] = test['Country_Region'].astype(str)
train_sample_ = [row.decode().split(",")for row in train_sample]<create_dataframe>
EMPTY_VAL = "EMPTY_VAL"
def fillState(state, country):
if state == EMPTY_VAL: return country
return state
train['Province_State'].fillna(EMPTY_VAL, inplace=True)
 train['Province_State'] = train.loc[:, ['Province_State', 'Country_Region']].apply(lambda x : fillState(x['Province_State'], x['Country_Region']), axis=1)
 test['Province_State'].fillna(EMPTY_VAL, inplace=True)
 test['Province_State'] = test.loc[:, ['Province_State', 'Country_Region']].apply(lambda x : fillState(x['Province_State'], x['Country_Region']), axis=1 )
train = pd.DataFrame(train_sample_ )<drop_column>
le = preprocessing.LabelEncoder()
train['country_encoder'] = le.fit_transform(train['Country_Region'])
 train['date_int'] = train['Date'].apply(lambda x: datetime.strftime(x, '%m%d')).astype(int)
 test['country_encoder'] = le.transform(test['Country_Region'])
 test['date_int'] = test['Date'].apply(lambda x: datetime.strftime(x, '%m%d')).astype(int )
del train_sample
del train_sample_<load_from_csv>
le = preprocessing.LabelEncoder()
train['province_encoder'] = le.fit_transform(train['Province_State'])
 test['province_encoder'] = le.transform(test['Province_State'] )
train_df = pd.read_csv('.. /input/train.csv', nrows=1)
 train.columns = train_df.columns<data_type_conversions>
train = train.apply(pd.to_numeric )<feature_engineering>
train_df = pd.read_csv(Path('/kaggle/input/covid19w2', 'train_df.csv'), index_col = 0, parse_dates = ['date'])
 train_df = train_df[train_df['date_int']>=301]
train_df['weekday'] = train_df['date'].dt.weekday
train_df[train_df['country'] == 'Italy']
train['Demanda_uni_equil'] = np.log1p(train['Demanda_uni_equil'] )<drop_column>
def split_train_val(df, val_ratio):
val_len = int(len(df)* val_ratio)
 train_set = df[:-val_len]
val_set = df[-val_len:]
return train_set, val_set
x_cols = train.columns
x_cols = x_cols.drop(['Venta_uni_hoy', 'Venta_hoy', 'Dev_uni_proxima', 'Dev_proxima', 'Demanda_uni_equil'])
 print(x_cols )<choose_model_class>
test_fixed_cols = ['ForecastId', 'Province_State', 'Country_Region', 'Date']
fixed_cols = ['Id', 'province', 'country', 'date']
output_cols = ['cc_cases', 'ft_cases']
input_cols = list(set(train_df.columns.to_list())- set(fixed_cols)- set(output_cols))
print('output columns are ', output_cols)
 print('input columns are ', input_cols)
 X = train_df[input_cols]
y = train_df[output_cols]
model = RandomForestRegressor()<train_model>
cc_input = ['country_encoder', 'province_encoder', 'weekday', 'date_int','cc_cases_1', 'cc_cases_2', 'cc_cases_3', 'cc_cases_4', 'cc_cases_5', 'cc_cases_6', 'cc_cases_7']
ft_input = ['country_encoder', 'province_encoder', 'weekday' , 'date_int', 'ft_cases_1', 'ft_cases_2', 'ft_cases_3', 'ft_cases_4', 'ft_cases_5', 'ft_cases_6', 'ft_cases_7']
cc_output = ['cc_cases']
ft_output = ['ft_cases']
val_ratio = 0.05
X_cc = X[cc_input]
X_ft = X[ft_input]
y_cc = y[cc_output]
y_ft = y[ft_output]
train_X_cc, val_X_cc = split_train_val(df = X_cc, val_ratio = val_ratio)
 train_y_cc, val_y_cc = split_train_val(df = y_cc, val_ratio = val_ratio)
 train_X_ft, val_X_ft = split_train_val(df = X_ft, val_ratio = val_ratio)
 train_y_ft, val_y_ft = split_train_val(df = y_ft, val_ratio = val_ratio )
model.fit(train[x_cols], train['Demanda_uni_equil'] )<load_from_csv>
test = pd.read_csv('.. /input/test.csv')
 test.info()<predict_on_test>
test['Demanda_uni_equil'] = np.expm1(model.predict(test[x_cols]))<save_to_csv>
X_train_cc = train_X_cc.to_numpy()
X_val_cc = val_X_cc.to_numpy()
X_train_ft = train_X_ft.to_numpy()
X_val_ft = val_X_ft.to_numpy()
y_train_cc = train_y_cc.to_numpy()
y_val_cc = val_y_cc.to_numpy()
y_train_ft = train_y_ft.to_numpy()
y_val_ft = val_y_ft.to_numpy()
test[['id', 'Demanda_uni_equil']].to_csv('predictions_rf_random_sampling.csv', index=False )<load_from_csv>
def root_mean_squared_log_error(y_true, y_pred):
return K.sqrt(K.mean(K.square(K.log(y_pred + 1)- K.log(y_true + 1))))
data1 = pd.read_csv('.. /input/lightgbm-with-simple-features-0-785-lb/submission_kernel00.csv')
 data2 = pd.read_csv('.. /input/tidy-xgb-all-tables-0-782/tidy_xgb_0.77821.csv' )<feature_engineering>
def LSTM_model(n_1, input_dim, output_dim):
model = Sequential()
model.add(LSTM(n_1,input_shape=(1, input_dim), activation='relu'))
model.add(Dense(output_dim, activation='relu'))
model.compile(loss=root_mean_squared_log_error, optimizer='adam')
 return model
data1['TARGET'] =(data1['TARGET']+data2['TARGET'])/2<save_to_csv>
K.clear_session()
model_cc = LSTM_model(4, X_train_cc.shape[-1], y_train_cc.shape[-1])
 model_ft = LSTM_model(4, X_train_ft.shape[-1], y_train_ft.shape[-1])
 early_stop_cc = EarlyStopping(monitor='val_loss', patience=5, verbose=0, mode='min')
 early_stop_ft = EarlyStopping(monitor='val_loss', patience=5, verbose=0, mode='min' )
data1.to_csv('blend1_.788lb.csv',index = False )<import_modules>
print('Start model training')
 start_time = time.time()
history_cc = model_cc.fit(X_train_cc, y_train_cc, batch_size = 16, epochs = 100,validation_data =(X_val_cc, y_val_cc), verbose = 2, callbacks=[early_stop_cc])
 model_cc.save("model_cc.h5")
 print('Time spent for model training is {} minutes'.format(round(( time.time() -start_time)/60,1)) )
import gc
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.preprocessing import StandardScaler,LabelEncoder
from sklearn.metrics import roc_auc_score<categorify>
print('Start model training')
 start_time = time.time()
history_ft = model_ft.fit(X_train_ft, y_train_ft, batch_size = 16, epochs = 8,validation_data =(X_val_ft, y_val_ft), verbose = 2, callbacks=[early_stop_ft])
 model_ft.save("model_ft.h5")
 print('Time spent for model training is {} minutes'.format(round(( time.time() -start_time)/60,1)) )
def add_noise(series, noise_level):
return series *(1 + noise_level * np.random.randn(len(series)))
 def target_encode(trn_series=None,
tst_series=None,
target=None,
min_samples_leaf=1,
smoothing=1,
noise_level=0):

 assert len(trn_series)== len(target)
 assert trn_series.name == tst_series.name
temp = pd.concat([trn_series, target], axis=1)
 averages = temp.groupby(by=trn_series.name)[target.name].agg(["mean", "count"])
 smoothing = 1 /(1 + np.exp(-(averages["count"] - min_samples_leaf)/ smoothing))
prior = target.mean()
averages[target.name] = prior *(1 - smoothing)+ averages["mean"] * smoothing
averages.drop(["mean", "count"], axis=1, inplace=True)
 ft_trn_series = pd.merge(
 trn_series.to_frame(trn_series.name),
averages.reset_index().rename(columns={'index': target.name, target.name: 'average'}),
on=trn_series.name,
how='left')['average'].rename(trn_series.name + '_mean' ).fillna(prior)
 ft_trn_series.index = trn_series.index
ft_tst_series = pd.merge(
 tst_series.to_frame(tst_series.name),
averages.reset_index().rename(columns={'index': target.name, target.name: 'average'}),
on=tst_series.name,
how='left')['average'].rename(trn_series.name + '_mean' ).fillna(prior)
 ft_tst_series.index = tst_series.index
return add_noise(ft_trn_series, noise_level), add_noise(ft_tst_series, noise_level )<feature_engineering>
yhat_val_cc = model_cc.predict(X_val_cc)
 print(yhat_val_cc[50:70] )
def UseGPFeatures(data):
v = pd.DataFrame()
v["i0"] = np.tanh(((((( -1.0*(((( np.maximum(((data["EXT_SOURCE_2"])) ,(( data["EXT_SOURCE_3"])))) -(data["te_OCCUPATION_TYPE"])))))) -(((((((data["EXT_SOURCE_2"])+(data["EXT_SOURCE_3"])))* 2.0)) +(data["EXT_SOURCE_3"])))))* 2.0))
v["i1"] = np.tanh(((((data["te_OCCUPATION_TYPE"])+(((((data["NAME_CONTRACT_STATUS_Refused"])-(((((data["EXT_SOURCE_3"])* 2.0)) * 2.0)))) +(((data["te_ORGANIZATION_TYPE"])-(((((data["EXT_SOURCE_1"])* 2.0)) * 2.0)))))))) * 2.0))
v["i2"] = np.tanh(((((((data["te_OCCUPATION_TYPE"])-(0.686567)))-(((((((data["EXT_SOURCE_3"])+(data["EXT_SOURCE_1"])))+(data["EXT_SOURCE_2"])))* 2.0)))) * 2.0))
v["i3"] = np.tanh(((((((((((data["NAME_CONTRACT_STATUS_Refused"])+(((data["te_OCCUPATION_TYPE"])-(data["EXT_SOURCE_2"])))))-(0.686567)))-(data["EXT_SOURCE_3"])))* 2.0)) -(((data["EXT_SOURCE_2"])+(data["EXT_SOURCE_3"])))))
 v["i4"] = np.tanh(((((((data["DAYS_CREDIT"])-(data["EXT_SOURCE_3"])))-(data["EXT_SOURCE_3"])))+(((np.where(data["EXT_SOURCE_1"]>0, -3.0, data["DAYS_BIRTH"])) -(data["EXT_SOURCE_1"])))))
 v["i5"] = np.tanh(((((data["NAME_CONTRACT_STATUS_Refused"])+(((((data["cc_bal_CNT_DRAWINGS_ATM_CURRENT"])+(((data["te_NAME_EDUCATION_TYPE"])+(((data["cc_bal_CNT_DRAWINGS_ATM_CURRENT"])-(((((( data["EXT_SOURCE_2"])* 2.0)) +(data["EXT_SOURCE_2"])) /2.0)))))))) * 2.0)))) * 2.0))
v["i6"] = np.tanh(((data["cc_bal_CNT_DRAWINGS_ATM_CURRENT"])+(((((((((( data["NAME_PRODUCT_TYPE_walk_in"])-(data["EXT_SOURCE_1"])))+(np.where(data["EXT_SOURCE_1"]>0, -2.0, data["te_ORGANIZATION_TYPE"])))/2.0)) -(data["EXT_SOURCE_3"])))* 2.0))))
v["i7"] = np.tanh(( -1.0*(((((((( data["EXT_SOURCE_2"])+(((data["DAYS_FIRST_DRAWING"])+(data["EXT_SOURCE_3"])))))* 2.0)) +(((data["EXT_SOURCE_2"])+(((0.697674)+(data["EXT_SOURCE_1"]))))))))))
v["i8"] = np.tanh(((data["REGION_RATING_CLIENT_W_CITY"])+(((((((((((data["cc_bal_CNT_DRAWINGS_ATM_CURRENT"])+(data["te_NAME_EDUCATION_TYPE"])))-(data["EXT_SOURCE_1"])))-(data["EXT_SOURCE_3"])))-(data["EXT_SOURCE_2"])))* 2.0))))
v["i9"] = np.tanh(((data["NAME_CONTRACT_STATUS_Refused"])+(((((((((((((data["cc_bal_AMT_BALANCE"])-(data["EXT_SOURCE_2"])))-(data["CODE_GENDER"])))+(data["cc_bal_AMT_BALANCE"])))-(0.416667)))* 2.0)) -(data["EXT_SOURCE_3"])))))
 v["i10"] = np.tanh(((((((((((data["ty__Microloan"])* 2.0)) *(((14.800000)/ 2.0)))) -(data["EXT_SOURCE_2"])))+(((data["FLAG_DOCUMENT_3"])-(data["EXT_SOURCE_3"])))))+(data["CNT_INSTALMENT_FUTURE"])))
 v["i11"] = np.tanh(((((data["te_ORGANIZATION_TYPE"])-(np.where(((( data["EXT_SOURCE_1"])+(data["NAME_CONTRACT_TYPE"])) /2.0)>0, 3.0, data["EXT_SOURCE_1"])))) +(((data["NAME_YIELD_GROUP_high"])-(data["EXT_SOURCE_2"])))))
 v["i12"] = np.tanh(((((((data["DEF_30_CNT_SOCIAL_CIRCLE"])+(((data["te_FLAG_OWN_CAR"])+(((((data["cc_bal_CNT_DRAWINGS_ATM_CURRENT"])-(((data["SK_ID_PREV_y"])* 2.0)))) +(data["CNT_INSTALMENT_FUTURE"])))))))-(data["EXT_SOURCE_3"])))+(data["CNT_INSTALMENT_FUTURE"])))
 v["i13"] = np.tanh(((data["te_CODE_GENDER"])+(((np.maximum(((data["NAME_PRODUCT_TYPE_walk_in"])) ,(((( data["REG_CITY_NOT_LIVE_CITY"])+(data["cc_bal_CNT_DRAWINGS_ATM_CURRENT"])))))) +(((((((data["cc_bal_CNT_DRAWINGS_CURRENT"])+(((data["ty__Microloan"])* 2.0)))) * 2.0)) * 2.0))))))
v["i14"] = np.tanh(((((((((((((((data["ty__Microloan"])+(data["te_NAME_EDUCATION_TYPE"])) /2.0)) +(data["te_NAME_CONTRACT_TYPE"])) /2.0)) -(data["AMT_ANNUITY"])))+(data["CNT_PAYMENT"])))* 2.0)) -(((data["FLAG_OWN_CAR"])+(data["AMT_ANNUITY"])))))
 v["i15"] = np.tanh(((((((((( data["DEF_30_CNT_SOCIAL_CIRCLE"])-(data["DAYS_FIRST_DRAWING"])))+(((data["DAYS_ID_PUBLISH"])+(((data["cc_bal_AMT_TOTAL_RECEIVABLE"])* 2.0)))))/2.0)) -(np.tanh(((( data["ca__Closed"])* 2.0)))))) +(data["cc_bal_AMT_BALANCE"])))
 v["i16"] = np.tanh(np.where(data["NAME_YIELD_GROUP_low_action"]>0,(( data["NAME_CONTRACT_STATUS_Refused"])-(3.0)) ,(((( data["REGION_RATING_CLIENT_W_CITY"])+(((data["avg_buro_buro_bal_status_1"])+(((( data["NAME_YIELD_GROUP_high"])+(data["NAME_CONTRACT_STATUS_Refused"])) /2.0)))))) +(data["avg_buro_buro_bal_status_1"]))))
v["i17"] = np.tanh(((np.maximum(((np.maximum(((data["CNT_PAYMENT"])) ,(( data["te_NAME_FAMILY_STATUS"]))))),(( data["NAME_CLIENT_TYPE_New"])))) +(((np.minimum(((data["AMT_ANNUITY_x"])) ,(((( np.maximum(((data["ty__Microloan"])) ,(( data["CNT_PAYMENT"])))) -(data["RATE_DOWN_PAYMENT"])))))) * 2.0))))
v["i18"] = np.tanh(((((((((((((( data["ty__Microloan"])*(11.714300)))-(data["ty__Mortgage"])))+(data["te_WALLSMATERIAL_MODE"])) /2.0)) -(data["inst_NUM_INSTALMENT_VERSION"])))-(data["PRODUCT_COMBINATION_POS_industry_with_interest"])))-(((data["NAME_GOODS_CATEGORY_Furniture"])* 2.0))))
v["i19"] = np.tanh(((((data["SK_ID_PREV_y"])+(-3.0)))*(((((data["SK_ID_PREV_y"])-(data["cc_bal_CNT_DRAWINGS_ATM_CURRENT"])))+(((0.797872)+(data["NAME_YIELD_GROUP_low_action"])))))))
 v["i20"] = np.tanh(((((data["avg_buro_buro_bal_status_1"])+(((data["avg_buro_buro_bal_status_1"])+(((data["PRODUCT_COMBINATION_Cash_X_Sell__high"])+(((((((( data["ty__Microloan"])* 2.0)) * 2.0)) +(((data["CNT_INSTALMENT_FUTURE"])*(data["CNT_INSTALMENT_FUTURE"])))) /2.0)))))))) * 2.0))
v["i21"] = np.tanh(((data["DAYS_LAST_PHONE_CHANGE"])+(((((((11.714300)*(((((((data["OWN_CAR_AGE"])+(data["CODE_REJECT_REASON_SCOFR"])))* 2.0)) -(((data["inst_AMT_PAYMENT"])-(data["CODE_REJECT_REASON_SCOFR"])))))))* 2.0)) / 2.0))))
v["i22"] = np.tanh(((0.576923)+(((data["AMT_ANNUITY_x"])+(((((data["AMT_ANNUITY_x"])+(np.maximum(((data["AMT_ANNUITY_x"])) ,(( np.maximum(((data["DEF_60_CNT_SOCIAL_CIRCLE"])) ,(( data["CNT_INSTALMENT_FUTURE"])))))))))-(data["CHANNEL_TYPE_Channel_of_corporate_sales"])))))))
 v["i23"] = np.tanh(((data["SK_DPD"])+(((((((( data["te_NAME_EDUCATION_TYPE"])+(data["te_FLAG_OWN_CAR"])) /2.0)) -(data["RATE_DOWN_PAYMENT"])))+(np.where(data["te_NAME_FAMILY_STATUS"]>0, 0.797872,(( data["SK_DPD"])-(0.494118))))))))
v["i24"] = np.tanh(((((((data["te_ORGANIZATION_TYPE"])+(data["CODE_REJECT_REASON_HC"])) /2.0)) +(((data["te_CODE_GENDER"])-(((data["NAME_YIELD_GROUP_low_normal"])+(((data["CHANNEL_TYPE_Channel_of_corporate_sales"])+(((data["ty__Mortgage"])*(data["ty__Mortgage"])))))))))) /2.0))
v["i25"] = np.tanh(((((((( 1.220590)-(data["FLOORSMAX_MODE"])))+(data["CNT_INSTALMENT_FUTURE"])) /2.0)) +(((14.800000)*(np.maximum(((data["ty__Microloan"])) ,(( data["SK_DPD"]))))))))
v["i26"] = np.tanh(( -1.0*(((((((0.494118)+(((((np.minimum(((data["inst_AMT_PAYMENT"])) ,(( data["MONTHS_BALANCE"])))) * 2.0)) +(data["NAME_YIELD_GROUP_low_action"])))) /2.0)) +(np.maximum(((data["CHANNEL_TYPE_Channel_of_corporate_sales"])) ,(((( data["AMT_CREDIT_SUM_LIMIT"])* 2.0))))))))))
v["i27"] = np.tanh(((( np.tanh(( data["PRODUCT_COMBINATION_Cash_Street__low"])))+(( -1.0*(((( data["cc_bal_CNT_INSTALMENT_MATURE_CUM"])+(((((data["PRODUCT_COMBINATION_Cash_Street__low"])+(((data["ORGANIZATION_TYPE"])-(data["CNT_PAYMENT"])))))-(data["cc_bal_CNT_DRAWINGS_CURRENT"])))))))))/2.0))
v["i28"] = np.tanh(((((( 0.062500)+(data["DAYS_REGISTRATION"])) /2.0)) -(np.where(((( data["SK_ID_PREV_y"])+(((( data["FLAG_DOCUMENT_18"])+(data["Active"])) /2.0)))/2.0)>0, data["Active"],(( data["PRODUCT_COMBINATION_Cash_X_Sell__low"])+(data["FLAG_DOCUMENT_16"]))))))
v["i29"] = np.tanh(((((( data["NAME_FAMILY_STATUS"])+(np.minimum(((data["PRODUCT_COMBINATION_Cash_Street__low"])) ,(((-1.0*(( np.maximum(((data["ty__Mortgage"])) ,(((( np.maximum(((data["PRODUCT_COMBINATION_Cash_Street__low"])) ,(((-1.0*(( data["te_FLAG_DOCUMENT_18"])))))))/ 2.0)))))))))))) /2.0)) -(data["cc_bal_AMT_CREDIT_LIMIT_ACTUAL"])))
 v["i30"] = np.tanh(np.where(( -1.0*(( data["cc_bal_AMT_TOTAL_RECEIVABLE"])))>0, data["CNT_PAYMENT"],(((((((data["FLAG_DOCUMENT_13"])<(np.tanh(((( data["SK_DPD"])*(0.797872)))))) *1.))+(data["ca__Sold"])))+(data["SK_DPD"]))))
v["i31"] = np.tanh(((( data["cc_bal_AMT_BALANCE"])+(( -1.0*(( np.maximum(((data["CODE_REJECT_REASON_SCO"])) ,(((((np.maximum(((data["FLAG_PHONE"])) ,(( data["FLAG_DOCUMENT_18"])))) +(np.minimum(((data["cc_bal_AMT_RECIVABLE"])) ,(( data["FLAG_PHONE"])))))/2.0)))))))))/2.0))
v["i32"] = np.tanh(((((((((( data["NAME_PAYMENT_TYPE_XNA"])-(data["FLAG_DOCUMENT_13"])))-(data["te_FLAG_CONT_MOBILE"])))+(((((data["DAYS_ID_PUBLISH"])+(data["te_FLAG_WORK_PHONE"])))-(data["ty__Car_loan"])))) /2.0)) / 2.0))
v["i33"] = np.tanh(((((( data["avg_buro_buro_bal_status_0"])<(-1.0)) *1.))-(np.maximum(((((data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])* 2.0))),(((((np.minimum(((( 3.99197196960449219))),(( data["FLAG_DOCUMENT_16"])))) +(data["DAYS_ENDDATE_FACT"])) /2.0)))))))
 v["i34"] = np.tanh(np.where(data["NAME_GOODS_CATEGORY_Direct_Sales"]>0,(( 0.323944)-(data["AMT_ANNUITY_x"])) , np.minimum(((((data["AMT_ANNUITY_x"])+(0.323944)))) ,(((((1.105260)>(np.maximum(((data["FLAG_DOCUMENT_13"])) ,(( data["AMT_ANNUITY_x"])))))*1.))))))
 v["i35"] = np.tanh(np.where(data["AMT_GOODS_PRICE_x"]>0,(-1.0*(( data["AMT_GOODS_PRICE_x"]))),(( data["AMT_GOODS_PRICE_x"])+(((( 0.036585)>(((np.maximum(((np.tanh(( data["AMT_GOODS_PRICE_x"])))) ,(( -2.0)))) -(data["CHANNEL_TYPE_AP___Cash_loan_"])))) *1.)))))
v["i36"] = np.tanh(((data["DAYS_EMPLOYED"])+(((((0.494118)+(data["DAYS_EMPLOYED"])))*(np.where(data["FLAG_DOCUMENT_8"]>0, 0.494118,(( 14.800000)*(1.857140))))))))
v["i37"] = np.tanh(np.minimum(((0.494118)) ,(( np.minimum(((((np.minimum(((( 1.0))),(((((data["te_NAME_INCOME_TYPE"])+(0.680000)) /2.0)))))+(data["AMT_CREDIT_x"])))) ,(((((np.tanh(( data["AMT_ANNUITY_x"])))>(data["AMT_CREDIT_x"])) *1.))))))))
 v["i38"] = np.tanh(((((np.where(data["PRODUCT_COMBINATION_Cash_X_Sell__middle"]>0, data["CNT_PAYMENT"],(-1.0*(( np.maximum(((((data["CHANNEL_TYPE_AP___Cash_loan_"])*(data["PRODUCT_COMBINATION_Cash_X_Sell__middle"])))) ,(((( data["AMT_REQ_CREDIT_BUREAU_QRT"])*(11.714300)))))))))) * 2.0)) -(data["AMT_REQ_CREDIT_BUREAU_QRT"])))
 v["i39"] = np.tanh(((data["NAME_GOODS_CATEGORY_Direct_Sales"])-(((( np.maximum(((data["cc_bal_CNT_INSTALMENT_MATURE_CUM"])) ,(((( 0.494118)*(data["NAME_TYPE_SUITE_Unaccompanied"])))))) +(((( data["Active"])+(data["SK_ID_PREV_y"])) /2.0)))/2.0))))
v["i40"] = np.tanh(((((( 0.576923)-(np.maximum(((data["inst_NUM_INSTALMENT_VERSION"])) ,(((((data["NFLAG_INSURED_ON_APPROVAL"])<(((((data["SK_DPD"])* 2.0)) * 2.0)))*1.))))))) +(((( data["SK_DPD"])+(((data["NFLAG_INSURED_ON_APPROVAL"])/ 2.0)))/2.0)))/2.0))
v["i41"] = np.tanh(np.where(data["cc_bal_SK_DPD_DEF"]>0, 11.714300,(((((data["inst_AMT_PAYMENT"])<(np.tanh(((-1.0*(( np.maximum(((np.minimum(((0.576923)) ,(( 0.680000))))),(((( data["te_REG_REGION_NOT_LIVE_REGION"])* 2.0)))))))))))*1.))* 2.0)))
 v["i42"] = np.tanh(( -1.0*(((((data["FLAG_DOCUMENT_18"])+(((( np.minimum(((0.697674)) ,(((( np.tanh(((((data["NAME_GOODS_CATEGORY_Direct_Sales"])<(data["cc_bal_SK_DPD_DEF"])) *1.))))-(data["te_FLAG_OWN_REALTY"])))))) <(data["te_OCCUPATION_TYPE"])) *1.))) /2.0)))))
 v["i43"] = np.tanh(((((( data["cc_bal_AMT_TOTAL_RECEIVABLE"])*(data["te_ORGANIZATION_TYPE"])))+(((((((data["cc_bal_AMT_TOTAL_RECEIVABLE"])+(data["te_ORGANIZATION_TYPE"])) /2.0)) +(np.where(data["cc_bal_AMT_BALANCE"]>0, data["te_CNT_CHILDREN"],(((data["FLAG_DOCUMENT_2"])+(data["cc_bal_AMT_TOTAL_RECEIVABLE"])) /2.0)))) /2.0)))/2.0))
v["i44"] = np.tanh(np.where(data["CNT_INSTALMENT_FUTURE"]>0,(((0.062500)>(( -1.0*(( data["te_WEEKDAY_APPR_PROCESS_START"])))))*1.) ,(( np.minimum(((data["ty__Mortgage"])) ,(( np.tanh(((-1.0*(((((data["ty__Mortgage"])>(data["cc_bal_SK_DPD_DEF"])) *1.))))))))))* 2.0)))
 v["i45"] = np.tanh(((((((data["NAME_GOODS_CATEGORY_Direct_Sales"])+(( -1.0*(( data["CODE_GENDER"])))))/2.0)) +(np.where(data["CODE_GENDER"]>0,(((-1.0*(( data["PRODUCT_COMBINATION_Cash_X_Sell__low"])))) *(1.105260)) , data["DAYS_FIRST_DUE"])))/2.0))
v["i46"] = np.tanh(( -1.0*(((( np.tanh(((((((((data["avg_buro_buro_bal_status_0"])+(((((( data["avg_buro_buro_bal_status_0"])+(data["avg_buro_buro_bal_status_0"])))<(data["FLAG_DOCUMENT_13"])) *1.))) /2.0)) * 2.0)) -(0.043478)))))-(0.043478))))))
v["i47"] = np.tanh(((((((( data["FLAG_DOCUMENT_2"])-(np.minimum(((data["inst_AMT_PAYMENT"])) ,(((( 0.036585)* 2.0)))))))>(0.576923)) *1.))-(np.maximum(((data["YEARS_BUILD_MODE"])) ,(((((( data["inst_AMT_PAYMENT"])/ 2.0)) / 2.0)))))))
 v["i48"] = np.tanh(np.where(data["AMT_CREDIT_x"]>0, 0.339286,(((data["cc_bal_CNT_DRAWINGS_ATM_CURRENT"])+(((( data["DEF_30_CNT_SOCIAL_CIRCLE"])+(((data["AMT_CREDIT_x"])+(((((( data["AMT_CREDIT_x"])>(data["NAME_YIELD_GROUP_high"])) *1.))-(data["NAME_YIELD_GROUP_high"])))))) /2.0)))/2.0)))
 v["i49"] = np.tanh(((((( data["NAME_GOODS_CATEGORY_Direct_Sales"])+(((((-1.0*(((((((data["te_REG_REGION_NOT_LIVE_REGION"])+(data["te_LIVE_REGION_NOT_WORK_REGION"])) /2.0)) / 2.0)))))+(( -1.0*(((( np.maximum(((data["cc_bal_CNT_INSTALMENT_MATURE_CUM"])) ,(( data["cc_bal_CNT_INSTALMENT_MATURE_CUM"])))) / 2.0)))))) /2.0)))/2.0)) * 2.0))
v["i50"] = np.tanh(((((-1.0*(( data["CHANNEL_TYPE_Channel_of_corporate_sales"])))) +(np.minimum(((((3.0)-(data["PRODUCT_COMBINATION_Cash_Street__low"])))) ,(((-1.0*(((( data["AMT_CREDIT_SUM_LIMIT"])+(((( data["WEEKDAY_APPR_PROCESS_START_SATURDAY"])+(data["CHANNEL_TYPE_Channel_of_corporate_sales"])) /2.0)))))))))))/2.0))
v["i51"] = np.tanh(((((((((np.minimum(((data["EXT_SOURCE_2"])) ,(((( -2.0)*(data["EXT_SOURCE_2"])))))) -(-2.0)))*(data["EXT_SOURCE_2"])))* 2.0)) * 2.0))
v["i52"] = np.tanh(((np.minimum(((np.minimum(((( -1.0*(( data["FLAG_DOCUMENT_13"]))))),(( data["FLAG_DOCUMENT_13"]))))),(( data["NAME_GOODS_CATEGORY_Direct_Sales"])))) +(((( data["DAYS_LAST_PHONE_CHANGE"])>(np.tanh(((( 3.0)-(1.220590)))))) *1.))))
 v["i53"] = np.tanh(((np.minimum(((np.minimum(((((data["FLAG_DOCUMENT_16"])*(data["WEEKDAY_APPR_PROCESS_START_SUNDAY"])))) ,(((-1.0*(( data["FLAG_DOCUMENT_14"])))))))) ,(((( data["FLAG_DOCUMENT_16"])*(data["NAME_CLIENT_TYPE_Refreshed"])))))) * 2.0))
v["i54"] = np.tanh(np.minimum(((( -1.0*(( data["cc_bal_cc_bal_status__Sent_proposal"]))))),(((((( np.where(data["cc_bal_cc_bal_status__Sent_proposal"]>0, data["cc_bal_cc_bal_status__Sent_proposal"],(((0.031746)+(np.where(data["NONLIVINGAPARTMENTS_MODE"]>0, data["NONLIVINGAPARTMENTS_MODE"], data["AMT_CREDIT_y"])))/2.0)))* 2.0)) * 2.0)))))
 v["i55"] = np.tanh(((((((((((( data["Returned_to_the_store"])+(data["NAME_GOODS_CATEGORY_Insurance"])) /2.0)) -(data["ty__Mortgage"])))+(data["cc_bal_CNT_DRAWINGS_CURRENT"])) /2.0)) +(((data["HOUR_APPR_PROCESS_START_y"])*(((((data["Returned_to_the_store"])* 2.0)) * 2.0)))))/2.0))
v["i56"] = np.tanh(((np.where(data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"]>0,(((((data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])>(0.323944)) *1.))*(((0.686567)-(data["REGION_POPULATION_RELATIVE"])))) , 2.235290)) *(((data["REGION_POPULATION_RELATIVE"])+(0.323944)))))
 v["i57"] = np.tanh(np.where(data["cc_bal_SK_DPD_DEF"]>0, 1.105260,(((((( data["FLAG_DOCUMENT_2"])/ 2.0)) -(((((-1.0*(((((data["ca__Sold"])+(data["FLAG_WORK_PHONE"])) /2.0)))))+(np.tanh(( data["Active"])))) /2.0)))) / 2.0)))
 v["i58"] = np.tanh(( -1.0*(( np.maximum(((data["FLAG_DOCUMENT_11"])) ,(( np.maximum(((((data["FLAG_DOCUMENT_18"])/ 2.0))),(((((((np.minimum(((data["FLAG_DOCUMENT_18"])) ,(((( data["AMT_REQ_CREDIT_BUREAU_MON"])*(data["FLAG_DOCUMENT_18"])))))) / 2.0)) <(data["NAME_GOODS_CATEGORY_Gardening"])) *1.)))))))))))
v["i59"] = np.tanh(np.where(((( 1.0)>(((( 0.043478)<(((data["NAME_GOODS_CATEGORY_Direct_Sales"])-(data["SK_DPD"])))) *1.))) *1.) >0, 3.526320, np.minimum(((data["te_NAME_HOUSING_TYPE"])) ,(( 0.043478)))))
 v["i60"] = np.tanh(((data["cc_bal_SK_DPD_DEF"])-(((((( 0.697674)+(data["HOUSETYPE_MODE"])) /2.0)) *(((((( data["NONLIVINGAPARTMENTS_MODE"])+(data["HOUSETYPE_MODE"])) /2.0)) *(data["nans"])))))))
 v["i61"] = np.tanh(((data["NAME_GOODS_CATEGORY_Direct_Sales"])+(((np.minimum(((data["FLAG_DOCUMENT_2"])) ,(( data["NAME_GOODS_CATEGORY_Direct_Sales"])))) +(np.tanh(((((np.where(data["inst_SK_ID_PREV"]>0, 0.680000, data["SK_ID_PREV_y"])) <(( -1.0*(( 0.697674)))))*1.))))))))
 v["i62"] = np.tanh(np.minimum(((((data["SK_ID_PREV_x"])*(((data["CNT_INSTALMENT_FUTURE"])* 2.0))))),(((((((np.where(data["CNT_INSTALMENT_FUTURE"]>0,(( data["SK_ID_PREV_x"])/ 2.0), data["CODE_REJECT_REASON_SCO"])) *(data["SK_ID_PREV_x"])))+(data["CNT_PAYMENT"])) /2.0)))))
 v["i63"] = np.tanh(np.minimum(((0.494118)) ,(( np.maximum(((data["te_NAME_FAMILY_STATUS"])) ,(((((data["PRODUCT_COMBINATION_Card_X_Sell"])+(((((((((( data["te_NAME_FAMILY_STATUS"])* 2.0)) *(data["PRODUCT_COMBINATION_Card_X_Sell"])))+(data["REG_CITY_NOT_LIVE_CITY"])))+(data["REG_CITY_NOT_LIVE_CITY"])) /2.0)))/2.0))))))))
v["i64"] = np.tanh(((((( data["cc_bal_AMT_CREDIT_LIMIT_ACTUAL"])<(((1.220590)-(2.0)))) *1.))-(((((( data["cc_bal_cc_bal_status__Sent_proposal"])+(((( data["SK_ID_PREV_y"])+(data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])) /2.0)))/2.0)) *(((2.0)/ 2.0))))))
v["i65"] = np.tanh(((data["cc_bal_AMT_DRAWINGS_OTHER_CURRENT"])+(((((data["AMT_CREDIT_y"])*(((( 0.680000)<(((((-1.0)* 2.0)) +(data["AMT_CREDIT_y"])))) *1.))))*(((data["AMT_CREDIT_y"])+(data["AMT_CREDIT_y"])))))))
 v["i66"] = np.tanh(((( np.where(data["NAME_GOODS_CATEGORY_Insurance"]>0, data["FLAG_DOCUMENT_6"],(((( -1.0*(( data["FLAG_DOCUMENT_11"])))) +(data["SK_ID_PREV_x"])) /2.0)))+(np.where(data["NAME_GOODS_CATEGORY_Insurance"]>0,(-1.0*(( data["te_FLAG_DOCUMENT_6"]))), data["cc_bal_CNT_DRAWINGS_CURRENT"])))/2.0))
v["i67"] = np.tanh(((data["EXT_SOURCE_3"])-(((0.494118)-(((((data["EXT_SOURCE_3"])+(((data["EXT_SOURCE_3"])+(2.0)))))*(data["EXT_SOURCE_3"])))))))
 v["i68"] = np.tanh(((np.minimum(((((( data["NAME_CONTRACT_STATUS_Approved"])<(data["ca__Closed"])) *1.))) ,(((-1.0*(( np.where(((( data["ca__Closed"])<(1.220590)) *1.) >0, data["ca__Closed"], data["inst_AMT_PAYMENT"])))))))) * 2.0))
v["i69"] = np.tanh(((((( data["CODE_REJECT_REASON_XAP"])/ 2.0)) +(np.where(((( data["CODE_REJECT_REASON_XAP"])>(0.576923)) *1.) >0,(((data["SK_ID_BUREAU"])<(((-2.0)/ 2.0)))*1.) , data["SK_ID_BUREAU"])))/2.0))
v["i70"] = np.tanh(((((( 0.697674)* 2.0)) +(((((( 6.0)) * 2.0)) *(np.where(data["AMT_CREDIT_SUM_DEBT"]>0, 11.714300, data["AMT_CREDIT_SUM_DEBT"])))))/2.0))
v["i71"] = np.tanh(((((( data["NAME_GOODS_CATEGORY_Direct_Sales"])<(( -1.0*(((( data["AMT_CREDIT_SUM"])+(np.where(( -1.0*(( data["cc_bal_SK_DPD_DEF"])))>0, 0.416667, np.tanh(( 0.416667)))))))))) *1.))*(2.235290)))
 v["i72"] = np.tanh(((data["NAME_GOODS_CATEGORY_Direct_Sales"])+(((data["Returned_to_the_store"])*(np.maximum(((np.maximum(((data["NAME_CASH_LOAN_PURPOSE_Furniture"])) ,(( 0.680000))))),(((((( np.maximum(((data["ty__Car_loan"])) ,(( data["Returned_to_the_store"])))) * 2.0)) * 2.0)))))))))
 v["i73"] = np.tanh(((((((((((( -1.0*(((((1.105260)<(data["NAME_EDUCATION_TYPE"])) *1.))))) -(data["cc_bal_AMT_DRAWINGS_POS_CURRENT"])))*(data["NAME_EDUCATION_TYPE"])))-(data["cc_bal_AMT_DRAWINGS_POS_CURRENT"])))*(data["NAME_EDUCATION_TYPE"])))-(data["cc_bal_AMT_DRAWINGS_POS_CURRENT"])))
 v["i74"] = np.tanh(((((( data["cc_bal_CNT_DRAWINGS_POS_CURRENT"])+(((((((( data["FLAG_DOCUMENT_2"])-(((( data["DAYS_LAST_DUE_1ST_VERSION"])+(( -1.0*(( data["NAME_GOODS_CATEGORY_Insurance"])))))/2.0)))) * 2.0)) +(((data["SK_DPD"])+(data["REGION_RATING_CLIENT_W_CITY"])))) /2.0)))/2.0)) / 2.0))
v["i75"] = np.tanh(((( -1.0*(((((data["cc_bal_SK_DPD_DEF"])<(data["ty__Mortgage"])) *1.))))) -(((((np.minimum(((data["YEARS_BEGINEXPLUATATION_MODE"])) ,(((((data["ty__Mortgage"])>(data["YEARS_BEGINEXPLUATATION_MODE"])) *1.))))) * 2.0)) * 2.0))))
v["i76"] = np.tanh(np.where(data["cc_bal_SK_DPD_DEF"]>0,(((( data["DAYS_LAST_DUE"])-(data["DAYS_FIRST_DUE"])))-(data["cc_bal_CNT_INSTALMENT_MATURE_CUM"])) ,(((((( data["DAYS_FIRST_DUE"])+(data["NAME_GOODS_CATEGORY_Insurance"])) /2.0)) >(1.220590)) *1.)))
v["i77"] = np.tanh(((np.where(data["AMT_REQ_CREDIT_BUREAU_DAY"]>0, data["AMT_REQ_CREDIT_BUREAU_DAY"],(( data["cc_bal_AMT_TOTAL_RECEIVABLE"])*(((np.where(data["cc_bal_AMT_TOTAL_RECEIVABLE"]>0, data["AMT_REQ_CREDIT_BUREAU_DAY"], 0.686567)) +(data["MONTHS_BALANCE"])))))) -(data["FLAG_DOCUMENT_15"])))
 v["i78"] = np.tanh(((((( np.where(((data["cc_bal_SK_DPD_DEF"])-(data["FLAG_DOCUMENT_2"])) >0, data["inst_AMT_PAYMENT"], data["te_FLAG_DOCUMENT_20"])) <(((-1.0)-(( -1.0*(((((0.031746)+(1.0)) /2.0)))))))) *1.))/ 2.0))
v["i79"] = np.tanh(np.minimum(((( -1.0*(((((((( 1.27607381343841553)) <(data["NAME_YIELD_GROUP_high"])) *1.))/ 2.0)))))) ,(((((( data["AMT_CREDIT_x"])+(0.797872)))+(((data["AMT_CREDIT_x"])+(0.797872))))))))
v["i80"] = np.tanh(((((((((1.0)-(data["NAME_CONTRACT_STATUS_Canceled"])))<(data["CHANNEL_TYPE_AP___Cash_loan_"])) *1.))<(np.where(np.tanh(( data["NAME_CONTRACT_STATUS_Canceled"])) >0, data["CHANNEL_TYPE_AP___Cash_loan_"],(((((data["NONLIVINGAPARTMENTS_MODE"])+(data["CHANNEL_TYPE_AP___Cash_loan_"])) /2.0)) / 2.0)))) *1.))
 v["i81"] = np.tanh(np.where(data["PRODUCT_COMBINATION_Cash_Street__low"]>0, np.where(((( data["FLAG_DOCUMENT_2"])<(data["ty__Microloan"])) *1.) >0, data["PRODUCT_COMBINATION_Cash_Street__low"], -2.0),(((data["NAME_GOODS_CATEGORY_Construction_Materials"])>(data["cc_bal_SK_DPD_DEF"])) *1.)))
v["i82"] = np.tanh(((((np.minimum(((0.0)) ,(( data["FLAG_DOCUMENT_14"])))) -(((data["FLAG_DOCUMENT_14"])-(((( data["te_ORGANIZATION_TYPE"])>(((((( 2.0)+(data["te_NAME_CONTRACT_TYPE"])))+(3.0)) /2.0)))*1.))))))* 2.0))
v["i83"] = np.tanh(np.maximum(((data["FLAG_DOCUMENT_2"])) ,(( np.maximum(((data["NAME_GOODS_CATEGORY_Insurance"])) ,(( np.maximum(((((((( 0.036585)<(data["cc_bal_SK_DPD_DEF"])) *1.))* 2.0))),(((( data["ty__Microloan"])-(((((6.0)) +(( 6.0)))/2.0)))))))))))))
 v["i84"] = np.tanh(((((0.0)-(((data["NAME_SELLER_INDUSTRY_Connectivity"])*(((data["avg_buro_MONTHS_BALANCE"])*(data["NAME_SELLER_INDUSTRY_Connectivity"])))))))*(((((((data["NAME_SELLER_INDUSTRY_Connectivity"])<(data["avg_buro_MONTHS_BALANCE"])) *1.))<(data["NAME_SELLER_INDUSTRY_Connectivity"])) *1.))))
 v["i85"] = np.tanh(np.minimum(((( -1.0*(( data["NAME_GOODS_CATEGORY_Sport_and_Leisure"]))))),(((((( data["cc_bal_CNT_INSTALMENT_MATURE_CUM"])+(data["NAME_GOODS_CATEGORY_Sport_and_Leisure"])))*(data["WEEKDAY_APPR_PROCESS_START_TUESDAY"]))))))
v["i86"] = np.tanh(((((( 2.0)-(1.0)))<(np.where(data["CNT_INSTALMENT_FUTURE"]>0,(((((data["cc_bal_SK_DPD"])-(((data["DAYS_FIRST_DUE"])/ 2.0)))) +(data["CNT_INSTALMENT_FUTURE"])) /2.0), data["DAYS_FIRST_DUE"])))*1.))
 v["i87"] = np.tanh(((( -1.0*(((((((( np.minimum(((data["cc_bal_SK_DPD"])) ,(((-1.0*(( data["inst_AMT_PAYMENT"])))))))+(np.minimum(((data["NAME_SELLER_INDUSTRY_Connectivity"])) ,(((( data["NAME_GOODS_CATEGORY_Direct_Sales"])* 2.0)))))) /2.0)) <(data["inst_AMT_PAYMENT"])) *1.))))) *(data["MONTHS_BALANCE"])))
 v["i88"] = np.tanh(((((((2.0)<(data["NAME_SELLER_INDUSTRY_Connectivity"])) *1.))+(((((( data["RATE_DOWN_PAYMENT"])>(data["cc_bal_SK_DPD"])) *1.))*(( -1.0*(((( 0.680000)-(((0.043478)* 2.0)))))))))) /2.0))
v["i89"] = np.tanh(( -1.0*(( np.maximum(((((data["FLAG_DOCUMENT_13"])* 2.0))),(((((( 0.062500)+(data["NAME_TYPE_SUITE_Children"])))+(((data["NAME_CLIENT_TYPE_Refreshed"])+(-3.0)))))))))))
 v["i90"] = np.tanh(((np.where(data["NONLIVINGAPARTMENTS_MODE"]>0, np.maximum(((((((data["NONLIVINGAPARTMENTS_MODE"])* 2.0)) *(((( data["NONLIVINGAPARTMENTS_MODE"])>(2.235290)) *1.))))) ,(( data["cc_bal_SK_DPD"]))), data["NONLIVINGAPARTMENTS_MODE"])) -(data["NONLIVINGAPARTMENTS_MODE"])))
 v["i91"] = np.tanh(np.maximum(((((((((data["DAYS_LAST_DUE"])>(((( data["LIVINGAPARTMENTS_MEDI"])<(0.323944)) *1.))) *1.))>(((( data["LIVINGAPARTMENTS_MEDI"])<(( -1.0*(( data["cc_bal_SK_DPD_DEF"])))))*1.))) *1.))) ,(((-1.0*(( data["cc_bal_SK_DPD_DEF"])))))))
 v["i92"] = np.tanh(((14.800000)*(((( data["ENTRANCES_MEDI"])<(( -1.0*(( np.where(data["PRODUCT_COMBINATION_Card_X_Sell"]>0, 0.686567,(( 0.686567)+(((0.697674)+(3.526320)))))))))) *1.))))
 v["i93"] = np.tanh(np.minimum(((data["cc_bal_SK_DPD"])) ,(((((((-1.0*(((((data["FLAG_DOCUMENT_15"])+(((( data["NAME_CASH_LOAN_PURPOSE_Everyday_expenses"])+(((data["NAME_CASH_LOAN_PURPOSE_Furniture"])/ 2.0)))/2.0)))/2.0)))))-(0.036585)))+(1.0))))))
v["i94"] = np.tanh(np.minimum(((((data["ty__Another_type_of_loan"])-(((((((data["ty__Another_type_of_loan"])* 2.0)) * 2.0)) * 2.0))))),(((((np.tanh(((((( data["REGION_POPULATION_RELATIVE"])* 2.0)) * 2.0)))) +(0.494118)) /2.0)))))
 v["i95"] = np.tanh(np.where(data["NAME_GOODS_CATEGORY_Direct_Sales"]>0, data["FLAG_DOCUMENT_16"],(((( data["cu__currency_3"])-(np.maximum(((data["cc_bal_cc_bal_status__Sent_proposal"])) ,(((((data["FONDKAPREMONT_MODE"])>(1.0)) *1.))))))) -(data["FLAG_DOCUMENT_16"]))))
v["i96"] = np.tanh(np.maximum(((((( np.minimum(((data["te_WEEKDAY_APPR_PROCESS_START"])) ,(( 0.043478)))) +(np.maximum(((( -1.0*(( data["FLAG_CONT_MOBILE"]))))),(((( data["FLAG_DOCUMENT_2"])/ 2.0)))))) /2.0))),(((((-1.0)+(data["NAME_CASH_LOAN_PURPOSE_Car_repairs"])) /2.0)))))
 v["i97"] = np.tanh(( -1.0*(( np.where(((1.220590)-(np.maximum(((((( data["NAME_GOODS_CATEGORY_Gardening"])+(data["inst_NUM_INSTALMENT_VERSION"])) /2.0))),(( data["FLAG_DOCUMENT_11"])))))>0, data["FLAG_DOCUMENT_16"],(7.99752998352050781))))))
v["i98"] = np.tanh(((np.minimum(((np.maximum(((((((((-1.0*(( 0.576923)))) / 2.0)) >(data["AMT_CREDIT_y"])) *1.))) ,(( data["NAME_SELLER_INDUSTRY_Auto_technology"]))))),(((( data["AMT_CREDIT_y"])-(( -1.0*(( 0.576923)))))))))*(2.0)))
 v["i99"] = np.tanh(((((( data["Active"])>(0.576923)) *1.))*(((data["Active"])*(((( -1.0*(((( np.minimum(((data["Active"])) ,(( data["CNT_INSTALMENT"])))) +(0.323944)))))) * 2.0))))))
v["i100"] = np.tanh(np.where(data["AMT_CREDIT_y"]>0,(( data["CNT_INSTALMENT_FUTURE"])*(np.maximum(((((( data["AMT_CREDIT_y"])>(1.105260)) *1.))) ,(((( 0.043478)*(data["CNT_INSTALMENT_FUTURE"]))))))),(((data["NAME_YIELD_GROUP_XNA"])>(1.105260)) *1.)))
v["i101"] = np.tanh(np.minimum(((0.686567)) ,(( np.minimum(((((data["FLAG_EMAIL"])*(data["inst_AMT_PAYMENT"])))) ,(((( 1.857140)-(data["NAME_CONTRACT_STATUS_Canceled"])))))))))
 v["i102"] = np.tanh(np.where(data["cc_bal_SK_DPD_DEF"]>0, data["FLAG_PHONE"],(( data["AMT_CREDIT_y"])*(((((( 0.576923)<(np.where(data["ca__Sold"]>0, data["AMT_CREDIT_y"], data["cc_bal_SK_DPD_DEF"])))*1.))* 2.0)))))
 v["i103"] = np.tanh(((((((( data["PRODUCT_COMBINATION_POS_household_without_interest"])>(((( data["PRODUCT_COMBINATION_POS_household_without_interest"])<(((data["FLAG_PHONE"])/ 2.0)))*1.))) *1.))/ 2.0)) +(((((data["FLAG_PHONE"])*(data["NAME_GOODS_CATEGORY_Direct_Sales"])))* 2.0))))
v["i104"] = np.tanh(np.where(data["NAME_GOODS_CATEGORY_Direct_Sales"]>0, data["FLAG_DOCUMENT_6"],(((((( np.tanh(( data["FLAG_DOCUMENT_6"])))+(((((data["FLAG_DOCUMENT_2"])-(((0.0)-(data["NAME_GOODS_CATEGORY_Insurance"])))))* 2.0)))/2.0)) +(data["cc_bal_SK_DPD_DEF"])) /2.0)))
 v["i105"] = np.tanh(( -1.0*(((( data["SK_ID_PREV_y"])*(((((((data["te_ORGANIZATION_TYPE"])+(data["NAME_GOODS_CATEGORY_Direct_Sales"])) /2.0)) +(((((( data["te_ORGANIZATION_TYPE"])+(data["FLAG_DOCUMENT_2"])) /2.0)) *(( -1.0*(( data["te_FLAG_DOCUMENT_20"])))))))/2.0)))))))
 v["i106"] = np.tanh(( -1.0*(((((0.576923)>(((((( np.minimum(((np.minimum(((data["te_NAME_INCOME_TYPE"])) ,(( data["NAME_GOODS_CATEGORY_Direct_Sales"]))))),(( 0.043478)))) -(data["SK_ID_PREV_y"])))+(( 3.83449053764343262)))/2.0)))*1.)))))
v["i107"] = np.tanh(( -1.0*(((( np.maximum(((data["NAME_SELLER_INDUSTRY_MLM_partners"])) ,(((((1.105260)<(data["NAME_GOODS_CATEGORY_Photo___Cinema_Equipment"])) *1.))))) * 2.0)))))
 v["i108"] = np.tanh(((np.where(((( data["NAME_SELLER_INDUSTRY_Connectivity"])+(0.494118)) /2.0)>0,(( data["te_NAME_INCOME_TYPE"])/ 2.0), 0.043478)) *(((0.323944)+(((( data["te_FLAG_DOCUMENT_20"])+(data["te_FLAG_DOCUMENT_3"])) /2.0))))))
v["i109"] = np.tanh(((np.maximum(((((((((((data["FLAG_DOCUMENT_2"])+(data["NAME_GOODS_CATEGORY_Insurance"])) /2.0)) * 2.0)) +(((data["NAME_GOODS_CATEGORY_Direct_Sales"])* 2.0)))/2.0))),(((( data["cu__currency_3"])* 2.0)))))*(((((data["te_FLAG_DOCUMENT_20"])* 2.0)) * 2.0))))
v["i110"] = np.tanh(((( 0.323944)+(np.minimum(((np.minimum(((np.tanh(( data["WEEKDAY_APPR_PROCESS_START_MONDAY"])))) ,(((( 0.697674)* 2.0)))))) ,(((( data["CODE_REJECT_REASON_VERIF"])*(((((data["FLAG_DOCUMENT_17"])*(data["CODE_REJECT_REASON_VERIF"])))/ 2.0)))))))) /2.0))
v["i111"] = np.tanh(np.tanh(( np.where(((((((((( 0.494118)+(data["DAYS_CREDIT_ENDDATE"])) /2.0)) <(( 0.0)))*1.))>(0.031746)) *1.) >0, 11.714300, np.minimum(((0.494118)) ,(( data["DAYS_CREDIT_ENDDATE"])))))))
 v["i112"] = np.tanh(((( -1.0*(( np.where(((data["NAME_CASH_LOAN_PURPOSE_Furniture"])/ 2.0)>0,(((-1.0*(( data["avg_buro_buro_count"])))) / 2.0),(((data["avg_buro_buro_count"])>(((( 2.235290)>(((data["CODE_REJECT_REASON_SCO"])/ 2.0)))*1.))) *1.))))))* 2.0))
v["i113"] = np.tanh(((((((((-1.0*(( data["NAME_CASH_LOAN_PURPOSE_Buying_a_home"])))) +(((data["NAME_CASH_LOAN_PURPOSE_Payments_on_other_loans"])*(((((data["NAME_CASH_LOAN_PURPOSE_Payments_on_other_loans"])+(np.maximum(((data["AMT_REQ_CREDIT_BUREAU_YEAR"])) ,(((( 0.043478)/ 2.0)))))))* 2.0)))))/2.0)) * 2.0)) * 2.0))
v["i114"] = np.tanh(((np.where(data["cc_bal_AMT_DRAWINGS_OTHER_CURRENT"]>0, np.where(data["PRODUCT_COMBINATION_Cash"]>0,(-1.0*(( data["avg_buro_buro_bal_status_X"]))), data["cc_bal_AMT_DRAWINGS_OTHER_CURRENT"]), data["PRODUCT_COMBINATION_Cash"])) / 2.0))
v["i115"] = np.tanh(((data["AMT_CREDIT_y"])*(np.where(0.494118>0,(((data["cc_bal_SK_DPD_DEF"])>(((((data["FLAG_DOCUMENT_17"])+(0.797872)))+(np.tanh(( data["AMT_CREDIT_x"])))))) *1.) , data["AMT_CREDIT_x"]))))
v["i116"] = np.tanh(np.where(data["FLAG_DOCUMENT_2"]>0, 3.0,(( data["NAME_CONTRACT_STATUS_Canceled"])*(((((data["NAME_CONTRACT_STATUS_Canceled"])*(data["NAME_CONTRACT_STATUS_Canceled"])))*(np.maximum(((data["NAME_CASH_LOAN_PURPOSE_Furniture"])) ,(( data["NAME_CASH_LOAN_PURPOSE_Furniture"])))))))))
 v["i117"] = np.tanh(((data["FLAG_DOCUMENT_2"])-(((data["AMT_GOODS_PRICE_x"])*(((1.105260)*(((((((((0.062500)<(data["PRODUCT_COMBINATION_Cash"])) *1.))*(0.323944)))<(data["PRODUCT_COMBINATION_Cash"])) *1.))))))))
 v["i118"] = np.tanh(np.where(data["AMT_CREDIT_x"]>0, np.where(data["AMT_ANNUITY_x"]>0,(((data["AMT_ANNUITY_x"])>(np.minimum(((0.494118)) ,(( data["AMT_CREDIT_x"])))))*1.) , -2.0),(((data["NAME_GOODS_CATEGORY_Direct_Sales"])>(0.043478)) *1.)))
v["i119"] = np.tanh(((((((data["FLAG_EMP_PHONE"])+(0.339286)))*(((((((( 8.0)) *(((data["FLAG_EMP_PHONE"])+(data["DAYS_EMPLOYED"])))))* 2.0)) * 2.0)))) * 2.0))
v["i120"] = np.tanh(((( -1.0*(((((0.680000)<(((( data["DAYS_BIRTH"])+(0.062500)) /2.0)))*1.))))) * 2.0))
v["i121"] = np.tanh(((( 0.062500)+(( -1.0*(((((data["LIVE_CITY_NOT_WORK_CITY"])>(np.minimum(((((data["cc_bal_SK_DPD_DEF"])+(((data["DAYS_EMPLOYED"])-(data["NAME_GOODS_CATEGORY_Insurance"])))))) ,(( 0.416667)))))*1.))))))/2.0))
v["i122"] = np.tanh(np.where(((( data["SK_ID_BUREAU"])>(2.0)) *1.) >0, data["SK_ID_BUREAU"],(((data["DAYS_ID_PUBLISH"])>(((((((0.0)+(3.526320)) /2.0)) +(data["DAYS_ID_PUBLISH"])) /2.0)))*1.)))
v["i123"] = np.tanh(((((data["te_FLAG_WORK_PHONE"])*(((( data["NAME_CASH_LOAN_PURPOSE_Furniture"])>(data["FLAG_DOCUMENT_2"])) *1.))))+(((((0.339286)*(np.minimum(((((data["te_FLAG_WORK_PHONE"])-(data["te_REG_REGION_NOT_LIVE_REGION"])))) ,(( data["NAME_CONTRACT_STATUS_Approved"])))))) / 2.0))))
v["i124"] = np.tanh(np.where(data["REGION_RATING_CLIENT_W_CITY"]>0,(((((0.036585)/ 2.0)) +(((( data["FLAG_DOCUMENT_2"])>(((( data["cc_bal_SK_DPD_DEF"])+(data["NAME_TYPE_SUITE_Unaccompanied"])) /2.0)))*1.))) /2.0),(((data["NAME_HOUSING_TYPE"])>(( -1.0*(( -3.0)))))*1.)))
v["i125"] = np.tanh(( -1.0*(( np.where(data["NAME_GOODS_CATEGORY_Insurance"]>0, data["te_OCCUPATION_TYPE"],(( data["te_OCCUPATION_TYPE"])*(((((((data["AMT_REQ_CREDIT_BUREAU_MON"])>(0.686567)) *1.))<(np.tanh(( data["AMT_REQ_CREDIT_BUREAU_MON"])))) *1.))))))))
 v["i126"] = np.tanh(( -1.0*(( np.minimum(((((data["EXT_SOURCE_1"])+(((data["EXT_SOURCE_1"])+(((((( 1.0)* 2.0)) +(3.0)) /2.0))))))),(((((np.tanh(( data["EXT_SOURCE_1"])))>(data["EXT_SOURCE_1"])) *1.))))))))
 v["i127"] = np.tanh(np.where(((( data["DAYS_BIRTH"])>(1.105260)) *1.) >0,(( 1.105260)-(data["DAYS_BIRTH"])) , np.where(data["te_NAME_INCOME_TYPE"]>0, data["DAYS_BIRTH"], np.tanh(( 0.031746)))))
 v["i128"] = np.tanh(np.tanh(( np.minimum(((0.576923)) ,(( np.where(((((((( data["FLAG_DOCUMENT_3"])-(( -1.0*(( data["DAYS_ID_PUBLISH"])))))) +(0.416667)) /2.0)) / 2.0)>0, 0.416667, data["DAYS_ID_PUBLISH"])))))))
 v["i129"] = np.tanh(((((( data["NAME_CASH_LOAN_PURPOSE_Furniture"])+(np.tanh(( data["NAME_CONTRACT_TYPE"])))) /2.0)) *(((data["CNT_FAM_MEMBERS"])+(((data["NAME_GOODS_CATEGORY_Clothing_and_Accessories"])*(((data["te_NAME_CONTRACT_TYPE"])-(data["FLAG_DOCUMENT_17"])))))))))
 v["i130"] = np.tanh(np.minimum(((((np.minimum(((data["FLAG_DOCUMENT_2"])) ,(((( data["AMT_CREDIT_x"])/ 2.0)))))*(data["AMT_REQ_CREDIT_BUREAU_MON"])))) ,(((((((( 0.697674)+(((0.339286)+(data["AMT_CREDIT_x"])))))* 2.0)) * 2.0)))))
 v["i131"] = np.tanh(((np.minimum(((((np.where(data["AMT_GOODS_PRICE_x"]>0, 3.526320, data["AMT_GOODS_PRICE_x"])) *(data["FLAG_DOCUMENT_17"])))) ,(((((( data["AMT_GOODS_PRICE_x"])* 2.0)) -(np.minimum(((-2.0)) ,(( data["FLAG_DOCUMENT_21"])))))))))* 2.0))
v["i132"] = np.tanh(((np.where(data["NAME_CLIENT_TYPE_Repeater"]>0, np.maximum(((0.062500)) ,(( data["FLAG_DOCUMENT_21"]))),(((( data["NAME_GOODS_CATEGORY_Direct_Sales"])*(0.036585)))-(((data["FLAG_DOCUMENT_21"])+(0.036585)))))) * 2.0))
v["i133"] = np.tanh(((((((-1.0*(((((((0.797872)/ 2.0)) >(((( -1.0*(( data["AMT_GOODS_PRICE_x"])))) * 2.0)))*1.))))) +(((( 1.220590)<(data["FLAG_DOCUMENT_5"])) *1.))) /2.0)) +(data["cc_bal_SK_DPD_DEF"])))
 v["i134"] = np.tanh(((( data["AMT_CREDIT_x"])>(((0.797872)*(((( np.minimum(((0.323944)) ,(((((((data["AMT_CREDIT_x"])/ 2.0)) +(0.339286)) /2.0)))))<(data["AMT_CREDIT_x"])) *1.))))) *1.))
 v["i135"] = np.tanh(( -1.0*(((( data["FLAG_DOCUMENT_15"])+(((( data["AMT_GOODS_PRICE_x"])>(((0.697674)+(np.minimum(((data["AMT_GOODS_PRICE_x"])) ,(((((0.12030962109565735)) -(data["AMT_GOODS_PRICE_x"])))))))))*1.)))))))
v["i136"] = np.tanh(((14.800000)*(((data["AMT_CREDIT_x"])-(((data["AMT_GOODS_PRICE_x"])-(((((((3.25225424766540527)) <(data["AMT_CREDIT_x"])) *1.))/ 2.0))))))))
v["i137"] = np.tanh(((((((( data["te_NAME_CONTRACT_TYPE"])<(((((( data["NAME_YIELD_GROUP_low_normal"])/ 2.0)) <(data["NAME_CONTRACT_TYPE"])) *1.))) *1.))/ 2.0)) *(((data["NAME_CONTRACT_TYPE"])+(np.tanh(( data["FLAG_WORK_PHONE"])))))))
 v["i138"] = np.tanh(((((((( 0.797872)*(data["AMT_CREDIT_x"])))+(((data["Completed"])/ 2.0)))) >(((((((( -1.0*(( data["AMT_CREDIT_x"])))) >(((data["Completed"])/ 2.0)))*1.))<(data["te_FLAG_DOCUMENT_8"])) *1.))) *1.))
 v["i139"] = np.tanh(((((((np.maximum(((((( data["cc_bal_AMT_CREDIT_LIMIT_ACTUAL"])+(1.857140)) /2.0))),(( data["inst_AMT_PAYMENT"])))) <(0.576923)) *1.))>(((( data["cc_bal_SK_DPD_DEF"])>(((data["FLAG_DOCUMENT_2"])*(1.857140)))) *1.))) *1.))
 v["i140"] = np.tanh(((((((((((( data["EXT_SOURCE_2"])<(( -1.0*(( 2.235290)))))*1.))* 2.0)) * 2.0)) +(np.tanh(( data["EXT_SOURCE_2"])))))/ 2.0))
v["i141"] = np.tanh(( -1.0*(((((data["AMT_GOODS_PRICE_x"])>(((((((((( 0.576923)/ 2.0)) <(data["AMT_GOODS_PRICE_x"])) *1.))* 2.0)) -(np.maximum(((((0.416667)/ 2.0))),(( data["AMT_GOODS_PRICE_x"])))))))*1.)))))
v["i142"] = np.tanh(((np.tanh(((((((((((data["AMT_GOODS_PRICE_x"])>(0.797872)) *1.))<(np.tanh(( data["AMT_GOODS_PRICE_x"])))) *1.))<(((( data["DAYS_LAST_DUE_1ST_VERSION"])<(np.tanh(( data["AMT_GOODS_PRICE_x"])))) *1.))) *1.))))/ 2.0))
v["i143"] = np.tanh(np.where(data["NAME_CONTRACT_STATUS_Refused"]>0,(-1.0*(((((data["CODE_REJECT_REASON_HC"])>(3.0)) *1.)))),(( 0.339286)*(((( 0.576923)<(((( data["ty__Consumer_credit"])>(0.797872)) *1.))) *1.)))))
v["i144"] = np.tanh(((np.minimum(((np.where(data["NAME_GOODS_CATEGORY_Computers"]>0,(( data["AMT_CREDIT_y"])/ 2.0),(((((data["AMT_CREDIT_y"])<(data["NAME_GOODS_CATEGORY_Insurance"])) *1.))/ 2.0)))) ,(( np.where(data["NAME_GOODS_CATEGORY_Computers"]>0, data["AMT_CREDIT_y"], data["YEARS_BUILD_AVG"])))))/ 2.0))
v["i145"] = np.tanh(np.where(data["NAME_CASH_LOAN_PURPOSE_Furniture"]>0,(((data["FLAG_DOCUMENT_14"])+(data["FLAG_DOCUMENT_18"])) /2.0), np.minimum(((( -1.0*(((((data["FLAG_DOCUMENT_18"])+(((data["FLAG_DOCUMENT_14"])+(data["FLAG_DOCUMENT_17"])))) /2.0)))))) ,(((-1.0*(( data["FLAG_DOCUMENT_18"]))))))))
v["i146"] = np.tanh(np.where(data["NAME_GOODS_CATEGORY_Insurance"]>0, data["FLAG_DOCUMENT_18"],(( data["cc_bal_SK_DPD_DEF"])-(((data["FLAG_DOCUMENT_18"])*(((data["te_FLAG_DOCUMENT_14"])+(np.minimum(((((data["te_FLAG_DOCUMENT_14"])* 2.0))),(( data["FLAG_DOCUMENT_18"])))))))))))
 v["i147"] = np.tanh(((((( np.tanh(((( data["ca__Bad_debt"])+(np.tanh(( 1.857140)))))))<(data["AMT_DOWN_PAYMENT"])) *1.))+(np.minimum(((data["NAME_GOODS_CATEGORY_Insurance"])) ,(((((data["te_FLAG_DOCUMENT_18"])+(( -1.0*(( data["AMT_DOWN_PAYMENT"])))))/2.0)))))))
 v["i148"] = np.tanh(np.where(((( 0.680000)<(data["AMT_DOWN_PAYMENT"])) *1.) >0,(( data["AMT_DOWN_PAYMENT"])/ 2.0),(( data["NAME_GOODS_CATEGORY_Insurance"])-(((np.maximum(((np.tanh(( data["SK_DPD"])))) ,(( data["AMT_DOWN_PAYMENT"])))) / 2.0)))))
 v["i149"] = np.tanh(((((data["MONTHS_BALANCE"])*(((3.526320)* 2.0)))) *(((data["NAME_GOODS_CATEGORY_Insurance"])+(( -1.0*(((((((data["inst_AMT_PAYMENT"])* 2.0)) >(((( data["ca__Bad_debt"])+(11.714300)) /2.0)))*1.)))))))))
v["i150"] = np.tanh(((((( -1.0)+(((1.857140)/ 2.0)))) <(np.where(data["SK_DPD"]>0, -1.0, np.tanh(( np.where(data["NAME_GOODS_CATEGORY_Insurance"]>0, 1.857140, data["SK_DPD"])))))) *1.))
 v["i151"] = np.tanh(( -1.0*(((((((data["inst_SK_ID_PREV"])>(((( np.tanh(( 0.697674)))+(np.minimum(((((0.697674)+(((((data["inst_SK_ID_PREV"])* 2.0)) * 2.0))))),(( data["HOUSETYPE_MODE"])))))/2.0)))*1.))/ 2.0)))))
 v["i152"] = np.tanh(((((((0.031746)*(((np.maximum(((( -1.0*(( data["WALLSMATERIAL_MODE"]))))),(( 0.686567)))) -(data["WALLSMATERIAL_MODE"])))))*(np.maximum(((( -1.0*(( data["EXT_SOURCE_2"]))))),(( data["SK_DPD"])))))) * 2.0))
v["i153"] = np.tanh(((((((data["CNT_INSTALMENT_FUTURE"])>(((np.maximum(((data["CNT_INSTALMENT_FUTURE"])) ,(( data["NAME_GOODS_CATEGORY_Fitness"])))) * 2.0)))*1.))+(((((( data["CNT_INSTALMENT_FUTURE"])>(3.0)) *1.))-(((data["FLAG_DOCUMENT_17"])* 2.0)))))/2.0))
v["i154"] = np.tanh(((np.where(data["NAME_TYPE_SUITE_Unaccompanied"]>0,(( data["FLAG_DOCUMENT_21"])+(data["FLAG_DOCUMENT_21"])) ,(( 0.043478)+(((( data["FLAG_DOCUMENT_21"])>(np.where(data["NAME_GOODS_CATEGORY_Insurance"]>0, data["SK_DPD"], 0.576923)))*1.))))) * 2.0))
v["i155"] = np.tanh(np.maximum(((data["cc_bal_SK_DPD_DEF"])) ,(((( data["SK_DPD"])-(((((((data["SK_DPD"])* 2.0)) *(data["cc_bal_cc_bal_status__Sent_proposal"])))-(((1.0)-(((11.714300)* 2.0)))))))))))
 v["i156"] = np.tanh(np.where(data["NAME_FAMILY_STATUS"]>0, data["DAYS_EMPLOYED"], np.where(data["NONLIVINGAPARTMENTS_MODE"]>0, data["DAYS_EMPLOYED"], np.minimum(((0.697674)) ,(( np.where(data["DAYS_EMPLOYED"]>0, data["AMT_CREDIT_x"], 0.031746)))))))
 v["i157"] = np.tanh(np.where(data["te_NAME_INCOME_TYPE"]>0, np.where(data["NONLIVINGAPARTMENTS_MODE"]>0, data["te_FLAG_WORK_PHONE"], np.where(data["NAME_GOODS_CATEGORY_Fitness"]>0, data["te_NAME_INCOME_TYPE"], 0.043478)) , np.where(data["NAME_GOODS_CATEGORY_Fitness"]>0, data["te_NAME_INCOME_TYPE"], data["NONLIVINGAPARTMENTS_MODE"])))
 v["i158"] = np.tanh(np.where(data["cc_bal_SK_DPD_DEF"]>0,(( data["cc_bal_cc_bal_status__Sent_proposal"])-(0.323944)) , np.minimum(((((data["NONLIVINGAREA_MODE"])*(( -1.0*(( 0.323944))))))),(((-1.0*(( data["cc_bal_cc_bal_status__Sent_proposal"]))))))))
v["i159"] = np.tanh(((((((( np.where(( -1.0*(( data["cc_bal_SK_DPD_DEF"])))>0, data["ORGANIZATION_TYPE"],(( data["NAME_GOODS_CATEGORY_Fitness"])/ 2.0)))-(-3.0)))<(data["ORGANIZATION_TYPE"])) *1.))* 2.0))
v["i160"] = np.tanh(((((( -1.0*(((-1.0*(((( data["NAME_GOODS_CATEGORY_Fitness"])*(((data["FLAG_DOCUMENT_13"])-(((data["HOUSETYPE_MODE"])+(data["NONLIVINGAPARTMENTS_MODE"])))))))))))))* 2.0)) * 2.0))
v["i161"] = np.tanh(np.where(data["WALLSMATERIAL_MODE"]>0,(((((-1.0*(( 0.062500)))) *(((data["WALLSMATERIAL_MODE"])+(data["NAME_GOODS_CATEGORY_Fitness"])))))* 2.0), 0.062500))
v["i162"] = np.tanh(((data["NAME_GOODS_CATEGORY_Direct_Sales"])*(((((3.526320)+(((data["te_WALLSMATERIAL_MODE"])+(((((data["FLAG_DOCUMENT_2"])+(((data["NAME_CASH_LOAN_PURPOSE_Furniture"])* 2.0)))) * 2.0)))))) *(data["te_WALLSMATERIAL_MODE"])))))
 v["i163"] = np.tanh(((((np.maximum(((data["ca__Bad_debt"])) ,(((((((11.714300)<(((( data["NAME_GOODS_CATEGORY_Direct_Sales"])+(data["NAME_CASH_LOAN_PURPOSE_Gasification___water_supply"])) /2.0)))*1.))* 2.0)))))-(np.minimum(((1.105260)) ,(( data["NAME_CASH_LOAN_PURPOSE_Gasification___water_supply"])))))) * 2.0))
v["i164"] = np.tanh(((((data["FLAG_WORK_PHONE"])*(((0.043478)-(np.minimum(((((((data["NAME_GOODS_CATEGORY_Fitness"])*(data["NAME_CASH_LOAN_PURPOSE_Furniture"])))* 2.0))),(((-1.0*(( data["NAME_SELLER_INDUSTRY_MLM_partners"])))))))))))* 2.0))
v["i165"] = np.tanh(((((data["PRODUCT_COMBINATION_Card_X_Sell"])*(((( data["PRODUCT_COMBINATION_Card_X_Sell"])>(np.where(np.where(data["cc_bal_AMT_BALANCE"]>0, 2.0, data["cc_bal_AMT_BALANCE"])>0, 2.0, 11.714300)))*1.))))-(((data["FLAG_DOCUMENT_15"])/ 2.0))))
v["i166"] = np.tanh(((((np.minimum(((data["NAME_SELLER_INDUSTRY_Auto_technology"])) ,(((( 0.797872)-(data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])))))) -(((((( 0.797872)*(((data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])/ 2.0)))) <(data["cc_bal_AMT_CREDIT_LIMIT_ACTUAL"])) *1.))))/ 2.0))
v["i167"] = np.tanh(((( 3.0)<(((0.323944)+(np.maximum(((np.maximum(((data["cc_bal_AMT_BALANCE"])) ,(( data["AMT_CREDIT_y"]))))),(((( np.where(data["cc_bal_CNT_DRAWINGS_POS_CURRENT"]>0, data["cc_bal_AMT_BALANCE"], 3.0)) +(data["cc_bal_CNT_DRAWINGS_POS_CURRENT"])))))))))*1.))
 v["i168"] = np.tanh(((((np.minimum(((((( 1.78492116928100586)) -(data["AMT_CREDIT_SUM"])))) ,(((((0.797872)>(((((( 1.220590)>(data["AMT_CREDIT_SUM"])) *1.))+(((data["AMT_CREDIT_SUM"])/ 2.0)))))*1.))))) * 2.0)) * 2.0))
v["i169"] = np.tanh(np.where(np.where(((( data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])>(data["NAME_CASH_LOAN_PURPOSE_Furniture"])) *1.) >0, 3.526320,(((data["PRODUCT_COMBINATION_Cash_X_Sell__middle"])>(3.0)) *1.))>0,(((data["PRODUCT_COMBINATION_Cash_X_Sell__middle"])>(3.0)) *1.) , data["SK_ID_BUREAU"]))
v["i170"] = np.tanh(((np.minimum(((data["NFLAG_INSURED_ON_APPROVAL"])) ,(( 0.680000)))) *(((( data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])<(np.tanh(( np.tanh(((((-1.0*(( np.maximum(((0.576923)) ,(((( data["NFLAG_INSURED_ON_APPROVAL"])/ 2.0)))))))) / 2.0)))))))*1.))))
 v["i171"] = np.tanh(((((( 0.031746)+(((((data["NAME_PORTFOLIO_Cash"])/ 2.0)) / 2.0)))/2.0)) -(((((data["inst_SK_ID_PREV"])/ 2.0)) *(((((data["inst_SK_ID_PREV"])/ 2.0)) *(data["NAME_PORTFOLIO_Cash"])))))))
 v["i172"] = np.tanh(((((( 1.105260)>(data["PRODUCT_COMBINATION_Cash"])) *1.))*(np.minimum(((np.minimum(((0.062500)) ,(((( data["PRODUCT_COMBINATION_Cash"])*(data["inst_AMT_PAYMENT"]))))))),(((( data["NFLAG_INSURED_ON_APPROVAL"])*(data["inst_AMT_PAYMENT"]))))))))
v["i173"] = np.tanh(((((((3.526320)*(((((np.maximum(((((( data["AMT_CREDIT_MAX_OVERDUE"])>(np.tanh(( data["AMT_CREDIT_MAX_OVERDUE"])))) *1.))) ,(( data["NAME_CASH_LOAN_PURPOSE_Medicine"])))) +(data["AMT_CREDIT_MAX_OVERDUE"])))* 2.0)))) * 2.0)) * 2.0))
v["i174"] = np.tanh(((( data["avg_buro_buro_bal_status_2"])<(((np.where(data["CNT_INSTALMENT_FUTURE"]>0, np.minimum(((data["DAYS_CREDIT"])) ,(( np.minimum(((data["DAYS_CREDIT"])) ,(((( 1.220590)-(data["DAYS_CREDIT"])))))))) , data["CNT_INSTALMENT_FUTURE"])) / 2.0)))*1.))
 v["i175"] = np.tanh(((np.tanh(( data["EXT_SOURCE_3"])))*(((0.416667)-(((((((( data["EXT_SOURCE_3"])<(np.minimum(((np.tanh(( data["FLAG_DOCUMENT_2"])))) ,(( -2.0)))))*1.))* 2.0)) * 2.0))))))
v["i176"] = np.tanh(np.where(data["SK_DPD"]>0, data["CNT_INSTALMENT"],(((((((np.maximum(((data["NAME_SELLER_INDUSTRY_Connectivity"])) ,(( data["NAME_SELLER_INDUSTRY_Connectivity"])))) * 2.0)) *(data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])))+(((data["CNT_INSTALMENT"])*(data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])))) /2.0)))
 v["i177"] = np.tanh(((data["CNT_INSTALMENT"])*(((( -1.0*(( np.maximum(((((((((((0.339286)<(data["FLAG_DOCUMENT_2"])) *1.))+(data["DAYS_CREDIT"])) /2.0)) *(data["DAYS_CREDIT"])))) ,(((-1.0*(( 0.0)))))))))) / 2.0))))
v["i178"] = np.tanh(np.maximum(((((((((( data["cc_bal_AMT_DRAWINGS_OTHER_CURRENT"])>(((3.0)/ 2.0)))*1.))* 2.0)) * 2.0))),(((((3.0)<(((data["CNT_INSTALMENT_FUTURE"])+(((( data["cc_bal_SK_DPD_DEF"])>(data["NAME_GOODS_CATEGORY_Weapon"])) *1.))))) *1.)))))
v["i179"] = np.tanh(((np.where(data["SK_DPD"]>0, data["CNT_INSTALMENT"], data["AMT_REQ_CREDIT_BUREAU_DAY"])) +(((( data["NAME_CASH_LOAN_PURPOSE_Furniture"])<(((( data["SK_DPD"])+(((data["AMT_REQ_CREDIT_BUREAU_DAY"])+(np.maximum(((data["AMT_REQ_CREDIT_BUREAU_DAY"])) ,(( data["AMT_REQ_CREDIT_BUREAU_DAY"])))))))/2.0)))*1.))))
 v["i180"] = np.tanh(((np.minimum(((((( data["NAME_SELLER_INDUSTRY_Connectivity"])>(((np.tanh(( data["NAME_SELLER_INDUSTRY_Connectivity"])))*(1.857140)))) *1.))) ,(( np.where(data["DAYS_CREDIT"]>0, 0.0, np.tanh(( data["NAME_SELLER_INDUSTRY_Connectivity"])))))))/ 2.0))
v["i181"] = np.tanh(((( -1.0*(( np.tanh(( np.where(np.tanh(( data["cc_bal_SK_DPD_DEF"])) >0, data["NAME_YIELD_GROUP_high"], np.where(data["NAME_YIELD_GROUP_high"]>0, np.where(data["SK_DPD"]>0, data["NAME_YIELD_GROUP_high"], data["SK_DPD"]), 0.036585)))))))) * 2.0))
v["i182"] = np.tanh(np.minimum(((((((2.0)+(data["PRODUCT_COMBINATION_POS_mobile_with_interest"])))-(((data["NAME_YIELD_GROUP_high"])* 2.0))))),(((( np.minimum(((0.323944)) ,(( 0.680000)))) / 2.0)))))
 v["i183"] = np.tanh(((((((((data["PRODUCT_COMBINATION_Cash_Street__high"])>(np.tanh(( 0.416667)))) *1.))+(((( data["SK_DPD"])+(((((( -1.0*(( data["PRODUCT_COMBINATION_Cash_Street__high"])))) *(data["SK_DPD"])))/ 2.0)))/2.0)))/2.0)) / 2.0))
v["i184"] = np.tanh(((( 2.0)<(((data["PRODUCT_COMBINATION_POS_household_with_interest"])+(((data["LIVINGAPARTMENTS_MEDI"])-(((((0.0)* 2.0)) -(((np.where(data["cc_bal_cc_bal_status__Refused"]>0, data["cc_bal_cc_bal_status__Refused"], data["LIVINGAPARTMENTS_MEDI"])) * 2.0)))))))))*1.))
 v["i185"] = np.tanh(((((data["ENTRANCES_MEDI"])*(((data["NAME_GOODS_CATEGORY_Insurance"])* 2.0)))) -(np.tanh(((((np.tanh(((((data["ENTRANCES_MEDI"])+(0.576923)) /2.0)))) +(np.tanh(( data["NAME_CASH_LOAN_PURPOSE_Furniture"])))) /2.0))))))
v["i186"] = np.tanh(((np.where(data["CODE_REJECT_REASON_VERIF"]>0, data["DAYS_FIRST_DUE"], np.maximum(((((0.062500)*(data["DAYS_FIRST_DUE"])))) ,(((-1.0*(( data["NAME_GOODS_CATEGORY_Weapon"])))))))) -(np.maximum(((data["FLAG_DOCUMENT_17"])) ,(( data["NAME_GOODS_CATEGORY_Weapon"]))))))
v["i187"] = np.tanh(((((data["NAME_GOODS_CATEGORY_Other"])-(((((( data["NAME_GOODS_CATEGORY_Other"])/ 2.0)) +(( 10.0)))/2.0)))) *(np.maximum(((data["NAME_GOODS_CATEGORY_Other"])) ,(( 0.0))))))
v["i188"] = np.tanh(np.minimum(((data["ty__Another_type_of_loan"])) ,(((( data["cc_bal_SK_DPD"])-(np.where(data["FLAG_DOCUMENT_14"]>0, data["FLAG_DOCUMENT_14"],(((((data["FLAG_DOCUMENT_14"])+(np.maximum(((data["NAME_GOODS_CATEGORY_Fitness"])) ,(( data["ty__Another_type_of_loan"])))))/2.0)) / 2.0))))))))
v["i189"] = np.tanh(((( -1.0*(((( 0.697674)-(((((( 0.680000)-(((( data["NAME_PORTFOLIO_POS"])<(0.680000)) *1.))))<(np.tanh(((((0.680000)+(data["NAME_PORTFOLIO_POS"])) /2.0)))))*1.))))))) / 2.0))
v["i190"] = np.tanh(np.minimum(((((np.where(data["NAME_GOODS_CATEGORY_XNA"]>0,(( data["NAME_GOODS_CATEGORY_XNA"])/ 2.0),(( data["PRODUCT_COMBINATION_Cash_Street__high"])/ 2.0)))*(np.minimum(((( 1.0))),(( data["PRODUCT_COMBINATION_Cash_Street__high"]))))))),(((((( data["NAME_GOODS_CATEGORY_XNA"])/ 2.0)) / 2.0)))))
 v["i191"] = np.tanh(np.tanh(( np.where(data["cc_bal_cc_bal_status__Refused"]>0,(( np.tanh(((( data["NFLAG_LAST_APPL_IN_DAY"])+(((((((data["NFLAG_LAST_APPL_IN_DAY"])-(0.062500)))* 2.0)) -(0.062500)))))))/ 2.0), data["NFLAG_LAST_APPL_IN_DAY"]))))
v["i192"] = np.tanh(((np.maximum(((data["cc_bal_SK_DPD"])) ,(((((2.0)<(((data["AMT_CREDIT_x"])*(np.where(data["cc_bal_SK_DPD"]>0, 0.686567, 1.220590)))))*1.))))) *(((data["AMT_CREDIT_x"])-(0.043478)))))
 v["i193"] = np.tanh(((np.minimum(((data["cc_bal_SK_DPD_DEF"])) ,(( data["cc_bal_SK_DPD"])))) -(np.where(( 10.0)>0,(((0.323944)<(((data["YEARS_BEGINEXPLUATATION_MEDI"])+(((( data["cc_bal_SK_DPD_DEF"])+(data["YEARS_BEGINEXPLUATATION_MEDI"])) /2.0)))))*1.) , 0.043478))))
v["i194"] = np.tanh(((( data["LIVINGAREA_MODE"])>(np.maximum(((data["TOTALAREA_MODE"])) ,(( np.where(((data["FLOORSMAX_AVG"])*(np.maximum(((data["LIVINGAREA_AVG"])) ,(( data["TOTALAREA_MODE"])))))>0,(( data["LIVINGAREA_AVG"])/ 2.0), data["LIVINGAREA_MODE"])))))) *1.))
 v["i195"] = np.tanh(((( -1.0*(( np.where(data["WALLSMATERIAL_MODE"]>0, np.where(((0.494118)*(data["FLAG_CONT_MOBILE"])) >0, 0.686567, data["FLAG_CONT_MOBILE"]),(((((0.036585)/ 2.0)) >(0.686567)) *1.))))))/ 2.0))
v["i196"] = np.tanh(((((((data["NAME_CASH_LOAN_PURPOSE_Everyday_expenses"])*(data["NAME_CASH_LOAN_PURPOSE_Buying_a_home"])))+(((( -1.0*(( data["NAME_CASH_LOAN_PURPOSE_Buying_a_home"])))) +(((data["NAME_CASH_LOAN_PURPOSE_Everyday_expenses"])*(data["cc_bal_SK_DPD_DEF"])))))))* 2.0))
v["i197"] = np.tanh(np.where(np.where(data["cc_bal_cc_bal_status__Refused"]>0,(((data["cu__currency_3"])<(0.323944)) *1.) , np.where(data["FLAG_DOCUMENT_2"]>0, data["FLAG_EMAIL"], 1.220590)) >0,(( data["cc_bal_cc_bal_status__Refused"])+(data["cu__currency_3"])) , data["FLAG_DOCUMENT_2"]))
v["i198"] = np.tanh(((((np.maximum(((data["FLAG_DOCUMENT_2"])) ,(((((data["NAME_GOODS_CATEGORY_Medicine"])>(data["cc_bal_SK_DPD_DEF"])) *1.))))) +(((((data["NAME_YIELD_GROUP_XNA"])* 2.0)) *(data["cc_bal_SK_DPD_DEF"])))))* 2.0))
v["i199"] = np.tanh(np.where(((( 0.680000)+(data["DAYS_FIRST_DRAWING"])) /2.0)>0, np.where(data["DAYS_FIRST_DRAWING"]>0,(-1.0*(( 0.062500))),(((10.75054359436035156)) / 2.0)) , 0.043478))
v["i200"] = np.tanh(np.where(data["NAME_GOODS_CATEGORY_Direct_Sales"]>0, data["FLAG_EMAIL"], np.where(data["cc_bal_SK_DPD_DEF"]>0, data["FLAG_EMAIL"], np.tanh(((((0.043478)+(((((0.494118)*(( -1.0*(( data["FLAG_EMAIL"])))))) / 2.0)))/2.0))))))
v["i201"] = np.tanh(((((data["te_FLAG_EMAIL"])*(np.where(((((14.800000)/ 2.0)) -(((data["NAME_YIELD_GROUP_XNA"])+(((data["NAME_TYPE_SUITE_Group_of_people"])-(data["te_FLAG_EMAIL"])))))) >0, data["NAME_TYPE_SUITE_Group_of_people"], data["NAME_YIELD_GROUP_XNA"])))) * 2.0))
v["i202"] = np.tanh(((data["NAME_TYPE_SUITE_Group_of_people"])*(((((((( np.maximum(((((0.031746)/ 2.0))),(( data["FLAG_DOCUMENT_10"])))) -(1.105260)))-(data["NAME_TYPE_SUITE_Group_of_people"])))+(data["NAME_CASH_LOAN_PURPOSE_Furniture"])) /2.0))))
v["i203"] = np.tanh(((np.where(data["NAME_YIELD_GROUP_XNA"]>0, data["PRODUCT_COMBINATION_POS_household_without_interest"], 0.043478)) *(((( 0.062500)<(((((( 1.220590)>(data["NAME_YIELD_GROUP_XNA"])) *1.))-(data["cc_bal_SK_DPD_DEF"])))) *1.))))
 v["i204"] = np.tanh(((np.minimum(((((( data["PRODUCT_COMBINATION_Cash"])>(((np.tanh(( 0.416667)))-(((np.tanh(( data["NAME_YIELD_GROUP_XNA"])))+(data["NAME_PORTFOLIO_POS"])))))) *1.))) ,(((((0.680000)<(data["NAME_YIELD_GROUP_XNA"])) *1.))))) / 2.0))
v["i205"] = np.tanh(( -1.0*(( np.maximum(((((-2.0)*(0.043478)))) ,(((( data["NAME_CONTRACT_STATUS_Approved"])*(((data["AMT_REQ_CREDIT_BUREAU_WEEK"])+(((data["CNT_INSTALMENT"])*(data["AMT_REQ_CREDIT_BUREAU_WEEK"])))))))))))))
 v["i206"] = np.tanh(np.where(((data["cc_bal_SK_DPD_DEF"])*(( -1.0*(( data["CODE_REJECT_REASON_LIMIT"])))))>0, data["NAME_GOODS_CATEGORY_XNA"],(( 0.043478)*(np.where(data["CODE_REJECT_REASON_LIMIT"]>0, 3.0, 0.043478)))))
 v["i207"] = np.tanh(np.minimum(((data["cc_bal_SK_DPD"])) ,(((( data["cc_bal_SK_DPD"])*(np.where(data["FLAG_EMAIL"]>0, -3.0,(((((11.714300)<(data["cc_bal_SK_DPD"])) *1.))+(data["FLAG_EMAIL"])))))))))
 v["i208"] = np.tanh(((((( 0.797872)<(data["inst_AMT_INSTALMENT"])) *1.))*(((((( 0.797872)+(((((( 2.0)/ 2.0)) >(data["inst_AMT_INSTALMENT"])) *1.))))>(np.tanh(( data["AMT_CREDIT_y"])))) *1.))))
 v["i209"] = np.tanh(((( -1.0*(((((np.where(data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"]>0, 11.714300,(( np.where(0.323944>0, 0.323944, data["inst_AMT_PAYMENT"])) * 2.0)))<(((np.tanh(( data["inst_AMT_PAYMENT"])))* 2.0)))*1.))))) / 2.0))
v["i210"] = np.tanh(((( data["inst_AMT_PAYMENT"])<(( -1.0*(( np.maximum(((np.tanh(( 0.680000)))) ,(( np.where(data["NAME_PORTFOLIO_Cards"]>0, 0.680000,(((0.043478)>(( -1.0*(( data["NAME_PORTFOLIO_Cards"])))))*1.))))))))))*1.))
 v["i211"] = np.tanh(((((( data["NAME_CONTRACT_TYPE_Consumer_loans"])*(data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])))+(((((( data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])+(np.minimum(((((data["AMT_CREDIT_y"])/ 2.0))),(( data["AMT_CREDIT_y"])))))/2.0)) *(((( data["NAME_CONTRACT_TYPE_Consumer_loans"])>(0.339286)) *1.))))) /2.0))
v["i212"] = np.tanh(np.where(((data["NAME_PORTFOLIO_Cards"])+(((0.036585)* 2.0)))>0,(((((( data["NAME_GOODS_CATEGORY_Insurance"])<(data["cc_bal_AMT_DRAWINGS_ATM_CURRENT"])) *1.))<(((( data["cc_bal_AMT_DRAWINGS_ATM_CURRENT"])>(data["cc_bal_AMT_PAYMENT_CURRENT"])) *1.))) *1.) , data["cc_bal_AMT_DRAWINGS_ATM_CURRENT"]))
v["i213"] = np.tanh(((data["cc_bal_CNT_DRAWINGS_CURRENT"])*(((((data["NAME_GOODS_CATEGORY_Photo___Cinema_Equipment"])*(data["NAME_GOODS_CATEGORY_Photo___Cinema_Equipment"])))-(np.minimum(((data["cc_bal_AMT_PAYMENT_CURRENT"])) ,(((( 11.714300)*(((( data["NAME_GOODS_CATEGORY_Direct_Sales"])>(data["cc_bal_CNT_DRAWINGS_CURRENT"])) *1.)))))))))))
v["i214"] = np.tanh(np.where(np.tanh(( data["CODE_REJECT_REASON_CLIENT"])) >0, data["cc_bal_AMT_DRAWINGS_ATM_CURRENT"],(( np.where(data["cc_bal_AMT_DRAWINGS_ATM_CURRENT"]>0,(((( data["NAME_GOODS_CATEGORY_Direct_Sales"])/ 2.0)) / 2.0),(( np.minimum(((data["NAME_GOODS_CATEGORY_Photo___Cinema_Equipment"])) ,(( data["NAME_PAYMENT_TYPE_Cash_through_the_bank"])))) / 2.0)))/ 2.0)))
 v["i215"] = np.tanh(((((((0.062500)-(((data["FLAG_EMAIL"])*(( -1.0*(((( data["FLAG_DOCUMENT_4"])+(data["NAME_GOODS_CATEGORY_Direct_Sales"])))))))))) -(data["NAME_GOODS_CATEGORY_Weapon"])))-(((data["FLAG_EMAIL"])*(0.062500)))))
 v["i216"] = np.tanh(( -1.0*(((((( data["cc_bal_cc_bal_status__Sent_proposal"])+(data["FLAG_CONT_MOBILE"])))*(((0.323944)+(((-1.0)*(data["NAME_YIELD_GROUP_XNA"]))))))))))
v["i217"] = np.tanh(((((( 1.857140)<(data["avg_buro_buro_bal_status_2"])) *1.))-(np.maximum(((((( 0.031746)<(((np.maximum(((data["cc_bal_SK_DPD_DEF"])) ,(( data["avg_buro_buro_bal_status_2"])))) * 2.0)))*1.))) ,(( data["cc_bal_cc_bal_status__Sent_proposal"]))))))
v["i218"] = np.tanh(((data["cc_bal_SK_ID_PREV"])*(((((( 0.031746)<(data["FLAG_OWN_CAR"])) *1.))+(((((( data["cc_bal_SK_ID_PREV"])>(1.857140)) *1.))-(0.062500)))))))
 v["i219"] = np.tanh(((( data["cc_bal_AMT_DRAWINGS_ATM_CURRENT"])>(((( 3.0)+(( -1.0*(( np.where(((data["NAME_CASH_LOAN_PURPOSE_Car_repairs"])+(data["DAYS_LAST_DUE"])) >0,(( data["NAME_CASH_LOAN_PURPOSE_Car_repairs"])+(data["DAYS_LAST_DUE"])) , 3.0)))))) /2.0)))*1.))
 v["i220"] = np.tanh(( -1.0*(((((0.680000)<(((data["cc_bal_CNT_DRAWINGS_ATM_CURRENT"])*(((np.maximum(((0.043478)) ,(((((( 0.680000)* 2.0)) *(data["DAYS_FIRST_DUE"])))))) +(((data["cc_bal_CNT_DRAWINGS_ATM_CURRENT"])* 2.0)))))))*1.)))))
v["i221"] = np.tanh(np.where(data["cc_bal_SK_DPD_DEF"]>0, np.minimum(((data["DAYS_LAST_DUE"])) ,(( data["PRODUCT_COMBINATION_Card_Street"]))),(((((((((data["cc_bal_CNT_DRAWINGS_POS_CURRENT"])<(((data["DAYS_LAST_DUE"])+(data["DAYS_LAST_DUE"])))) *1.))+(0.062500)) /2.0)) <(data["cc_bal_CNT_DRAWINGS_POS_CURRENT"])) *1.)))
v["i222"] = np.tanh(((( data["inst_AMT_PAYMENT"])<(( -1.0*(((((((-1.0*(( data["inst_AMT_PAYMENT"])))) / 2.0)) +(0.323944)))))))*1.))
 v["i223"] = np.tanh(((((((data["cc_bal_SK_DPD_DEF"])+(np.where(data["cc_bal_SK_DPD"]>0, 1.105260,(((data["cc_bal_SK_DPD"])<(data["NAME_SELLER_INDUSTRY_Auto_technology"])) *1.))))/2.0)) <(((( data["NAME_SELLER_INDUSTRY_Auto_technology"])>(14.800000)) *1.))) *1.))
 v["i224"] = np.tanh(np.where(data["cc_bal_SK_DPD_DEF"]>0,(-1.0*(( data["DAYS_FIRST_DUE"]))),(((( data["cc_bal_SK_DPD_DEF"])-(((data["NAME_GOODS_CATEGORY_Direct_Sales"])*(data["DAYS_FIRST_DUE"])))))* 2.0)))
 v["i225"] = np.tanh(((((( data["cc_bal_SK_DPD"])>(((14.800000)-(np.maximum(((((np.where(data["cc_bal_SK_DPD"]>0,(((data["NAME_GOODS_CATEGORY_Direct_Sales"])+(data["NAME_GOODS_CATEGORY_Insurance"])) /2.0), data["FLAG_DOCUMENT_2"])) * 2.0))),(( data["NAME_GOODS_CATEGORY_Direct_Sales"])))))))*1.))* 2.0))
v["i226"] = np.tanh(((((( 3.0)<(((( data["NAME_CASH_LOAN_PURPOSE_Business_development"])+(data["CHANNEL_TYPE_Car_dealer"])) /2.0)))*1.))-(np.where(data["CHANNEL_TYPE_Car_dealer"]>0, data["CHANNEL_TYPE_Car_dealer"],(( data["FLAG_DOCUMENT_17"])*(( -1.0*(( data["NAME_CASH_LOAN_PURPOSE_Purchase_of_electronic_equipment"])))))))))
 v["i227"] = np.tanh(((0.036585)+(((0.062500)+(np.where(data["AMT_CREDIT_MAX_OVERDUE"]>0,(( data["AMT_CREDIT_MAX_OVERDUE"])*(14.800000)) ,(( 0.036585)+(((data["AMT_CREDIT_MAX_OVERDUE"])*(14.800000))))))))))
v["i228"] = np.tanh(np.minimum(((( -1.0*(((( np.tanh(( data["AMT_DOWN_PAYMENT"])))/ 2.0)))))) ,(((((data["REGION_RATING_CLIENT_W_CITY"])+(((( data["AMT_DOWN_PAYMENT"])<(data["DAYS_CREDIT"])) *1.))) /2.0)))))
 v["i229"] = np.tanh(((((( -2.0)>(data["te_REGION_RATING_CLIENT"])) *1.))*(((((((((data["te_WALLSMATERIAL_MODE"])*(data["AMT_CREDIT_y"])))+(((data["AMT_GOODS_PRICE_y"])*(data["te_REGION_RATING_CLIENT"])))))* 2.0)) * 2.0))))
v["i230"] = np.tanh(np.minimum(((((data["inst_AMT_INSTALMENT"])+(((((( data["AMT_APPLICATION"])>(data["te_WALLSMATERIAL_MODE"])) *1.))* 2.0))))),(((((((( data["inst_AMT_INSTALMENT"])<(0.797872)) *1.))<(( -1.0*(( data["AMT_DOWN_PAYMENT"])))))*1.)))))
v["i231"] = np.tanh(np.where(data["FLAG_OWN_CAR"]>0,(((data["NAME_CASH_LOAN_PURPOSE_Business_development"])+(((((((data["CNT_INSTALMENT"])<(( -1.0*(( data["NAME_GOODS_CATEGORY_Insurance"])))))*1.))+(( -1.0*(( 1.0)))))/2.0)))/2.0),(( np.tanh(( 0.062500)))* 2.0)))
 v["i232"] = np.tanh(((((0.031746)+(((data["CODE_GENDER"])*(( -1.0*(((((data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])<(data["NAME_GOODS_CATEGORY_Insurance"])) *1.))))))))) +(((np.tanh(( data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])))*(( -1.0*(( 0.797872))))))))
v["i233"] = np.tanh(((( data["cc_bal_AMT_RECIVABLE"])>(np.maximum(((data["cc_bal_AMT_RECEIVABLE_PRINCIPAL"])) ,(( np.tanh(( data["cc_bal_AMT_RECEIVABLE_PRINCIPAL"])))))))*1.))
 v["i234"] = np.tanh(( -1.0*(((( np.where(( -1.0*(( data["DAYS_LAST_DUE_1ST_VERSION"])))>0, data["inst_AMT_PAYMENT"], np.tanh(( np.tanh(( data["inst_SK_ID_PREV"])))))) / 2.0)))))
 v["i235"] = np.tanh(((((data["inst_AMT_INSTALMENT"])*(((((((((data["AMT_GOODS_PRICE_y"])*(((1.105260)/ 2.0)))) >(1.0)) *1.))<(((data["AMT_GOODS_PRICE_y"])-(1.105260)))) *1.))))* 2.0))
v["i236"] = np.tanh(((data["AMT_DOWN_PAYMENT"])*(((((np.tanh(((((data["inst_AMT_PAYMENT"])>(((data["te_OCCUPATION_TYPE"])*(0.036585)))) *1.))))-(np.where(data["inst_AMT_PAYMENT"]>0, data["te_OCCUPATION_TYPE"], data["AMT_DOWN_PAYMENT"])))) / 2.0))))
v["i237"] = np.tanh(((((((-1.0*(( data["inst_AMT_PAYMENT"])))) *(np.where(0.036585>0, data["AMT_DOWN_PAYMENT"],(-1.0*(( data["inst_AMT_PAYMENT"])))))))+(( -1.0*(((((0.416667)<(((data["inst_AMT_PAYMENT"])* 2.0)))*1.))))))/2.0))
v["i238"] = np.tanh(((( 0.680000)<(((np.where(data["AMT_GOODS_PRICE_y"]>0, np.where(data["AMT_DOWN_PAYMENT"]>0, data["AMT_DOWN_PAYMENT"], data["CNT_INSTALMENT_FUTURE"]),(( data["AMT_DOWN_PAYMENT"])-(data["NAME_CASH_LOAN_PURPOSE_Business_development"])))) / 2.0)))*1.))
 v["i239"] = np.tanh(((( 0.339286)<(((( 0.339286)+(np.where(data["CNT_INSTALMENT_FUTURE"]>0,(((data["CNT_INSTALMENT"])<(((( data["CNT_INSTALMENT_FUTURE"])>(data["CNT_INSTALMENT"])) *1.))) *1.) , data["CNT_INSTALMENT"])))/2.0)))*1.))
 v["i240"] = np.tanh(( -1.0*(( np.minimum(((((( data["CNT_INSTALMENT"])>(((( data["CNT_INSTALMENT"])>(0.323944)) *1.))) *1.))) ,(( np.maximum(((((((( data["CNT_INSTALMENT"])>(data["CNT_PAYMENT"])) *1.))/ 2.0))),(( data["inst_AMT_PAYMENT"]))))))))))
v["i241"] = np.tanh(( -1.0*(((( np.where(data["AMT_DOWN_PAYMENT"]>0, data["avg_buro_buro_bal_status_2"],(-1.0*(( np.where(data["avg_buro_buro_bal_status_2"]>0,(((( 1.0)) <(((data["avg_buro_buro_bal_status_2"])/ 2.0)))*1.) , data["avg_buro_buro_bal_status_2"])))))) * 2.0)))))
 v["i242"] = np.tanh(((data["avg_buro_buro_bal_status_4"])-(((data["avg_buro_buro_bal_status_2"])-(np.where(-1.0>0,(( data["inst_AMT_PAYMENT"])* 2.0),(((( data["inst_AMT_PAYMENT"])* 2.0)) *(data["NAME_CASH_LOAN_PURPOSE_Business_development"]))))))))
v["i243"] = np.tanh(((data["CNT_INSTALMENT_FUTURE"])*(np.maximum(((((( data["cc_bal_AMT_PAYMENT_CURRENT"])>(((2.235290)-(((( data["AMT_DOWN_PAYMENT"])+(data["NAME_CASH_LOAN_PURPOSE_Business_development"])) /2.0)))))*1.))) ,(((( -3.0)-(0.680000))))))))
v["i244"] = np.tanh(np.tanh(((((((( data["AMT_CREDIT_y"])*(np.maximum(((((-2.0)*(0.036585)))) ,(( np.minimum(((0.036585)) ,(( np.tanh(((( data["te_FLAG_EMAIL"])* 2.0)))))))))))) * 2.0)) * 2.0))))
v["i245"] = np.tanh(((((data["avg_buro_buro_bal_status_4"])*(data["te_FLAG_DOCUMENT_7"])))*(((((((data["avg_buro_buro_bal_status_4"])-(0.576923)))-(((((((0.0)<(11.714300)) *1.))<(data["te_FLAG_DOCUMENT_4"])) *1.))))* 2.0))))
v["i246"] = np.tanh(np.where(((( 1.105260)+(((( 1.105260)+(data["te_FLAG_EMAIL"])) /2.0)))/2.0)>0,(((( data["te_FLAG_EMAIL"])*(data["avg_buro_buro_bal_status_2"])))-(data["avg_buro_buro_bal_status_4"])) , np.tanh(( 0.416667))))
v["i247"] = np.tanh(((np.minimum(((((((np.minimum(((( -1.0*(((((( data["avg_buro_buro_bal_status_4"])*(((( data["avg_buro_buro_bal_status_4"])<(1.857140)) *1.))))* 2.0)))))) ,(( data["cc_bal_cc_bal_status__Refused"])))) * 2.0)) * 2.0))),(( data["cc_bal_cc_bal_status__Refused"])))) * 2.0))
v["i248"] = np.tanh(np.maximum(((-3.0)) ,(((-1.0*(( np.where(data["NAME_YIELD_GROUP_XNA"]>0,(((0.686567)<(((data["avg_buro_buro_bal_status_2"])* 2.0)))*1.) ,(-1.0*(((( data["avg_buro_buro_bal_status_2"])*(((data["avg_buro_buro_bal_status_2"])* 2.0))))))))))))))
v["i249"] = np.tanh(((data["NAME_CONTRACT_TYPE_Revolving_loans"])*(((np.minimum(((((1.0)-(np.where(data["avg_buro_buro_bal_status_4"]>0, 0.494118, data["NAME_CONTRACT_TYPE_Revolving_loans"]))))),(( np.where(data["avg_buro_buro_bal_status_4"]>0, 0.797872, data["NAME_CONTRACT_TYPE_Revolving_loans"])))))/ 2.0))))
v["i250"] = np.tanh(( -1.0*(((((data["NAME_CASH_LOAN_PURPOSE_Business_development"])>(np.where(data["NAME_GOODS_CATEGORY_Insurance"]>0, data["avg_buro_buro_bal_status_2"],(-1.0*(((((data["avg_buro_buro_bal_status_2"])>(((( 11.714300)+(-1.0)) /2.0)))*1.))))))) *1.)))))
v["i251"] = np.tanh(((np.where(((((( -3.0)<(data["DAYS_LAST_DUE_1ST_VERSION"])) *1.))+(( -1.0*(( data["CODE_REJECT_REASON_SCOFR"])))))>0, data["cc_bal_SK_DPD_DEF"], -3.0)) *(np.where(data["avg_buro_buro_bal_status_4"]>0, data["DAYS_LAST_DUE_1ST_VERSION"], 11.714300))))
v["i252"] = np.tanh(((( 1.0)<(((((( np.where(((( data["cc_bal_AMT_PAYMENT_CURRENT"])>(data["NAME_CASH_LOAN_PURPOSE_Business_development"])) *1.) >0, data["CODE_REJECT_REASON_SCOFR"], 0.697674)) *(data["cc_bal_AMT_PAYMENT_CURRENT"])))+(data["cc_bal_AMT_PAYMENT_CURRENT"])) /2.0)))*1.))
 v["i253"] = np.tanh(((((((((data["CODE_REJECT_REASON_SCOFR"])*(((data["avg_buro_buro_bal_status_2"])*(((data["avg_buro_buro_bal_status_2"])*(data["inst_AMT_PAYMENT"])))))))/ 2.0)) *(((data["avg_buro_buro_bal_status_2"])+(data["CODE_REJECT_REASON_SCOFR"])))))* 2.0))
v["i254"] = np.tanh(((11.714300)*(((np.where(((data["avg_buro_buro_bal_status_4"])*(data["avg_buro_buro_bal_status_2"])) >0, data["inst_AMT_PAYMENT"],(( data["inst_AMT_PAYMENT"])+(14.800000)))) *(data["NAME_CASH_LOAN_PURPOSE_Business_development"])))))
 v["i255"] = np.tanh(np.minimum(((np.maximum(((data["AMT_GOODS_PRICE_y"])) ,(((-1.0*(( data["CNT_INSTALMENT_FUTURE"])))))))) ,(((( data["CODE_REJECT_REASON_SCOFR"])*(np.where(0.494118>0,(-1.0*(( data["CNT_INSTALMENT_FUTURE"]))),(( 0.0)-(0.036585)))))))))
 v["i256"] = np.tanh(((((( -1.0*(((( data["YEARS_BEGINEXPLUATATION_MEDI"])*(np.where(data["avg_buro_buro_bal_status_2"]>0,(-1.0*(( data["YEARS_BEGINEXPLUATATION_MEDI"]))), np.tanh(( 14.800000)))))))))-(data["FLAG_DOCUMENT_4"])))* 2.0))
v["i257"] = np.tanh(((((((((data["avg_buro_buro_count"])/ 2.0)) / 2.0)) +(((((np.where(data["PRODUCT_COMBINATION_Cash_X_Sell__low"]>0, data["avg_buro_buro_count"], data["NAME_GOODS_CATEGORY_Weapon"])) / 2.0)) +(data["PRODUCT_COMBINATION_Cash_X_Sell__low"])))))*(data["avg_buro_buro_count"])))
 v["i258"] = np.tanh(((np.maximum(((data["avg_buro_MONTHS_BALANCE"])) ,(((( np.tanh(((((data["SK_ID_BUREAU"])<(((((((data["NAME_CASH_LOAN_PURPOSE_Business_development"])<(data["avg_buro_MONTHS_BALANCE"])) *1.))>(data["avg_buro_MONTHS_BALANCE"])) *1.))) *1.))))*(data["SK_ID_BUREAU"])))))) *(data["SK_ID_BUREAU"])))
 v["i259"] = np.tanh(((((((np.minimum(((data["NAME_CASH_LOAN_PURPOSE_Business_development"])) ,(((((2.235290)+(data["DAYS_CREDIT"])) /2.0)))))+(-3.0)) /2.0)) >(np.minimum(((0.323944)) ,(( data["avg_buro_buro_count"])))))*1.))
 v["i260"] = np.tanh(np.minimum(((((data["TOTALAREA_MODE"])*(np.tanh(((-1.0*(( data["avg_buro_buro_bal_status_2"]))))))))),(( np.maximum(((np.minimum(((data["TOTALAREA_MODE"])) ,(( data["TOTALAREA_MODE"]))))),(( np.minimum(((data["avg_buro_buro_bal_status_2"])) ,(( data["DAYS_CREDIT"]))))))))))
v["i261"] = np.tanh(np.minimum(((((data["CODE_REJECT_REASON_SCOFR"])*(((( -1.0*(((((0.697674)<(np.minimum(((data["CODE_REJECT_REASON_SCOFR"])) ,(( data["DAYS_LAST_DUE_1ST_VERSION"])))))*1.))))) * 2.0))))),(((( data["CODE_REJECT_REASON_SCOFR"])*(data["NAME_PAYMENT_TYPE_Cashless_from_the_account_of_the_employer"]))))))
v["i262"] = np.tanh(((((data["avg_buro_buro_count"])*(np.minimum(((data["avg_buro_buro_bal_status_4"])) ,(((((( 9.0)) >(((data["avg_buro_buro_count"])*(np.minimum(((((data["avg_buro_buro_bal_status_2"])* 2.0))),(( data["avg_buro_buro_bal_status_4"])))))))*1.))))))) *(data["avg_buro_buro_bal_status_2"])))
 v["i263"] = np.tanh(((((( data["avg_buro_buro_count"])>(np.maximum(((data["NAME_CASH_LOAN_PURPOSE_Business_development"])) ,(( np.tanh(( 11.714300)))))))*1.))*(((11.714300)*(((11.714300)*(data["AMT_DOWN_PAYMENT"])))))))
 v["i264"] = np.tanh(np.minimum(((( -1.0*(((( np.maximum(((data["NAME_SELLER_INDUSTRY_Tourism"])) ,(((((14.800000)<(data["avg_buro_buro_bal_status_4"])) *1.))))) * 2.0)))))) ,(((-1.0*(( np.maximum(((data["NAME_GOODS_CATEGORY_Weapon"])) ,(((((data["avg_buro_buro_bal_status_4"])>(0.062500)) *1.)))))))))))
v["i265"] = np.tanh(((data["SK_DPD"])*(np.where(((( data["SK_DPD"])<(data["avg_buro_buro_bal_status_4"])) *1.) >0,(-1.0*(( data["SK_DPD"]))),(( data["avg_buro_buro_bal_status_4"])+(((-1.0)*(((data["NAME_CASH_LOAN_PURPOSE_Business_development"])* 2.0)))))))))
 v["i266"] = np.tanh(( -1.0*(((( data["NAME_CASH_LOAN_PURPOSE_Business_development"])*(np.where(data["avg_buro_buro_bal_status_4"]>0,(( 0.576923)* 2.0), data["WEEKDAY_APPR_PROCESS_START_WEDNESDAY"])))))))
 v["i267"] = np.tanh(np.minimum(((((data["NAME_CASH_LOAN_PURPOSE_Business_development"])*(np.where(data["avg_buro_buro_bal_status_4"]>0, data["CODE_REJECT_REASON_SYSTEM"], data["NAME_GOODS_CATEGORY_Insurance"]))))),(((( data["avg_buro_buro_bal_status_4"])-(np.where(data["avg_buro_buro_bal_status_4"]>0,(( data["NAME_GOODS_CATEGORY_Direct_Sales"])* 2.0), data["NAME_GOODS_CATEGORY_Fitness"])))))))
 v["i268"] = np.tanh(np.where(((data["FLAG_DOCUMENT_4"])+(np.tanh(((( data["cc_bal_SK_DPD_DEF"])* 2.0)))))>0, -3.0,(((data["cc_bal_SK_DPD"])>(np.maximum(((11.714300)) ,(( 11.714300)))))*1.)))
v["i269"] = np.tanh(((np.maximum(((3.0)) ,(((9.0)))))*(((np.maximum(((data["cc_bal_SK_DPD_DEF"])) ,(((((( 4.0)) <(np.maximum(((((data["CHANNEL_TYPE_AP___Cash_loan_"])/ 2.0))),(( data["CHANNEL_TYPE_AP___Cash_loan_"])))))*1.))))) *(data["CHANNEL_TYPE_AP___Cash_loan_"])))))
 v["i270"] = np.tanh(np.tanh(((((((( np.tanh(( data["avg_buro_buro_bal_status_4"])))*(data["CHANNEL_TYPE_AP___Cash_loan_"])))*(11.714300)))*(np.maximum(((data["CHANNEL_TYPE_AP___Cash_loan_"])) ,(((( data["NAME_CASH_LOAN_PURPOSE_Business_development"])* 2.0)))))))))
 v["i271"] = np.tanh(np.minimum(((((( np.where(data["cc_bal_SK_DPD_DEF"]>0, data["avg_buro_buro_bal_status_2"], 0.031746)) <(((( data["cc_bal_cc_bal_status__Refused"])+(((( 0.339286)<(data["avg_buro_buro_bal_status_2"])) *1.))) /2.0)))*1.))) ,(((((data["avg_buro_buro_bal_status_2"])<(3.526320)) *1.)))))
v["i272"] = np.tanh(( -1.0*(( np.maximum(((data["NAME_CASH_LOAN_PURPOSE_Business_development"])) ,(((( -3.0)+(((data["NAME_CLIENT_TYPE_Refreshed"])-(((np.minimum(((data["NAME_GOODS_CATEGORY_Insurance"])) ,(((( 11.714300)-(data["avg_buro_buro_bal_status_4"])))))) * 2.0))))))))))))
v["i273"] = np.tanh(np.minimum(((np.minimum(((((( 12.49790954589843750)) -(data["avg_buro_buro_bal_status_4"])))) ,(((( data["NAME_GOODS_CATEGORY_Direct_Sales"])*(np.where(((data["avg_buro_buro_bal_status_4"])/ 2.0)>0, data["NAME_CASH_LOAN_PURPOSE_Business_development"],(-1.0*(( data["avg_buro_buro_bal_status_4"]))))))))))),(( 11.714300))))
v["i274"] = np.tanh(((( -1.0*(((((((data["avg_buro_buro_bal_status_2"])*(data["avg_buro_buro_bal_status_4"])))<(np.maximum(((data["FLAG_DOCUMENT_17"])) ,(( np.tanh(( data["FLAG_DOCUMENT_17"])))))))*1.))))) * 2.0))
v["i275"] = np.tanh(((data["cc_bal_SK_DPD_DEF"])+(((((np.where(((data["FLAG_DOCUMENT_10"])-(data["cc_bal_SK_DPD_DEF"])) >0, data["cu__currency_3"], data["cc_bal_SK_DPD_DEF"])) -(((( data["cu__currency_3"])>(data["cc_bal_SK_DPD_DEF"])) *1.))))/ 2.0))))
v["i276"] = np.tanh(( -1.0*(((((11.714300)<(((np.maximum(((((14.800000)/ 2.0))),(((((np.maximum(((0.036585)) ,(( 2.0)))) +(data["NAME_CASH_LOAN_PURPOSE_Business_development"])) /2.0)))))+(data["avg_buro_buro_bal_status_4"])))) *1.)))))
v["i277"] = np.tanh(np.maximum(((0.031746)) ,(((((0.031746)<(((data["NAME_YIELD_GROUP_XNA"])*(np.minimum(((data["cc_bal_SK_DPD_DEF"])) ,(( np.where(0.416667>0, 0.031746, data["NAME_PORTFOLIO_XNA"])))))))) *1.)))))
v["i278"] = np.tanh(((( np.maximum(((data["NAME_CASH_LOAN_PURPOSE_Business_development"])) ,(( data["cu__currency_3"])))) +(((data["NAME_CASH_LOAN_PURPOSE_Business_development"])*(((np.where(data["cu__currency_3"]>0, data["cu__currency_3"], data["NAME_GOODS_CATEGORY_Fitness"])) *(((data["cu__currency_3"])+(data["NAME_CASH_LOAN_PURPOSE_Business_development"])))))))) /2.0))
v["i279"] = np.tanh(((np.where(((((((data["NAME_GOODS_CATEGORY_Sport_and_Leisure"])+(data["NAME_PORTFOLIO_XNA"])) /2.0)) +(data["REG_REGION_NOT_WORK_REGION"])) /2.0)>0, 0.680000, data["NAME_GOODS_CATEGORY_Sport_and_Leisure"])) *(((data["NAME_GOODS_CATEGORY_Sport_and_Leisure"])*(((( -1.0*(( 1.857140)))) * 2.0))))))
v["i280"] = np.tanh(((((data["CHANNEL_TYPE_Stone"])* 2.0)) *(((( 0.062500)+(((((np.minimum(((data["CHANNEL_TYPE_Stone"])) ,(( data["NAME_CASH_LOAN_PURPOSE_Business_development"])))) *(np.tanh(( np.tanh(( data["NAME_CONTRACT_STATUS_Approved"])))))))/ 2.0)))/2.0))))
v["i281"] = np.tanh(np.where(data["FLAG_DOCUMENT_4"]>0, -3.0,(((( np.where(data["SK_ID_PREV_x"]>0,(((((( -3.0)+(data["SK_ID_PREV_x"])) /2.0)) >(2.235290)) *1.) , data["NAME_GOODS_CATEGORY_Direct_Sales"])) * 2.0)) * 2.0)))
 v["i282"] = np.tanh(((np.where(((data["NAME_SELLER_INDUSTRY_MLM_partners"])*(data["FLAG_DOCUMENT_15"])) >0,(( data["NAME_GOODS_CATEGORY_Fitness"])*(data["FLAG_DOCUMENT_15"])) ,(((((((data["NAME_SELLER_INDUSTRY_MLM_partners"])*(data["FLAG_DOCUMENT_15"])))* 2.0)) +(data["NAME_CASH_LOAN_PURPOSE_Business_development"])) /2.0)))* 2.0))
v["i283"] = np.tanh(((((( np.where(data["NAME_GOODS_CATEGORY_Insurance"]>0, 11.714300, data["FLAG_DOCUMENT_5"])) *(data["te_FLAG_DOCUMENT_20"])))>(((( 0.416667)+(0.416667)) /2.0)))*1.))
 v["i284"] = np.tanh(((((data["te_FLAG_DOCUMENT_3"])*(((np.maximum(((((( 0.494118)<(data["cc_bal_SK_DPD_DEF"])) *1.))) ,(((((((data["NAME_CASH_LOAN_PURPOSE_Business_development"])*(2.235290)))>(0.0)) *1.))))) * 2.0)))) * 2.0))
v["i285"] = np.tanh(np.where(data["NAME_GOODS_CATEGORY_Insurance"]>0,(-1.0*(((( data["te_FLAG_DOCUMENT_6"])*(2.0))))),(( data["te_FLAG_DOCUMENT_18"])*(((((( data["nans"])>(((( -1.0*(( data["FLAG_DOCUMENT_18"])))) / 2.0)))*1.))/ 2.0)))))
 v["i286"] = np.tanh(((((((( data["te_FLAG_DOCUMENT_8"])<(( -1.0*(((((( data["te_FLAG_DOCUMENT_8"])*(data["FLAG_DOCUMENT_21"])))+(((data["cc_bal_SK_DPD_DEF"])+(((((7.04159450531005859)) +(data["te_FLAG_DOCUMENT_20"])) /2.0)))))))))) *1.))* 2.0)) * 2.0))
v["i287"] = np.tanh(np.minimum(((np.maximum(((data["te_LIVE_REGION_NOT_WORK_REGION"])) ,(( data["NAME_GOODS_CATEGORY_Weapon"]))))),(((((((((((data["FLAG_DOCUMENT_10"])*(data["FLAG_DOCUMENT_10"])))>(data["cc_bal_SK_DPD_DEF"])) *1.))* 2.0)) -(((( data["NAME_GOODS_CATEGORY_Weapon"])+(data["te_LIVE_REGION_NOT_WORK_REGION"])) /2.0)))))))
 v["i288"] = np.tanh(((((np.maximum(((np.maximum(((0.031746)) ,(((-1.0*(( data["NAME_GOODS_CATEGORY_Insurance"])))))))) ,(( data["REG_REGION_NOT_WORK_REGION"])))) -(data["te_FLAG_DOCUMENT_20"])))*(np.maximum(((0.062500)) ,(( data["NAME_CASH_LOAN_PURPOSE_Business_development"]))))))
v["i289"] = np.tanh(((np.where(data["NAME_GOODS_CATEGORY_Education"]>0, data["te_REG_REGION_NOT_LIVE_REGION"],(( data["te_REG_REGION_NOT_LIVE_REGION"])*(data["te_CODE_GENDER"])))) *(((data["te_REG_REGION_NOT_LIVE_REGION"])+(data["te_OCCUPATION_TYPE"])))))
 v["i290"] = np.tanh(((((( data["FLAG_DOCUMENT_3"])*(((( 0.323944)<(data["RATE_DOWN_PAYMENT"])) *1.))))+(np.where(data["NAME_CASH_LOAN_PURPOSE_Business_development"]>0, data["FLAG_DOCUMENT_3"],(( data["te_REG_REGION_NOT_LIVE_REGION"])*(data["RATE_DOWN_PAYMENT"])))))/2.0))
v["i291"] = np.tanh(((np.where(((data["AMT_DOWN_PAYMENT"])+(((0.697674)/ 2.0)))>0,(((data["AMT_DOWN_PAYMENT"])>(0.062500)) *1.) , data["AMT_DOWN_PAYMENT"])) *(data["FLAG_DOCUMENT_6"])))
 v["i292"] = np.tanh(np.where(((( -1.0*(( data["te_NAME_CONTRACT_TYPE"])))) -(np.maximum(((0.797872)) ,(( data["NAME_CONTRACT_TYPE"])))))>0, data["NAME_CONTRACT_TYPE"],(((( np.tanh(( data["FLAG_DOCUMENT_6"])))/ 2.0)) / 2.0)))
 v["i293"] = np.tanh(((((((((((((((data["FLAG_DOCUMENT_2"])+(data["AMT_DOWN_PAYMENT"])) /2.0)) >(2.0)) *1.))-(data["NAME_GOODS_CATEGORY_Education"])))-(data["NAME_GOODS_CATEGORY_Education"])))-(data["FLAG_DOCUMENT_17"])))-(data["NAME_GOODS_CATEGORY_Education"])))
 v["i294"] = np.tanh(np.maximum(((np.minimum(((( -1.0*(( data["AMT_INCOME_TOTAL"]))))),(( np.tanh(((((data["AMT_INCOME_TOTAL"])<(((((1.220590)-(2.0)))/ 2.0)))*1.)))))))),(((( data["AMT_INCOME_TOTAL"])-(2.0))))))
v["i295"] = np.tanh(((((( data["AMT_INCOME_TOTAL"])>(((( 1.220590)>(-3.0)) *1.))) *1.))+(((3.0)*(((( data["AMT_ANNUITY_x"])>(np.where(data["AMT_CREDIT_x"]>0, 1.220590, 2.235290)))*1.))))))
 v["i296"] = np.tanh(((((( data["NAME_GOODS_CATEGORY_Insurance"])>(((data["AMT_ANNUITY_x"])+(np.where(0.031746>0, 1.220590, data["NAME_GOODS_CATEGORY_Insurance"])))))*1.))-(0.062500)))
 v["i297"] = np.tanh(( -1.0*(( np.where(data["te_HOUSETYPE_MODE"]>0, 0.062500,(( data["te_NAME_CONTRACT_TYPE"])*(np.tanh(( np.minimum(((np.maximum(((data["te_NAME_CONTRACT_TYPE"])) ,(( data["FLAG_DOCUMENT_3"]))))),(((-1.0*(( data["te_NAME_CONTRACT_TYPE"])))))))))))))))
 v["i298"] = np.tanh(np.minimum(((((np.where(data["te_ORGANIZATION_TYPE"]>0,(-1.0*(((((0.697674)<(((data["HOUR_APPR_PROCESS_START_x"])/ 2.0)))*1.)))), data["AMT_INCOME_TOTAL"])) *(data["te_ORGANIZATION_TYPE"])))) ,(( np.maximum(((data["te_ORGANIZATION_TYPE"])) ,(( data["HOUR_APPR_PROCESS_START_x"])))))))
 v["i299"] = np.tanh(np.maximum(((np.maximum(((((( data["AMT_INCOME_TOTAL"])>(1.105260)) *1.))) ,(((((data["PRODUCT_COMBINATION_Cash_X_Sell__high"])>(2.235290)) *1.)))))),(( 0.062500))))
v["i300"] = np.tanh(((np.minimum(((0.697674)) ,(( np.where(data["AMT_DOWN_PAYMENT"]>0, data["DAYS_ID_PUBLISH"], 0.062500)))))*(data["NAME_YIELD_GROUP_XNA"])))
 v["i301"] = np.tanh(((((((((1.0)<(data["te_NAME_EDUCATION_TYPE"])) *1.))>(((np.maximum(((data["FLAG_EMP_PHONE"])) ,(( data["te_NAME_EDUCATION_TYPE"])))) / 2.0)))*1.))* 2.0))
v["i302"] = np.tanh(((((( data["ORGANIZATION_TYPE"])>(3.526320)) *1.))-(((3.526320)*(((((( data["NAME_CONTRACT_STATUS_Canceled"])*(((data["ORGANIZATION_TYPE"])*(data["ORGANIZATION_TYPE"])))))>(3.526320)) *1.))))))
 v["i303"] = np.tanh(((data["ORGANIZATION_TYPE"])*(((np.maximum(((((( 3.526320)<(data["ORGANIZATION_TYPE"])) *1.))) ,(((((data["PRODUCT_COMBINATION_Cash_X_Sell__high"])>(((( data["AMT_DOWN_PAYMENT"])<(0.036585)) *1.))) *1.))))) / 2.0))))
v["i304"] = np.tanh(np.minimum(((((data["AMT_INCOME_TOTAL"])*(((data["NAME_YIELD_GROUP_high"])*(data["PRODUCT_COMBINATION_Cash_X_Sell__high"])))))) ,(((( data["NAME_YIELD_GROUP_high"])*(( -1.0*(( data["AMT_INCOME_TOTAL"])))))))))
 v["i305"] = np.tanh(np.where(data["NAME_TYPE_SUITE_Other_A"]>0, data["AMT_DOWN_PAYMENT"], np.tanh(((((( -1.0*(( data["avg_buro_buro_count"])))) >(((((-1.0*(( data["avg_buro_buro_count"])))) >(data["AMT_DOWN_PAYMENT"])) *1.))) *1.)))))
v["i306"] = np.tanh(((((( data["avg_buro_buro_bal_status_C"])>(((( 0.416667)+(( -1.0*(( np.minimum(((0.043478)) ,(( -2.0)))))))) /2.0)))*1.))*(((1.0)-(data["NAME_TYPE_SUITE_Other_A"])))))
 v["i307"] = np.tanh(( -1.0*(((((((( 0.323944)+(data["AMT_GOODS_PRICE_x"])) /2.0)) <(np.where(data["AMT_ANNUITY_x"]>0,(((((data["AMT_ANNUITY_x"])<(data["AMT_GOODS_PRICE_x"])) *1.))*(data["AMT_ANNUITY_x"])) , data["AMT_ANNUITY_x"])))*1.)))))
v["i308"] = np.tanh(np.where(data["ty__Credit_card"]>0,(( np.tanh(((((np.tanh(( data["ty__Credit_card"])))>(((((( 0.339286)*(0.339286)))+(0.339286)) /2.0)))*1.))))/ 2.0), data["NAME_CASH_LOAN_PURPOSE_Furniture"]))
v["i309"] = np.tanh(((((data["AMT_DOWN_PAYMENT"])/ 2.0)) +(((14.800000)*(( -1.0*(((((((data["AMT_DOWN_PAYMENT"])-(1.857140)))>(0.036585)) *1.)))))))))
v["i310"] = np.tanh(np.where(data["NAME_CASH_LOAN_PURPOSE_Business_development"]>0,(( -1.0)-(data["EXT_SOURCE_3"])) ,(((data["EXT_SOURCE_3"])>(0.680000)) *1.)))
v["i311"] = np.tanh(((((((( data["EXT_SOURCE_3"])*(data["NAME_TYPE_SUITE_Other_A"])))*(data["NAME_TYPE_SUITE_Other_A"])))+(((( np.minimum(((data["nans"])) ,(((-1.0*(( data["EXT_SOURCE_3"])))))))+(data["NAME_TYPE_SUITE_Other_A"])) /2.0)))/2.0))
v["i312"] = np.tanh(((((np.tanh(((((3.0)<(((((data["FONDKAPREMONT_MODE"])-(0.323944)))-(np.maximum(((data["te_WALLSMATERIAL_MODE"])) ,(( data["NAME_CASH_LOAN_PURPOSE_Business_development"])))))))*1.))))* 2.0)) * 2.0))
v["i313"] = np.tanh(((((((( data["SK_ID_PREV_x"])*(data["SK_ID_BUREAU"])))<(((((data["SK_ID_BUREAU"])+(-3.0)))/ 2.0)))*1.))*(np.minimum(((1.0)) ,(((( data["SK_ID_BUREAU"])+(-3.0))))))))
v["i314"] = np.tanh(((3.526320)*(np.where(((data["PRODUCT_COMBINATION_POS_mobile_with_interest"])+(0.062500)) >0,(((((data["SK_ID_BUREAU"])>(2.235290)) *1.))*(data["SK_ID_PREV_x"])) , 0.043478))))
v["i315"] = np.tanh(np.minimum(((((((data["SK_ID_PREV_x"])*(( -1.0*(( data["avg_buro_buro_bal_status_3"])))))) * 2.0))),(((( np.maximum(((data["te_HOUSETYPE_MODE"])) ,(((((( data["avg_buro_buro_bal_status_3"])* 2.0)) * 2.0)))))* 2.0)))))
 v["i316"] = np.tanh(np.tanh(((( 0.043478)+(((0.043478)+(((((( 0.494118)+(data["WALLSMATERIAL_MODE"])) /2.0)) -(((( data["WALLSMATERIAL_MODE"])>(((( data["te_FONDKAPREMONT_MODE"])>(0.043478)) *1.))) *1.))))))))))
 v["i317"] = np.tanh(np.where(np.tanh(( np.minimum(((((( np.minimum(((0.036585)) ,(( 0.036585)))) >(((((( -1.0*(( data["AMT_CREDIT_SUM_DEBT"])))) / 2.0)) / 2.0)))*1.))) ,(( data["SK_ID_BUREAU"])))))>0, data["SK_ID_BUREAU"], data["AMT_CREDIT_SUM_DEBT"]))
v["i318"] = np.tanh(((data["ty__Consumer_credit"])*(np.where(((data["AMT_CREDIT_SUM"])*(0.686567)) >0,(((( data["AMT_CREDIT_SUM"])*(0.686567)))+(data["ca__Closed"])) , data["NAME_GOODS_CATEGORY_Insurance"]))))
v["i319"] = np.tanh(((((((( 2.235290)<(data["AMT_GOODS_PRICE_x"])) *1.))*(np.where(np.minimum(((2.235290)) ,(( data["NAME_CASH_LOAN_PURPOSE_Business_development"])))>0, data["AMT_CREDIT_SUM_DEBT"], data["DAYS_LAST_DUE"])))) *(2.235290)))
 v["i320"] = np.tanh(((((((( 1.220590)<(((data["ty__Consumer_credit"])-(((((( data["inst_AMT_PAYMENT"])>(data["AMT_CREDIT_SUM"])) *1.))+(data["AMT_CREDIT_SUM"])))))) *1.))/ 2.0)) *(data["ca__Closed"])))
 v["i321"] = np.tanh(((np.where(((data["NAME_CASH_LOAN_PURPOSE_Business_development"])* 2.0)>0,(((( data["ty__Consumer_credit"])-(-2.0)))* 2.0), np.minimum(((( -1.0*(((((data["AMT_CREDIT_SUM"])>(0.323944)) *1.)))))),(( 0.686567)))))/ 2.0))
v["i322"] = np.tanh(((((( 2.235290)<(((( data["nans"])+(((( 0.680000)<(((data["te_WALLSMATERIAL_MODE"])/ 2.0)))*1.))) /2.0)))*1.))*(data["nans"])))
 v["i323"] = np.tanh(np.minimum(((( -1.0*(((((data["NAME_CASH_LOAN_PURPOSE_Business_development"])>(((( data["te_WALLSMATERIAL_MODE"])>(data["NAME_GOODS_CATEGORY_Insurance"])) *1.))) *1.)))))),(( np.tanh(((-1.0*(((((data["te_WALLSMATERIAL_MODE"])>(((( 0.697674)>(data["NAME_CASH_LOAN_PURPOSE_Business_development"])) *1.))) *1.))))))))))
 v["i324"] = np.tanh(((((( data["NAME_CASH_LOAN_PURPOSE_Furniture"])+(0.339286)) /2.0)) *(( -1.0*(((((np.minimum(((((((( 0.062500)<(data["te_WALLSMATERIAL_MODE"])) *1.))+(data["NAME_CASH_LOAN_PURPOSE_Furniture"])))) ,(( data["WALLSMATERIAL_MODE"])))) +(data["te_HOUSETYPE_MODE"])) /2.0)))))))
 v["i325"] = np.tanh(((((data["CNT_INSTALMENT"])*(((np.where(np.tanh(( data["ty__Credit_card"])) >0, data["NAME_GOODS_CATEGORY_Direct_Sales"],(-1.0*(( data["inst_AMT_PAYMENT"])))))+(((data["inst_AMT_PAYMENT"])*(data["ty__Credit_card"])))))))/ 2.0))
v["i326"] = np.tanh(((((((((-1.0*(( np.minimum(((data["CNT_INSTALMENT"])) ,(( data["CNT_INSTALMENT"])))))))*(((data["CNT_INSTALMENT"])/ 2.0)))) +(data["inst_AMT_PAYMENT"])) /2.0)) *(((data["ty__Consumer_credit"])+(data["ty__Credit_card"])))))
 v["i327"] = np.tanh(np.where(data["CODE_REJECT_REASON_CLIENT"]>0, data["PRODUCT_COMBINATION_POS_industry_without_interest"], np.where(((( data["inst_AMT_PAYMENT"])<(((((( data["PRODUCT_COMBINATION_POS_industry_without_interest"])>(1.0)) *1.))/ 2.0)))*1.) >0,(-1.0*(( data["PRODUCT_COMBINATION_POS_industry_without_interest"]))), data["PRODUCT_COMBINATION_POS_industry_without_interest"])))
 v["i328"] = np.tanh(((np.where(data["inst_AMT_INSTALMENT"]>0,(( np.maximum(((data["NAME_CONTRACT_STATUS_Unused_offer"])) ,(( data["ty__Consumer_credit"])))) / 2.0), np.minimum(((0.036585)) ,(((((data["NAME_CONTRACT_STATUS_Unused_offer"])+(data["ty__Consumer_credit"])) /2.0)))))) / 2.0))
v["i329"] = np.tanh(( -1.0*(((((((((((( 0.686567)<(data["te_NAME_HOUSING_TYPE"])) *1.))>(((((data["te_NAME_HOUSING_TYPE"])/ 2.0)) +(((((( 3.0)>(data["FLAG_DOCUMENT_10"])) *1.))/ 2.0)))))*1.))* 2.0)) * 2.0)))))
 v["i330"] = np.tanh(np.where(data["te_DAYS_ID_PUBLISH"]>0,(((((((1.220590)-(2.0)))/ 2.0)) >(data["AMT_CREDIT_SUM"])) *1.) ,(((data["AMT_CREDIT_SUM"])>(np.maximum(((0.323944)) ,(( data["NAME_TYPE_SUITE_Other_B"])))))*1.)))
v["i331"] = np.tanh(np.where(((((np.where(((data["te_DAYS_ID_PUBLISH"])/ 2.0)>0, 1.0,(( data["AMT_CREDIT_x"])/ 2.0)))/ 2.0)) / 2.0)>0,(((( data["te_DAYS_ID_PUBLISH"])/ 2.0)) *(data["te_DAYS_ID_PUBLISH"])) , data["te_DAYS_ID_PUBLISH"]))
v["i332"] = np.tanh(((( data["inst_AMT_PAYMENT"])<(( -1.0*(((((((0.043478)/ 2.0)) +(((np.maximum(((3.0)) ,(((( data["NAME_TYPE_SUITE_Other_B"])-(1.857140)))))) -(data["NAME_TYPE_SUITE_Other_B"])))) /2.0)))))) *1.))
 v["i333"] = np.tanh(((np.minimum(((((((((( 8.0)) -(data["inst_AMT_PAYMENT"])))-(((data["NAME_CASH_LOAN_PURPOSE_Furniture"])/ 2.0)))) * 2.0))),(((((((( data["NAME_CASH_LOAN_PURPOSE_Furniture"])/ 2.0)) * 2.0)) / 2.0)))))*(3.0)))
 v["i334"] = np.tanh(((((np.where(data["NAME_GOODS_CATEGORY_Insurance"]>0, 3.526320,(( np.maximum(((np.maximum(((data["cu__currency_3"])) ,(( data["FLAG_DOCUMENT_13"]))))),(( -2.0)))) * 2.0)))*(((data["cu__currency_3"])* 2.0)))) * 2.0))
v["i335"] = np.tanh(((np.where(data["cc_bal_cc_bal_status__Sent_proposal"]>0, np.minimum(((np.maximum(((data["NAME_CASH_LOAN_PURPOSE_Refusal_to_name_the_goal"])) ,(( data["Amortized_debt"]))))),(((-1.0*(( data["NAME_CASH_LOAN_PURPOSE_Buying_a_used_car"])))))) ,(((((( -1.0*(( data["cc_bal_cc_bal_status__Sent_proposal"])))) / 2.0)) <(data["NAME_CASH_LOAN_PURPOSE_Buying_a_used_car"])) *1.))) * 2.0))
v["i336"] = np.tanh(((((((((data["Amortized_debt"])+(0.031746)) /2.0)) +(((( 0.416667)+(data["Amortized_debt"])) /2.0)))/2.0)) -(((( data["Amortized_debt"])>(data["NAME_CASH_LOAN_PURPOSE_Refusal_to_name_the_goal"])) *1.))))
 v["i337"] = np.tanh(np.minimum(((data["NAME_CASH_LOAN_PURPOSE_Refusal_to_name_the_goal"])) ,(((((7.0)) +(((14.800000)-(np.maximum(((np.maximum(((np.maximum(((data["NAME_CASH_LOAN_PURPOSE_Business_development"])) ,(( data["NAME_GOODS_CATEGORY_Education"]))))),(( 14.800000))))),(( data["NAME_CASH_LOAN_PURPOSE_Furniture"])))))))))))
 v["i338"] = np.tanh(np.where(data["cc_bal_cc_bal_status__Sent_proposal"]>0,(( data["NAME_CONTRACT_STATUS_Unused_offer"])*(data["cc_bal_cc_bal_status__Sent_proposal"])) ,(-1.0*(((((((data["CODE_REJECT_REASON_CLIENT"])*(data["CODE_REJECT_REASON_CLIENT"])))<(((data["NAME_CONTRACT_STATUS_Unused_offer"])* 2.0)))*1.))))))
 v["i339"] = np.tanh(((data["NAME_PORTFOLIO_XNA"])*(((np.where(((data["WALLSMATERIAL_MODE"])/ 2.0)>0,(((data["WALLSMATERIAL_MODE"])>(3.526320)) *1.) ,(((data["NAME_CONTRACT_STATUS_Unused_offer"])>(( 6.0)))*1.))) *(3.526320)))))
 v["i340"] = np.tanh(((((((data["SK_ID_PREV_x"])*(((np.maximum(((((-3.0)+(np.where(data["SK_ID_PREV_x"]>0, data["NAME_CASH_LOAN_PURPOSE_Business_development"], data["SK_ID_PREV_x"]))))),(( data["CNT_INSTALMENT_FUTURE"])))) / 2.0)))) / 2.0)) -(0.036585)))
 v["i341"] = np.tanh(( -1.0*(((((( np.where(data["AMT_DOWN_PAYMENT"]>0, 0.043478,(-1.0*(((( np.where(data["NAME_GOODS_CATEGORY_Direct_Sales"]>0, 0.043478, 0.0)) * 2.0)))))) * 2.0)) * 2.0)))))
 v["i342"] = np.tanh(np.where(((((data["NAME_YIELD_GROUP_high"])*(((( data["te_FLAG_DOCUMENT_8"])<(0.0)) *1.))))-(((1.220590)/ 2.0)))>0, data["DAYS_TERMINATION"],(((data["te_FLAG_DOCUMENT_8"])<(0.0)) *1.)))
v["i343"] = np.tanh(((0.062500)*(np.minimum(((data["DAYS_LAST_DUE"])) ,(((( data["te_FLAG_DOCUMENT_3"])-(data["te_CODE_GENDER"]))))))))
v["i344"] = np.tanh(((((data["FLAG_DOCUMENT_5"])*(np.minimum(((np.where(((( data["NAME_CONTRACT_STATUS_Canceled"])>(0.062500)) *1.) >0, data["WALLSMATERIAL_MODE"],(-1.0*(( data["WALLSMATERIAL_MODE"])))))) ,(((-1.0*(( data["WALLSMATERIAL_MODE"])))))))))/ 2.0))
v["i345"] = np.tanh(((( -1.0*(((((data["FLAG_DOCUMENT_8"])>(((( 9.55103015899658203)) *(((( 0.797872)>(((((data["AMT_DOWN_PAYMENT"])* 2.0)) -(( 0.0)))))*1.))))) *1.))))) * 2.0))
v["i346"] = np.tanh(((((((3.0)+(((np.where(data["cc_bal_cc_bal_status__Refused"]>0,(-1.0*(( data["FLAG_DOCUMENT_6"]))), data["DAYS_CREDIT"])) +(((( 0.0)<(data["FLAG_DOCUMENT_6"])) *1.))))) /2.0)) <(data["DAYS_CREDIT"])) *1.))
 v["i347"] = np.tanh(np.where(((( -1.0*(( data["AMT_DOWN_PAYMENT"])))) / 2.0)>0,(((((data["OCCUPATION_TYPE"])>(2.0)) *1.))*(data["NAME_CONTRACT_STATUS_Canceled"])) , np.minimum(((data["AMT_DOWN_PAYMENT"])) ,(( data["FLAG_DOCUMENT_5"])))))
 v["i348"] = np.tanh(((np.where(((data["NAME_CONTRACT_STATUS_Approved"])-(((( data["AMT_DOWN_PAYMENT"])<(((((data["AMT_DOWN_PAYMENT"])/ 2.0)) *(data["OCCUPATION_TYPE"])))) *1.))) >0, 0.031746,(-1.0*(( data["OCCUPATION_TYPE"])))))*(data["AMT_DOWN_PAYMENT"])))
 v["i349"] = np.tanh(((( -1.0*(( data["ty__Microloan"])))) *(((((( np.where(( -1.0*(( data["AMT_DOWN_PAYMENT"])))>0,(-1.0*(( data["ty__Microloan"]))),(-1.0*(( data["AMT_DOWN_PAYMENT"])))))* 2.0)) <(data["NAME_CONTRACT_STATUS_Approved"])) *1.))))
 v["i350"] = np.tanh(((( -1.0)+(((( data["WEEKDAY_APPR_PROCESS_START_WEDNESDAY"])>(((((data["NAME_CONTRACT_STATUS_Canceled"])-(np.maximum(((( -1.0*(( 0.062500))))),(( 1.857140)))))) * 2.0)))*1.))) /2.0))
v["i351"] = np.tanh(((( np.where(( -1.0*(( data["AMT_DOWN_PAYMENT"])))>0, 2.0,(( data["cc_bal_cc_bal_status__Refused"])/ 2.0)))<(((( -1.0*(( 1.105260)))) +(np.where(data["cc_bal_cc_bal_status__Refused"]>0, data["NAME_CASH_LOAN_PURPOSE_Repairs"], data["AMT_DOWN_PAYMENT"])))))*1.))
 v["i352"] = np.tanh(((np.maximum(((((data["AMT_DOWN_PAYMENT"])-(14.800000)))) ,(( data["CODE_REJECT_REASON_VERIF"])))) *(((((data["AMT_DOWN_PAYMENT"])-(np.where(data["CODE_REJECT_REASON_VERIF"]>0, 0.062500, 1.220590)))) / 2.0))))
v["i353"] = np.tanh(np.where(data["te_OCCUPATION_TYPE"]>0,(((data["te_OCCUPATION_TYPE"])<(0.680000)) *1.) ,(((data["te_OCCUPATION_TYPE"])<(((( -3.0)+(((( np.minimum(((0.494118)) ,(( 3.0)))) <(data["AMT_DOWN_PAYMENT"])) *1.))) /2.0)))*1.)))
v["i354"] = np.tanh(np.where(np.maximum(((data["NAME_GOODS_CATEGORY_Direct_Sales"])) ,(( -3.0)))>0,(-1.0*(( np.minimum(((data["OBS_60_CNT_SOCIAL_CIRCLE"])) ,(( data["OBS_60_CNT_SOCIAL_CIRCLE"])))))) ,(((0.0)>(( 7.0)))*1.)))
v["i355"] = np.tanh(np.where(((np.where(data["NAME_CASH_LOAN_PURPOSE_Business_development"]>0, data["cc_bal_cc_bal_status__Refused"],(((data["DEF_60_CNT_SOCIAL_CIRCLE"])>(((2.235290)+(((( 6.48228216171264648)) / 2.0)))))*1.))) * 2.0)>0, data["DEF_60_CNT_SOCIAL_CIRCLE"],(( data["NAME_CASH_LOAN_PURPOSE_Business_development"])* 2.0)))
 v["i356"] = np.tanh(((((((11.51243686676025391)) +(14.800000)) /2.0)) *(((data["cc_bal_SK_DPD_DEF"])*(np.maximum(((data["ty__Microloan"])) ,(( data["inst_AMT_PAYMENT"]))))))))
v["i357"] = np.tanh(((( -2.0)>(( -1.0*(((((((((((((data["AMT_DOWN_PAYMENT"])<(data["cc_bal_SK_DPD_DEF"])) *1.))*(data["inst_AMT_INSTALMENT"])))/ 2.0)) *(data["inst_AMT_INSTALMENT"])))/ 2.0)))))) *1.))
 v["i358"] = np.tanh(((((( data["ty__Microloan"])>(((( 11.714300)+(((( 11.714300)+(( -1.0*(((( data["AMT_DOWN_PAYMENT"])-(((((-1.0*(( data["ty__Microloan"])))) <(data["AMT_DOWN_PAYMENT"])) *1.))))))))/2.0)))/2.0)))*1.))* 2.0))
v["i359"] = np.tanh(((((data["inst_AMT_PAYMENT"])*(((((data["AMT_DOWN_PAYMENT"])*(data["inst_AMT_PAYMENT"])))*(data["CHANNEL_TYPE_Car_dealer"])))))-(((data["CHANNEL_TYPE_Car_dealer"])* 2.0))))
v["i360"] = np.tanh(((((( 0.576923)<(data["cc_bal_cc_bal_status__Refused"])) *1.))-(((((np.maximum(((data["NAME_GOODS_CATEGORY_Insurance"])) ,(((( data["te_FLAG_WORK_PHONE"])*(0.036585)))))) +(0.043478)))+(data["CHANNEL_TYPE_Car_dealer"])))))
 v["i361"] = np.tanh(np.where(((data["cc_bal_SK_DPD_DEF"])-(0.036585)) >0, data["WEEKDAY_APPR_PROCESS_START"],(( 11.714300)*(((data["cc_bal_SK_DPD_DEF"])*(((((data["cc_bal_SK_DPD_DEF"])*(11.714300)))+(data["inst_AMT_PAYMENT"]))))))))
v["i362"] = np.tanh(np.where(0.031746>0,(((( np.maximum(((((((4.0)) <(data["inst_AMT_INSTALMENT"])) *1.))) ,(((((( 4.0)) <(data["AMT_DOWN_PAYMENT"])) *1.))))) *(3.526320)))*(data["inst_AMT_INSTALMENT"])) , 3.526320))
v["i363"] = np.tanh(((data["WEEKDAY_APPR_PROCESS_START"])*(( -1.0*(( np.tanh(( np.maximum(((data["cc_bal_AMT_PAYMENT_CURRENT"])) ,(((((( -1.0*(( data["NAME_CASH_LOAN_PURPOSE_Business_development"])))) +(( -1.0*(( np.tanh(( data["cc_bal_AMT_PAYMENT_CURRENT"])))))))/2.0))))))))))))
v["i364"] = np.tanh(( -1.0*(((((((((6.0)) +(data["te_FLAG_DOCUMENT_5"])))* 2.0)) *(((( data["inst_AMT_PAYMENT"])>(((( data["te_FLAG_DOCUMENT_5"])+(( 4.66436243057250977)))/2.0)))*1.)))))))
v["i365"] = np.tanh(((( data["inst_AMT_INSTALMENT"])+(((((( data["CNT_INSTALMENT_FUTURE"])>(3.0)) *1.))-(np.tanh(( np.maximum(((np.tanh(( data["inst_AMT_INSTALMENT"])))) ,(((-1.0*(( 0.036585)))))))))))) /2.0))
v["i366"] = np.tanh(((data["inst_AMT_PAYMENT"])*(( -1.0*(((((((data["AMT_ANNUITY"])>(2.235290)) *1.))*(np.maximum(((14.800000)) ,(( data["AMT_ANNUITY"])))))))))))
 v["i367"] = np.tanh(np.where(data["NAME_CASH_LOAN_PURPOSE_Building_a_house_or_an_annex"]>0,(((((( 8.0)) +(((np.minimum(((data["NFLAG_INSURED_ON_APPROVAL"])) ,(( 0.797872)))) * 2.0)))) <(data["NAME_CASH_LOAN_PURPOSE_Building_a_house_or_an_annex"])) *1.) ,(( -2.0)*(data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"]))))
v["i368"] = np.tanh(((((np.minimum(((((data["NAME_CASH_LOAN_PURPOSE_Business_development"])*(-1.0)))) ,(((((( data["NAME_CASH_LOAN_PURPOSE_Business_development"])*(data["AMT_DOWN_PAYMENT"])))* 2.0)))))-(data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"])))*(data["AMT_DOWN_PAYMENT"])))
 v["i369"] = np.tanh(np.where(data["CNT_INSTALMENT"]>0, np.minimum(((((0.680000)-(data["NAME_GOODS_CATEGORY_Photo___Cinema_Equipment"])))) ,(( np.maximum(((((data["CNT_INSTALMENT"])-(data["CNT_INSTALMENT"])))) ,(( data["NAME_GOODS_CATEGORY_Photo___Cinema_Equipment"])))))) ,(( 0.036585)*(data["NAME_GOODS_CATEGORY_Photo___Cinema_Equipment"]))))
v["i370"] = np.tanh(((((-1.0*(( np.where(((((1.857140)*(((data["NAME_GOODS_CATEGORY_Insurance"])/ 2.0)))) -(data["cc_bal_SK_DPD_DEF"])) >0, data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"], 0.043478)))))+(((data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"])*(0.062500)))) /2.0))
v["i371"] = np.tanh(np.minimum(((0.416667)) ,(((((((((( 1.105260)-(((((( 0.30295735597610474)) / 2.0)) *(data["AMT_DOWN_PAYMENT"])))))+(data["NFLAG_INSURED_ON_APPROVAL"])) /2.0)) <(((data["AMT_DOWN_PAYMENT"])/ 2.0)))*1.)))))
v["i372"] = np.tanh(((((( data["cc_bal_SK_DPD_DEF"])<(np.minimum(((data["NAME_YIELD_GROUP_middle"])) ,(((((( data["NAME_GOODS_CATEGORY_Insurance"])*(data["NAME_YIELD_GROUP_middle"])))* 2.0)))))) *1.))*(data["NAME_YIELD_GROUP_middle"])))
 v["i373"] = np.tanh(( -1.0*(((( np.minimum(((np.tanh(((((((np.tanh(( 0.339286)))>(data["NAME_YIELD_GROUP_middle"])) *1.))/ 2.0))))),(((((data["NAME_YIELD_GROUP_middle"])>(data["NAME_GOODS_CATEGORY_Photo___Cinema_Equipment"])) *1.))))) / 2.0)))))
 v["i374"] = np.tanh(np.where(((( 0.680000)<(np.minimum(((((( data["NAME_YIELD_GROUP_middle"])+(0.0)) /2.0))),(( data["AMT_CREDIT_SUM"])))))*1.) >0, data["AMT_CREDIT_SUM"],(( np.minimum(((data["AMT_CREDIT_SUM"])) ,(( data["AMT_CREDIT_SUM"])))) *(data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"]))))
v["i375"] = np.tanh(((np.minimum(((((np.where(data["NAME_CASH_LOAN_PURPOSE_Gasification___water_supply"]>0,(( data["NAME_CASH_LOAN_PURPOSE_Gasification___water_supply"])-(14.800000)) ,(( data["NAME_CASH_LOAN_PURPOSE_Gasification___water_supply"])*(data["NAME_GOODS_CATEGORY_Computers"])))) * 2.0))),(((( data["NAME_CASH_LOAN_PURPOSE_Gasification___water_supply"])/ 2.0)))))* 2.0))
v["i376"] = np.tanh(np.where(data["te_NAME_HOUSING_TYPE"]>0, 0.323944,(((((-1.0*(((((data["te_NAME_HOUSING_TYPE"])<(np.tanh(( -2.0)))) *1.))))) * 2.0)) * 2.0)))
 v["i377"] = np.tanh(((( -1.0*(( data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"])))) *(np.where(data["NAME_YIELD_GROUP_middle"]>0, np.minimum(((-2.0)) ,(((( np.tanh(( 2.235290)))/ 2.0)))) ,(((0.686567)+(data["NAME_YIELD_GROUP_middle"])) /2.0)))))
 v["i378"] = np.tanh(( -1.0*(((((( data["inst_AMT_PAYMENT"])*(0.031746)))-(((( 0.416667)<(np.where(data["te_NAME_HOUSING_TYPE"]>0, data["inst_AMT_PAYMENT"],(( data["AMT_CREDIT_SUM"])*(data["inst_AMT_PAYMENT"])))))*1.)))))))
v["i379"] = np.tanh(((((( data["AMT_CREDIT_SUM"])+(((0.043478)* 2.0)))) <(np.tanh(( data["AMT_CREDIT_SUM_DEBT"])))) *1.))
 v["i380"] = np.tanh(((( -1.0*(((((0.416667)+(np.where(np.minimum(((data["EXT_SOURCE_3"])) ,(( 0.494118)))>0, data["ca__Active"], np.tanh(( data["te_NAME_HOUSING_TYPE"])))))/2.0)))))-(((0.323944)/ 2.0))))
v["i381"] = np.tanh(np.where(data["SK_ID_BUREAU"]>0,(( data["ty__Credit_card"])*(data["SK_ID_BUREAU"])) ,(((((data["inst_AMT_PAYMENT"])<(np.tanh(( data["ty__Credit_card"])))) *1.))*(((data["SK_ID_BUREAU"])+(0.494118))))))
v["i382"] = np.tanh(((((((((((data["NAME_CASH_LOAN_PURPOSE_Business_development"])*(((( data["DAYS_ID_PUBLISH"])<(1.105260)) *1.))))* 2.0)) * 2.0)) * 2.0)) *(np.tanh(( np.tanh(((( data["DAYS_ID_PUBLISH"])+(data["DAYS_ID_PUBLISH"])))))))))
 v["i383"] = np.tanh(( -1.0*(( np.maximum(((((( data["DAYS_CREDIT_UPDATE"])<(-1.0)) *1.))) ,(((((((((( data["NAME_GOODS_CATEGORY_Insurance"])<(14.800000)) *1.))* 2.0)) <(data["NAME_PRODUCT_TYPE_walk_in"])) *1.))))))))
 v["i384"] = np.tanh(((((((data["DAYS_CREDIT"])*(np.where(0.036585>0, data["ca__Sold"], data["DAYS_CREDIT"])))) * 2.0)) -(np.where(data["ca__Sold"]>0, data["ca__Sold"], data["ca__Sold"]))))
v["i385"] = np.tanh(np.maximum(((((data["ca__Sold"])-(((2.0)* 2.0))))),(( np.tanh(((( data["DAYS_CREDIT"])*(((( data["NAME_PRODUCT_TYPE_walk_in"])>(((( 3.526320)<(data["NAME_PRODUCT_TYPE_walk_in"])) *1.))) *1.)))))))))
v["i386"] = np.tanh(((((((data["NAME_CASH_LOAN_PURPOSE_Other"])>(0.416667)) *1.))+(np.minimum(((((0.494118)/ 2.0))),(((( data["DAYS_ENDDATE_FACT"])*(( -1.0*(((( 0.494118)/ 2.0)))))))))))/2.0))
v["i387"] = np.tanh(np.minimum(((((( data["AMT_REQ_CREDIT_BUREAU_YEAR"])>(( -1.0*(( data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"])))))*1.))) ,(((( 0.576923)-(np.tanh(( np.maximum(((np.tanh(((((data["AMT_REQ_CREDIT_BUREAU_YEAR"])+(0.416667)) /2.0))))),(( data["AMT_REQ_CREDIT_BUREAU_YEAR"])))))))))))
 v["i388"] = np.tanh(((data["ca__Sold"])*(np.maximum(((( -1.0*(( data["ca__Sold"]))))),(((((((data["NAME_CASH_LOAN_PURPOSE_Business_development"])<(data["FLAG_DOCUMENT_10"])) *1.))+(((((data["ca__Sold"])-(((11.714300)/ 2.0)))) * 2.0)))))))))
 v["i389"] = np.tanh(( -1.0*(((( np.minimum(((((( data["DAYS_CREDIT"])+(2.0)) /2.0))),(((( data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"])*(data["DAYS_CREDIT"])))))) *(np.where(data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"]>0, 2.0, data["DAYS_CREDIT"])))))))
 v["i390"] = np.tanh(((data["AMT_CREDIT_SUM_DEBT"])*(((data["AMT_CREDIT_SUM_DEBT"])*(np.where(data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"]>0, data["AMT_CREDIT_SUM_DEBT"], np.tanh(( np.where(data["AMT_CREDIT_SUM_DEBT"]>0, data["NAME_GOODS_CATEGORY_Insurance"], np.minimum(((data["DAYS_LAST_DUE_1ST_VERSION"])) ,(( data["NAME_GOODS_CATEGORY_Insurance"]))))))))))))
v["i391"] = np.tanh(((((((((((((( data["AMT_CREDIT_MAX_OVERDUE"])>(np.where(data["AMT_CREDIT_MAX_OVERDUE"]>0, 0.062500, data["FLAG_DOCUMENT_4"])))*1.))* 2.0)) +(np.tanh(( data["AMT_CREDIT_MAX_OVERDUE"])))))+(data["FLAG_DOCUMENT_4"])))* 2.0)) * 2.0))
v["i392"] = np.tanh(np.minimum(((((data["FLAG_DOCUMENT_15"])*(data["NAME_CASH_LOAN_PURPOSE_Business_development"])))) ,(((( data["FLAG_EMAIL"])*(((((( data["DAYS_LAST_DUE_1ST_VERSION"])>(0.680000)) *1.))*(((data["FLAG_DOCUMENT_15"])*(data["FLAG_EMAIL"]))))))))))
v["i393"] = np.tanh(np.minimum(((((data["cc_bal_CNT_INSTALMENT_MATURE_CUM"])*(((data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"])+(data["NAME_GOODS_CATEGORY_Insurance"])))))) ,(((-1.0*(((((data["cu__currency_4"])+(((( data["cc_bal_CNT_INSTALMENT_MATURE_CUM"])>(((data["cc_bal_CNT_INSTALMENT_MATURE_CUM"])*(data["cc_bal_CNT_INSTALMENT_MATURE_CUM"])))) *1.))) /2.0))))))))
v["i394"] = np.tanh(((((((np.minimum(((data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"])) ,(( data["AMT_REQ_CREDIT_BUREAU_YEAR"])))) *(((0.043478)* 2.0)))) -(data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"])))-(((data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"])*(((data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"])*(data["AMT_REQ_CREDIT_BUREAU_YEAR"])))))))
 v["i395"] = np.tanh(((np.where(data["cc_bal_SK_DPD_DEF"]>0, data["cc_bal_SK_DPD"], data["ty__Loan_for_working_capital_replenishment"])) *(( -1.0*(((( 0.043478)-(((( 14.800000)>(((( data["ty__Loan_for_working_capital_replenishment"])+(2.0)) /2.0)))*1.)))))))))
v["i396"] = np.tanh(((0.043478)+(( -1.0*(( np.maximum(((((( 14.800000)<(data["ty__Loan_for_working_capital_replenishment"])) *1.))) ,(( np.where(data["ty__Loan_for_working_capital_replenishment"]>0,(( 2.235290)*(data["cc_bal_SK_DPD"])) , data["NAME_GOODS_CATEGORY_Insurance"]))))))))))
v["i397"] = np.tanh(((( 9.0)) *(((((( 0.680000)<(data["cc_bal_SK_DPD_DEF"])) *1.))+(((( data["NAME_CASH_LOAN_PURPOSE_Hobby"])>(((( data["cc_bal_SK_DPD_DEF"])+(0.0)) /2.0)))*1.))))))
 v["i398"] = np.tanh(np.where(data["cc_bal_SK_DPD"]>0, data["NAME_GOODS_CATEGORY_Insurance"],(( 0.686567)*(np.minimum(((( -1.0*(((( data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"])/ 2.0)))))) ,(((((-1.0*(( np.maximum(((data["FLAG_DOCUMENT_4"])) ,(( data["NAME_CASH_LOAN_PURPOSE_Business_development"])))))))/ 2.0))))))))
v["i399"] = np.tanh(np.where(data["NAME_GOODS_CATEGORY_Insurance"]>0, data["te_FLAG_DOCUMENT_19"],(((((( data["te_FLAG_DOCUMENT_19"])-(((((((13.52401924133300781)) <(data["avg_buro_buro_bal_status_4"])) *1.))/ 2.0)))) -(data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"])))*(data["avg_buro_buro_bal_status_4"]))))
v["i400"] = np.tanh(((data["avg_buro_buro_bal_status_4"])*(((((np.minimum(((data["cu__currency_4"])) ,(( data["te_FLAG_DOCUMENT_20"])))) -(data["NAME_CASH_LOAN_PURPOSE_Business_development"])))-(((np.maximum(((data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"])) ,(( data["cc_bal_SK_DPD"])))) * 2.0))))))
v["i401"] = np.tanh(((np.where(data["te_FLAG_DOCUMENT_19"]>0, data["te_FLAG_DOCUMENT_19"], data["avg_buro_buro_bal_status_4"])) *(( -1.0*(( np.where(data["NAME_CASH_LOAN_PURPOSE_Business_development"]>0, data["te_FLAG_DOCUMENT_19"], data["avg_buro_buro_bal_status_4"])))))))
 v["i402"] = np.tanh(np.minimum(((((( data["NAME_GOODS_CATEGORY_Insurance"])>(np.maximum(((np.minimum(((( -1.0*(( data["cu__currency_4"]))))),(( data["NAME_GOODS_CATEGORY_Insurance"]))))),(( data["te_FLAG_DOCUMENT_19"])))))*1.))) ,(((( data["te_FLAG_DOCUMENT_19"])+(((( 4.89787912368774414)) -(data["cu__currency_4"]))))))))
v["i403"] = np.tanh(np.where(data["FLAG_DOCUMENT_10"]>0, data["inst_AMT_PAYMENT"],(((((( data["inst_AMT_PAYMENT"])*(data["NAME_CASH_LOAN_PURPOSE_Buying_a_holiday_home___land"])))+(((data["NAME_CASH_LOAN_PURPOSE_Business_development"])*(((data["inst_AMT_PAYMENT"])+(data["HOUR_APPR_PROCESS_START_y"])))))))* 2.0)))
 v["i404"] = np.tanh(np.tanh(( np.tanh(( np.tanh(( np.tanh(((((( data["AMT_CREDIT_y"])+(((( -1.0*(( data["AMT_APPLICATION"])))) -(((( data["AMT_APPLICATION"])<(-1.0)) *1.))))))* 2.0))))))))))
v["i405"] = np.tanh(((data["NAME_CLIENT_TYPE_Repeater"])*(((0.036585)*(((((3.526320)*(((((data["NAME_CLIENT_TYPE_Repeater"])/ 2.0)) +(data["NAME_PRODUCT_TYPE_XNA"])))))* 2.0))))))
v["i406"] = np.tanh(((data["NAME_CONTRACT_TYPE_Revolving_loans"])*(((((0.680000)+(data["AMT_DOWN_PAYMENT"])))*(((data["inst_AMT_INSTALMENT"])*(data["NAME_PORTFOLIO_Cards"])))))))
 v["i407"] = np.tanh(((((data["NAME_CASH_LOAN_PURPOSE_Business_development"])*(((data["inst_AMT_PAYMENT"])*(((( 8.0)) *(((( data["NAME_CASH_LOAN_PURPOSE_Business_development"])>(np.minimum(((data["NAME_PRODUCT_TYPE_XNA"])) ,(( data["NAME_PRODUCT_TYPE_XNA"])))))*1.))))))))* 2.0))
v["i408"] = np.tanh(((( 0.680000)<(((((((data["inst_AMT_INSTALMENT"])-(0.416667)))*(((( np.tanh(( data["inst_AMT_INSTALMENT"])))>(((( data["PRODUCT_COMBINATION_Card_X_Sell"])<(data["te_FLAG_DOCUMENT_19"])) *1.))) *1.))))/ 2.0)))*1.))
 v["i409"] = np.tanh(np.minimum(((0.043478)) ,(( np.where(((((data["te_FLAG_DOCUMENT_19"])* 2.0)) +(data["PRODUCT_COMBINATION_Card_X_Sell"])) >0, np.tanh(( 14.800000)) ,(-1.0*(( data["PRODUCT_COMBINATION_Card_X_Sell"]))))))))
v["i410"] = np.tanh(((((data["te_FLAG_DOCUMENT_20"])+(np.tanh(( data["AMT_DOWN_PAYMENT"])))))*(((14.800000)*(((( 11.714300)<(((((data["AMT_DOWN_PAYMENT"])+(data["NAME_SELLER_INDUSTRY_XNA"])))+(data["NAME_SELLER_INDUSTRY_XNA"])))) *1.))))))
 v["i411"] = np.tanh(((( -1.0*(((((data["NAME_GOODS_CATEGORY_Insurance"])+(((((((((data["te_FLAG_DOCUMENT_20"])/ 2.0)) +(data["NFLAG_INSURED_ON_APPROVAL"])) /2.0)) +(np.where(data["NAME_CONTRACT_STATUS_Approved"]>0, data["CNT_INSTALMENT"], 0.697674)))/2.0)))/2.0)))))/ 2.0))
v["i412"] = np.tanh(np.where(data["NAME_YIELD_GROUP_high"]>0,(((((0.686567)/ 2.0)) <(data["inst_NUM_INSTALMENT_VERSION"])) *1.) ,(((data["NAME_YIELD_GROUP_low_action"])>(((((((((data["inst_NUM_INSTALMENT_VERSION"])/ 2.0)) >(data["NAME_YIELD_GROUP_high"])) *1.))>(data["AMT_APPLICATION"])) *1.))) *1.)))
v["i413"] = np.tanh(((((((((((( data["DAYS_REGISTRATION"])/ 2.0)) >(0.680000)) *1.))-(0.062500)))-(((data["DAYS_REGISTRATION"])*(0.062500)))))-(((data["DAYS_REGISTRATION"])*(0.062500)))))
 v["i414"] = np.tanh(np.minimum(((((((( data["inst_AMT_INSTALMENT"])>(0.339286)) *1.))*(( -1.0*(( data["PRODUCT_COMBINATION_POS_household_without_interest"]))))))),(( np.maximum(((data["NAME_YIELD_GROUP_low_normal"])) ,(( np.where(data["NAME_YIELD_GROUP_low_action"]>0, 0.043478, 0.797872))))))))
v["i415"] = np.tanh(((((( data["inst_AMT_PAYMENT"])>(0.494118)) *1.))*(np.minimum(((( -1.0*(((((((((data["inst_AMT_PAYMENT"])>(0.686567)) *1.))/ 2.0)) / 2.0)))))) ,(( data["NAME_YIELD_GROUP_low_normal"]))))))
v["i416"] = np.tanh(((( np.maximum(((np.where(((data["NAME_YIELD_GROUP_XNA"])*(data["AMT_DOWN_PAYMENT"])) >0, data["inst_AMT_INSTALMENT"],(-1.0*(( data["inst_AMT_INSTALMENT"])))))) ,(( 0.797872)))) <(((( 0.797872)<(data["inst_AMT_INSTALMENT"])) *1.))) *1.))
 v["i417"] = np.tanh(np.minimum(((( -1.0*(((((3.526320)<(data["NAME_YIELD_GROUP_low_action"])) *1.)))))),(((( data["NAME_PRODUCT_TYPE_walk_in"])*(( -1.0*(( np.maximum(((((( -1.0*(( data["NAME_CASH_LOAN_PURPOSE_Business_development"])))) *(2.0)))) ,(( data["NAME_YIELD_GROUP_low_action"]))))))))))))
v["i418"] = np.tanh(((((data["DAYS_LAST_DUE"])*(np.where(-1.0>0, data["NAME_CASH_LOAN_PURPOSE_Business_development"],(((( 6.0)) <(((((( data["PRODUCT_COMBINATION_Card_Street"])+(2.235290)) /2.0)) * 2.0)))*1.))))) * 2.0))
v["i419"] = np.tanh(((((data["REG_CITY_NOT_LIVE_CITY"])*(((( np.maximum(((np.tanh(( data["NAME_CASH_LOAN_PURPOSE_Business_development"])))) ,(( data["cc_bal_AMT_CREDIT_LIMIT_ACTUAL"])))) +(((data["te_FLAG_DOCUMENT_19"])*(data["PRODUCT_COMBINATION_Cash_X_Sell__low"])))) /2.0)))) -(((( 3.526320)<(data["PRODUCT_COMBINATION_Cash_X_Sell__low"])) *1.))))
 v["i420"] = np.tanh(((np.minimum(((((data["NAME_YIELD_GROUP_high"])/ 2.0))),(( data["CHANNEL_TYPE_Car_dealer"])))) *(((((np.maximum(((np.tanh(( data["inst_AMT_PAYMENT"])))) ,(( data["CHANNEL_TYPE_Car_dealer"])))) * 2.0)) +(((( data["NAME_YIELD_GROUP_high"])+(data["CHANNEL_TYPE_Car_dealer"])) /2.0))))))
v["i421"] = np.tanh(((((( np.tanh(((( np.tanh(((((data["AMT_GOODS_PRICE_y"])>(0.576923)) *1.))))*(data["inst_AMT_INSTALMENT"])))))>(((( 1.857140)>(((data["AMT_ANNUITY"])*(data["CNT_INSTALMENT_FUTURE"])))) *1.))) *1.))* 2.0))
v["i422"] = np.tanh(( -1.0*(((((1.220590)<(((( -1.0*(((((-1.0*(((-1.0*(((((-1.0*(( data["CNT_INSTALMENT"])))) -(((data["FLAG_DOCUMENT_4"])/ 2.0)))))))))) -(0.031746)))))) / 2.0)))*1.)))))
v["i423"] = np.tanh(((( data["CNT_INSTALMENT_FUTURE"])<(((np.minimum(((data["inst_AMT_INSTALMENT"])) ,(((((((data["CNT_INSTALMENT_FUTURE"])/ 2.0)) >(( -1.0*(( 0.323944)))))*1.))))) -(1.220590)))) *1.))
 v["i424"] = np.tanh(np.minimum(((((( data["CNT_INSTALMENT_FUTURE"])>(((3.0)+(data["NAME_YIELD_GROUP_middle"])))) *1.))) ,(((((( data["AMT_CREDIT_y"])-(np.minimum(((((data["NAME_YIELD_GROUP_middle"])* 2.0))),(((( data["NAME_GOODS_CATEGORY_Direct_Sales"])/ 2.0)))))))/ 2.0)))))
 v["i425"] = np.tanh(((((( data["te_FLAG_DOCUMENT_19"])<(((1.220590)*(((data["inst_AMT_PAYMENT"])+(-2.0)))))) *1.))*(((((data["inst_AMT_PAYMENT"])+(-2.0)))-(data["CNT_INSTALMENT"])))))
 v["i426"] = np.tanh(((((np.minimum(((0.062500)) ,(( np.minimum(((data["NAME_GOODS_CATEGORY_Insurance"])) ,(( data["NAME_SELLER_INDUSTRY_Connectivity"])))))))*(((( data["inst_AMT_PAYMENT"])+(np.minimum(((0.062500)) ,(( data["inst_AMT_PAYMENT"])))))/2.0)))) / 2.0))
v["i427"] = np.tanh(( -1.0*(( np.where(data["NAME_GOODS_CATEGORY_Photo___Cinema_Equipment"]>0,(( data["PRODUCT_COMBINATION_Card_X_Sell"])* 2.0),(((((data["NAME_GOODS_CATEGORY_Photo___Cinema_Equipment"])>(( -1.0*(( data["NAME_CASH_LOAN_PURPOSE_Business_development"])))))*1.))* 2.0))))))
v["i428"] = np.tanh(((( data["cc_bal_AMT_DRAWINGS_ATM_CURRENT"])>(((( np.minimum(((0.031746)) ,(((((data["NAME_GOODS_CATEGORY_Insurance"])+(data["cc_bal_CNT_INSTALMENT_MATURE_CUM"])) /2.0)))))<(((data["cc_bal_AMT_PAYMENT_CURRENT"])-(data["NAME_GOODS_CATEGORY_Insurance"])))) *1.))) *1.))
 v["i429"] = np.tanh(((((((( data["cc_bal_CNT_DRAWINGS_ATM_CURRENT"])>(0.697674)) *1.))* 2.0)) *(np.minimum(((( -1.0*(( 0.0))))),(((( data["cc_bal_CNT_DRAWINGS_ATM_CURRENT"])-(( 14.39841270446777344)))))))))
 v["i430"] = np.tanh(((((( data["cc_bal_AMT_BALANCE"])*(data["cc_bal_CNT_DRAWINGS_CURRENT"])))>(((( data["cc_bal_CNT_DRAWINGS_CURRENT"])>(((data["cc_bal_AMT_BALANCE"])*(((((((( data["cc_bal_CNT_DRAWINGS_CURRENT"])/ 2.0)) / 2.0)) >(data["cc_bal_CNT_DRAWINGS_CURRENT"])) *1.))))) *1.))) *1.))
 v["i431"] = np.tanh(((np.minimum(((0.323944)) ,(( np.minimum(((((data["inst_NUM_INSTALMENT_VERSION"])*(data["inst_NUM_INSTALMENT_VERSION"])))) ,(((((data["inst_NUM_INSTALMENT_VERSION"])+(0.686567)) /2.0)))))))) +(((( data["cc_bal_MONTHS_BALANCE"])>(1.105260)) *1.))))
 v["i432"] = np.tanh(np.minimum(((((data["inst_AMT_PAYMENT"])*(( -1.0*(((((data["DAYS_FIRST_DRAWING"])<(((data["NAME_CASH_LOAN_PURPOSE_Business_development"])+(((((( data["inst_AMT_PAYMENT"])*(data["inst_AMT_PAYMENT"])))>(( 11.87245082855224609)))*1.))))) *1.)))))))),(( 0.062500))))
v["i433"] = np.tanh(((((( data["cc_bal_AMT_PAYMENT_CURRENT"])-(( -1.0*(( 0.031746)))))) >(((((( data["cc_bal_AMT_PAYMENT_CURRENT"])*(data["te_FLAG_DOCUMENT_19"])))>(( -1.0*(((( 0.036585)* 2.0)))))) *1.))) *1.))
 v["i434"] = np.tanh(( -1.0*(((((((np.minimum(((data["AMT_APPLICATION"])) ,(( data["YEARS_BUILD_AVG"])))) *(((( 1.0)>(data["cc_bal_AMT_CREDIT_LIMIT_ACTUAL"])) *1.))))>(((np.minimum(((data["AMT_APPLICATION"])) ,(( 0.043478)))) / 2.0)))*1.)))))
v["i435"] = np.tanh(((data["YEARS_BUILD_MEDI"])*(((((((( -1.0*(( 0.062500)))) >(((data["te_FLAG_DOCUMENT_19"])+(data["AMT_APPLICATION"])))) *1.))+(((data["te_FLAG_DOCUMENT_19"])*(((( -1.0)+(data["YEARS_BUILD_MEDI"])) /2.0)))))/2.0))))
v["i436"] = np.tanh(((np.where(data["AMT_CREDIT_y"]>0,(((((( 0.062500)+(( 10.65713787078857422)))/2.0)) +(data["AMT_CREDIT_y"])) /2.0), -3.0)) *(((( 3.526320)<(((data["AMT_ANNUITY"])-(0.494118)))) *1.))))
 v["i437"] = np.tanh(((((data["inst_AMT_INSTALMENT"])*(((( np.where(((( data["AMT_APPLICATION"])>(( 7.21875619888305664)))*1.) >0, data["AMT_APPLICATION"], data["CNT_INSTALMENT_FUTURE"])) >(( 6.57621288299560547)))*1.))))* 2.0))
v["i438"] = np.tanh(((3.526320)*(((data["inst_AMT_INSTALMENT"])*(((( 3.526320)<(((( data["AMT_APPLICATION"])+(data["AMT_APPLICATION"])) /2.0)))*1.))))))
 v["i439"] = np.tanh(np.minimum(((data["CHANNEL_TYPE_Car_dealer"])) ,(((((((((data["CHANNEL_TYPE_Car_dealer"])*(data["CHANNEL_TYPE_Car_dealer"])))*(((0.036585)*(data["NAME_SELLER_INDUSTRY_Connectivity"])))))+(( -1.0*(( data["CHANNEL_TYPE_Car_dealer"])))))/2.0)))))
 v["i440"] = np.tanh(((((np.minimum(((((np.minimum(((((0.036585)* 2.0))),(( data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])))) * 2.0))),(( data["FLAG_DOCUMENT_4"])))) *(np.tanh(( data["DAYS_FIRST_DRAWING"])))))-(((data["FLAG_DOCUMENT_4"])/ 2.0))))
v["i441"] = np.tanh(( -1.0*(((( data["DAYS_LAST_DUE_1ST_VERSION"])*(((data["DAYS_LAST_DUE_1ST_VERSION"])*(((( data["cc_bal_CNT_INSTALMENT_MATURE_CUM"])>(((((((( 0.576923)<(data["te_FLAG_DOCUMENT_19"])) *1.))/ 2.0)) * 2.0)))*1.)))))))))
v["i442"] = np.tanh(((((( 0.323944)<(data["inst_NUM_INSTALMENT_NUMBER"])) *1.))*(((((( data["inst_SK_ID_PREV"])<(((((( 0.494118)<(data["inst_NUM_INSTALMENT_NUMBER"])) *1.))*(0.494118)))) *1.))* 2.0))))
v["i443"] = np.tanh(np.where(data["cc_bal_AMT_DRAWINGS_CURRENT"]>0, 0.0,(((((( data["inst_NUM_INSTALMENT_NUMBER"])* 2.0)) *(((( 0.416667)<(((data["inst_NUM_INSTALMENT_NUMBER"])-(data["inst_SK_ID_PREV"])))) *1.))))* 2.0)))
 v["i444"] = np.tanh(((np.minimum(((data["inst_AMT_INSTALMENT"])) ,(((((data["SK_ID_PREV_x"])+(np.minimum(((( -1.0*(( np.where(data["inst_AMT_INSTALMENT"]>0, data["inst_NUM_INSTALMENT_NUMBER"], data["cc_bal_CNT_INSTALMENT_MATURE_CUM"])))))) ,(( 0.036585)))))/2.0)))))/ 2.0))
v["i445"] = np.tanh(((((( -1.0*(((( data["inst_AMT_PAYMENT"])* 2.0)))))+(np.tanh(( 0.686567)))))*(((np.minimum(((data["inst_AMT_PAYMENT"])) ,(( np.minimum(((data["inst_AMT_PAYMENT"])) ,(( 0.062500)))))))*(data["PRODUCT_COMBINATION_Cash_Street__low"])))))
 v["i446"] = np.tanh(np.where(data["NAME_CONTRACT_STATUS_Canceled"]>0, np.where(data["te_FLAG_EMAIL"]>0, data["NAME_CASH_LOAN_PURPOSE_Buying_a_new_car"],(((0.494118)<(data["CNT_INSTALMENT_FUTURE"])) *1.)), np.minimum(((((( 0.494118)<(data["CNT_INSTALMENT_FUTURE"])) *1.))) ,(( 0.494118)))))
 v["i447"] = np.tanh(((np.minimum(((0.680000)) ,(((-1.0*(( np.where(( -1.0*(( data["NAME_SELLER_INDUSTRY_Connectivity"])))>0,(((data["CNT_PAYMENT"])>(2.235290)) *1.) ,(((0.043478)<(-2.0)) *1.))))))))) * 2.0))
v["i448"] = np.tanh(np.where(((( data["PRODUCT_COMBINATION_Cash_Street__low"])>(np.tanh(((6.05926561355590820)))))*1.) >0,(((data["AMT_APPLICATION"])+(data["AMT_CREDIT_y"])) /2.0), np.minimum(((( -1.0*(( data["PRODUCT_COMBINATION_Cash_Street__low"]))))),(((( data["PRODUCT_COMBINATION_Cash_Street__low"])*(data["CNT_INSTALMENT"])))))))
 v["i449"] = np.tanh(((np.where(((( 2.235290)>(data["CNT_INSTALMENT_FUTURE"])) *1.) >0,(((data["CNT_PAYMENT"])>(3.0)) *1.) ,(((data["CNT_INSTALMENT_FUTURE"])>(3.0)) *1.))) * 2.0))
v["i450"] = np.tanh(np.minimum(((((data["te_FLAG_DOCUMENT_19"])*(((( data["NAME_CASH_LOAN_PURPOSE_Business_development"])<(data["PRODUCT_COMBINATION_Cash_Street__low"])) *1.))))) ,(((((( data["te_FLAG_DOCUMENT_19"])*(np.maximum(((data["PRODUCT_COMBINATION_Cash_Street__low"])) ,(( data["te_FLAG_DOCUMENT_19"])))))) *(data["NAME_CASH_LOAN_PURPOSE_XNA"]))))))
v["i451"] = np.tanh(((((((np.maximum(((data["cc_bal_AMT_DRAWINGS_CURRENT"])) ,(((-1.0*(( 0.043478)))))))*(((1.220590)-(data["NAME_CASH_LOAN_PURPOSE_Education"])))))+(((data["te_FLAG_DOCUMENT_19"])/ 2.0)))) *(data["NAME_CASH_LOAN_PURPOSE_Education"])))
 v["i452"] = np.tanh(np.minimum(((((((((2.0)) +(0.036585)))>(((( data["cc_bal_AMT_BALANCE"])+(data["te_FLAG_DOCUMENT_19"])) /2.0)))*1.))) ,(((((data["cc_bal_AMT_BALANCE"])>(((( 2.0)) +(data["NAME_CASH_LOAN_PURPOSE_Business_development"])))) *1.)))))
v["i453"] = np.tanh(((( 0.062500)<(np.where(((data["DAYS_LAST_DUE_1ST_VERSION"])-(0.323944)) >0,(((( data["DAYS_LAST_DUE_1ST_VERSION"])-(0.339286)))*(((( data["NAME_SELLER_INDUSTRY_Connectivity"])>(data["DAYS_LAST_DUE_1ST_VERSION"])) *1.))) , data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])))*1.))
 v["i454"] = np.tanh(np.tanh(( np.tanh(((( data["DAYS_FIRST_DRAWING"])*(np.tanh(((((((data["DAYS_FIRST_DRAWING"])<(((((-1.0*(( data["DAYS_FIRST_DRAWING"])))) <(data["inst_NUM_INSTALMENT_NUMBER"])) *1.))) *1.))*(data["inst_NUM_INSTALMENT_NUMBER"])))))))))))
 v["i455"] = np.tanh(((((((((data["cc_bal_AMT_DRAWINGS_CURRENT"])>(((( data["te_FLAG_DOCUMENT_19"])+(data["inst_AMT_PAYMENT"])) /2.0)))*1.))<(data["cc_bal_AMT_DRAWINGS_CURRENT"])) *1.))*(((((data["inst_AMT_PAYMENT"])* 2.0)) *(( -1.0*(( 1.857140))))))))
v["i456"] = np.tanh(np.where(data["inst_SK_ID_PREV"]>0,(((np.where(data["inst_SK_ID_PREV"]>0, 3.0, 1.105260)) <(data["inst_SK_ID_PREV"])) *1.) ,(((((( data["cc_bal_AMT_PAYMENT_CURRENT"])* 2.0)) * 2.0)) * 2.0)))
 v["i457"] = np.tanh(np.where(((( data["ty__Loan_for_working_capital_replenishment"])<(data["FLAG_DOCUMENT_10"])) *1.) >0, np.where(data["NAME_PORTFOLIO_Cards"]>0,(-1.0*(( data["CHANNEL_TYPE_Car_dealer"]))), data["cc_bal_AMT_DRAWINGS_CURRENT"]), np.maximum(((((data["ty__Loan_for_working_capital_replenishment"])* 2.0))),(( data["cc_bal_AMT_DRAWINGS_CURRENT"])))))
 v["i458"] = np.tanh(((data["cc_bal_AMT_DRAWINGS_POS_CURRENT"])*(np.minimum(((( -1.0*(((( data["cc_bal_CNT_DRAWINGS_CURRENT"])*(((data["cc_bal_AMT_DRAWINGS_POS_CURRENT"])*(data["cc_bal_AMT_DRAWINGS_POS_CURRENT"]))))))))),(( data["cc_bal_AMT_DRAWINGS_POS_CURRENT"]))))))
v["i459"] = np.tanh(np.maximum(((((data["ty__Loan_for_working_capital_replenishment"])*(-2.0)))) ,(( np.maximum(((((data["ty__Loan_for_working_capital_replenishment"])*(data["SK_ID_PREV_y"])))) ,(((((data["cc_bal_CNT_DRAWINGS_POS_CURRENT"])+(((0.036585)+(-2.0)))) /2.0))))))))
v["i460"] = np.tanh(((((((((( data["cc_bal_AMT_DRAWINGS_CURRENT"])<(data["cc_bal_AMT_PAYMENT_CURRENT"])) *1.))* 2.0)) * 2.0)) *(np.minimum(((data["cc_bal_AMT_PAYMENT_CURRENT"])) ,(((( 0.043478)-(np.minimum(((data["te_FLAG_DOCUMENT_19"])) ,(( data["cc_bal_AMT_DRAWINGS_CURRENT"])))))))))))
 v["i461"] = np.tanh(((((((((((data["te_FLAG_DOCUMENT_19"])*(((( data["cc_bal_AMT_PAYMENT_CURRENT"])>(np.maximum(((((data["te_FLAG_DOCUMENT_19"])+(2.0)))) ,(( 0.697674)))))*1.))))* 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i462"] = np.tanh(((( -1.0*(( np.where(data["cc_bal_AMT_DRAWINGS_ATM_CURRENT"]>0,(((data["cc_bal_AMT_DRAWINGS_ATM_CURRENT"])>(( 1.95135760307312012)))*1.) ,(((-1.0*(((((( 1.95135760307312012)) <(data["cc_bal_AMT_DRAWINGS_CURRENT"])) *1.))))) / 2.0)))))) * 2.0))
v["i463"] = np.tanh(((((np.where(np.minimum(((data["cc_bal_AMT_PAYMENT_CURRENT"])) ,(( data["ty__Loan_for_working_capital_replenishment"])))>0, 1.857140, data["FLAG_DOCUMENT_10"])) * 2.0)) *(((( data["NAME_YIELD_GROUP_high"])<(( -1.0*(( 0.576923)))))*1.))))
 v["i464"] = np.tanh(((data["te_FONDKAPREMONT_MODE"])*(((((data["NAME_CASH_LOAN_PURPOSE_Business_development"])+(np.where(data["cc_bal_AMT_DRAWINGS_CURRENT"]>0, data["cc_bal_AMT_DRAWINGS_CURRENT"], data["ty__Loan_for_working_capital_replenishment"])))) +(((data["NAME_CASH_LOAN_PURPOSE_Business_development"])* 2.0))))))
v["i465"] = np.tanh(((( -1.0*(((((((( data["ty__Loan_for_working_capital_replenishment"])+(data["cc_bal_AMT_DRAWINGS_CURRENT"])) /2.0)) +(((data["ty__Loan_for_working_capital_replenishment"])*(( -1.0*(((((data["cc_bal_AMT_DRAWINGS_CURRENT"])+(((data["NAME_CASH_LOAN_PURPOSE_Business_development"])* 2.0)))/2.0)))))))) /2.0)))))/ 2.0))
v["i466"] = np.tanh(((((((((-1.0*(((( data["ty__Real_estate_loan"])-(( -1.0*(( data["cc_bal_AMT_PAYMENT_CURRENT"])))))))))+(data["te_FLAG_DOCUMENT_19"])))>(((( data["cc_bal_AMT_PAYMENT_CURRENT"])<(data["te_FLAG_DOCUMENT_19"])) *1.))) *1.))* 2.0))
v["i467"] = np.tanh(((((((data["cc_bal_AMT_PAYMENT_CURRENT"])* 2.0)) *(((2.0)* 2.0)))) *(((((((( -1.0*(( 0.576923)))) +(data["cc_bal_AMT_TOTAL_RECEIVABLE"])) /2.0)) >(data["cc_bal_AMT_TOTAL_RECEIVABLE"])) *1.))))
 v["i468"] = np.tanh(((data["ty__Loan_for_working_capital_replenishment"])+(np.where(((data["cc_bal_SK_DPD_DEF"])+(data["NAME_GOODS_CATEGORY_Insurance"])) >0, data["cc_bal_AMT_DRAWINGS_CURRENT"],(((((( data["cc_bal_AMT_DRAWINGS_CURRENT"])* 2.0)) *(data["ty__Loan_for_working_capital_replenishment"])))* 2.0)))))
 v["i469"] = np.tanh(((((( 3.526320)<(((np.maximum(((((3.526320)/ 2.0))),(( data["cc_bal_AMT_INST_MIN_REGULARITY"])))) -(((data["te_FLAG_DOCUMENT_19"])*(( -1.0*(( 3.526320)))))))))*1.))*(((data["cc_bal_AMT_INST_MIN_REGULARITY"])/ 2.0))))
v["i470"] = np.tanh(( -1.0*(( np.where(data["ty__Loan_for_working_capital_replenishment"]>0,(( data["NAME_CASH_LOAN_PURPOSE_Business_development"])-(((( data["SK_ID_PREV_y"])<(np.tanh(( -3.0)))) *1.))) , data["NAME_CASH_LOAN_PURPOSE_Business_development"])))))
 v["i471"] = np.tanh(((np.maximum(((data["cc_bal_SK_DPD_DEF"])) ,(( data["NAME_CASH_LOAN_PURPOSE_Business_development"])))) *(((((( 14.800000)+(((( 14.800000)+(((((-1.0*(( data["cc_bal_SK_DPD_DEF"])))) +(data["te_FLAG_DOCUMENT_19"])) /2.0)))/2.0)))/2.0)) *(data["cc_bal_AMT_DRAWINGS_CURRENT"])))))
 v["i472"] = np.tanh(((((( data["cc_bal_AMT_PAYMENT_CURRENT"])>(((2.235290)-(((data["NAME_CASH_LOAN_PURPOSE_Business_development"])*(((((data["cc_bal_AMT_PAYMENT_CURRENT"])-(np.minimum(((0.494118)) ,(( data["cc_bal_AMT_PAYMENT_CURRENT"])))))) * 2.0)))))))*1.))* 2.0))
v["i473"] = np.tanh(((((((((((np.where(data["ty__Real_estate_loan"]>0,(( data["NAME_GOODS_CATEGORY_Insurance"])* 2.0),(( data["NAME_GOODS_CATEGORY_Insurance"])*(data["ty__Loan_for_working_capital_replenishment"])))) / 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i474"] = np.tanh(((data["ty__Loan_for_working_capital_replenishment"])*(((( np.maximum(((np.tanh(( data["ty__Loan_for_working_capital_replenishment"])))) ,(( data["FLAG_DOCUMENT_4"])))) +(((((data["NAME_CASH_LOAN_PURPOSE_Purchase_of_electronic_equipment"])*(data["ty__Loan_for_working_capital_replenishment"])))-(data["FLAG_DOCUMENT_10"])))) /2.0))))
v["i475"] = np.tanh(((( -1.0*(((((((14.800000)* 2.0)) <(((data["NAME_CASH_LOAN_PURPOSE_Purchase_of_electronic_equipment"])+(((((( 1.98723483085632324)) *(data["ty__Loan_for_working_capital_replenishment"])))+(((data["FLAG_DOCUMENT_10"])+(data["ty__Loan_for_working_capital_replenishment"])))))))) *1.))))) * 2.0))
v["i476"] = np.tanh(np.where(((( data["ty__Loan_for_working_capital_replenishment"])>(( 10.0)))*1.) >0,(-1.0*(( np.maximum(((data["ty__Loan_for_working_capital_replenishment"])) ,(( np.maximum(((data["ty__Loan_for_working_capital_replenishment"])) ,(( data["ty__Loan_for_working_capital_replenishment"]))))))))), np.maximum(((data["ty__Loan_for_working_capital_replenishment"])) ,(((-1.0*(( data["ty__Loan_for_working_capital_replenishment"]))))))))
v["i477"] = np.tanh(np.minimum(((((((((data["avg_buro_buro_bal_status_1"])*(data["EXT_SOURCE_3"])))*(data["EXT_SOURCE_3"])))*(data["EXT_SOURCE_3"])))) ,(((((((data["avg_buro_buro_bal_status_1"])*(data["EXT_SOURCE_3"])))>(np.tanh(( 0.680000)))) *1.)))))
v["i478"] = np.tanh(((data["te_FLAG_DOCUMENT_19"])*(np.where(((( data["avg_buro_buro_bal_status_3"])>(((data["te_FLAG_DOCUMENT_19"])+(1.220590)))) *1.) >0, 14.800000,(-1.0*(((( data["avg_buro_buro_bal_status_3"])*(data["te_FLAG_DOCUMENT_19"])))))))))
 v["i479"] = np.tanh(( -1.0*(((((( data["FLAG_DOCUMENT_19"])* 2.0)) *(((( data["FLAG_DOCUMENT_3"])+(((((( data["FLAG_DOCUMENT_3"])+(((data["te_FLAG_DOCUMENT_19"])+(((data["FLAG_DOCUMENT_19"])/ 2.0)))))/2.0)) / 2.0)))/2.0)))))))
 v["i480"] = np.tanh(((((data["te_FLAG_DOCUMENT_19"])+(((data["CODE_REJECT_REASON_SYSTEM"])*(data["NAME_TYPE_SUITE_Other_B"])))))*(((data["NAME_TYPE_SUITE_Other_B"])-(((data["NAME_CASH_LOAN_PURPOSE_Business_development"])-(data["CODE_REJECT_REASON_SYSTEM"])))))))
 v["i481"] = np.tanh(((((((data["te_FLAG_DOCUMENT_17"])-(data["te_FLAG_DOCUMENT_10"])))*(11.714300)))*(np.where(((data["te_FLAG_DOCUMENT_17"])-(data["te_FLAG_DOCUMENT_10"])) >0,(( data["te_FLAG_DOCUMENT_10"])* 2.0), data["te_SK_ID_CURR"]))))
v["i482"] = np.tanh(((((( data["cc_bal_cc_bal_status__Refused"])* 2.0)) >(((( data["SK_ID_PREV_y"])>(( -1.0*(( np.maximum(((0.043478)) ,(((((2.235290)>(( -1.0*(( 14.800000)))))*1.))))))))) *1.))) *1.))
 v["i483"] = np.tanh(((np.maximum(((np.tanh(( np.maximum(((data["NAME_GOODS_CATEGORY_Insurance"])) ,(((( data["inst_AMT_PAYMENT"])*(data["Active"]))))))))),(( 0.0)))) *(((data["Completed"])*(data["Active"])))))
 v["i484"] = np.tanh(np.minimum(((np.tanh(((((0.043478)>(data["inst_AMT_PAYMENT"])) *1.))))) ,(((( np.where(data["inst_AMT_PAYMENT"]>0, data["te_FLAG_DOCUMENT_19"],(((-1.0*(((((0.043478)+(data["inst_AMT_PAYMENT"])) /2.0)))))* 2.0)))/ 2.0)))))
 v["i485"] = np.tanh(np.minimum(((((np.tanh(( data["inst_AMT_INSTALMENT"])))+(0.323944)))) ,(((( data["inst_AMT_INSTALMENT"])*(np.where(((( data["AMT_GOODS_PRICE_y"])+(0.031746)) /2.0)>0, 0.043478, data["inst_AMT_INSTALMENT"])))))))
 v["i486"] = np.tanh(((((((((( 0.036585)*(((((data["DAYS_FIRST_DRAWING"])*(((((data["te_REG_REGION_NOT_LIVE_REGION"])/ 2.0)) / 2.0)))) * 2.0)))) >(((( data["inst_AMT_PAYMENT"])+(data["DAYS_FIRST_DRAWING"])) /2.0)))*1.))/ 2.0)) / 2.0))
v["i487"] = np.tanh(np.minimum(((((( 0.043478)+(np.tanh(((-1.0*(((((data["EXT_SOURCE_3"])<(( -1.0*(( np.where(data["NAME_YIELD_GROUP_high"]>0, data["AMT_GOODS_PRICE_y"], 0.686567)))))) *1.))))))))/2.0))),(( 0.043478))))
v["i488"] = np.tanh(((((((((( -2.0)>(np.minimum(((((( data["te_FLAG_DOCUMENT_20"])<(0.036585)) *1.))) ,(( data["DAYS_CREDIT"])))))*1.))* 2.0)) *(np.maximum(((data["inst_AMT_PAYMENT"])) ,(( data["te_FLAG_DOCUMENT_20"])))))) * 2.0))
v["i489"] = np.tanh(((((((data["DAYS_CREDIT"])-(((data["DAYS_CREDIT_UPDATE"])*(data["DAYS_CREDIT_UPDATE"])))))/ 2.0)) *(((((( data["DAYS_CREDIT"])+(((data["DAYS_CREDIT_UPDATE"])*(np.tanh(( data["DAYS_CREDIT"])))))) /2.0)) / 2.0))))
v["i490"] = np.tanh(((((data["DAYS_CREDIT"])*(data["NAME_SELLER_INDUSTRY_Connectivity"])))*(((((data["te_FLAG_CONT_MOBILE"])*(((( 0.494118)<(data["te_FLAG_CONT_MOBILE"])) *1.))))-(data["te_FLAG_CONT_MOBILE"])))))
 v["i491"] = np.tanh(((((data["DAYS_CREDIT"])*(np.maximum(((((0.062500)*(((data["inst_AMT_PAYMENT"])-(((data["DAYS_CREDIT"])*(((data["DAYS_CREDIT"])*(data["inst_AMT_PAYMENT"])))))))))) ,(( 0.062500)))))) * 2.0))
v["i492"] = np.tanh(((0.062500)*(np.where(data["inst_AMT_PAYMENT"]>0,(( np.minimum(((( -1.0*(( data["inst_AMT_PAYMENT"]))))),(( data["te_FLAG_DOCUMENT_20"])))) * 2.0),(-1.0*(((( data["te_FLAG_CONT_MOBILE"])-(data["inst_AMT_PAYMENT"])))))))))
 v["i493"] = np.tanh(np.where(((0.797872)-(data["DAYS_CREDIT_UPDATE"])) >0,(((((( data["AMT_CREDIT_x"])/ 2.0)) *(data["AMT_CREDIT_x"])))*(data["inst_AMT_INSTALMENT"])) ,(( 0.0)-(((data["AMT_CREDIT_x"])/ 2.0)))))
 v["i494"] = np.tanh(((((0.339286)*(((((((( data["DAYS_DECISION"])/ 2.0)) >(data["AMT_CREDIT_x"])) *1.))-(data["DAYS_DECISION"])))))*(data["AMT_CREDIT_x"])))
 v["i495"] = np.tanh(((((((( 0.062500)>(data["DAYS_CREDIT_UPDATE"])) *1.))*(((np.maximum(((data["NAME_SELLER_INDUSTRY_Connectivity"])) ,(((((0.036585)>(data["inst_AMT_PAYMENT"])) *1.))))) / 2.0)))) *(((1.220590)/ 2.0))))
v["i496"] = np.tanh(((( -1.0*(( data["te_FLAG_DOCUMENT_11"])))) *(np.where(data["NAME_GOODS_CATEGORY_Insurance"]>0, 0.494118, np.minimum(((data["te_FLAG_DOCUMENT_11"])) ,(( 11.714300)))))))
 v["i497"] = np.tanh(np.where(data["PRODUCT_COMBINATION_POS_mobile_without_interest"]>0, np.minimum(((np.minimum(((data["inst_AMT_PAYMENT"])) ,(( data["PRODUCT_COMBINATION_POS_mobile_without_interest"]))))),(( 0.043478))), np.minimum(((0.043478)) ,(( np.maximum(((data["PRODUCT_COMBINATION_Cash"])) ,(((-1.0*(( data["inst_AMT_PAYMENT"])))))))))))
 v["i498"] = np.tanh(((np.where(np.where(data["AMT_GOODS_PRICE_y"]>0, data["te_FLAG_DOCUMENT_20"], 0.036585)>0, 0.036585,(((np.tanh(( data["te_FLAG_OWN_CAR"])))+(data["AMT_GOODS_PRICE_y"])) /2.0)))*(data["AMT_GOODS_PRICE_y"])))
 v["i499"] = np.tanh(((11.714300)*(((((( data["cc_bal_AMT_DRAWINGS_CURRENT"])*(np.minimum(((data["CNT_INSTALMENT"])) ,(((((1.0)<(np.minimum(((data["CNT_INSTALMENT"])) ,(( data["cc_bal_AMT_DRAWINGS_CURRENT"])))))*1.))))))) >(0.494118)) *1.))))
 v["i500"] = np.tanh(((( -1.0*(( data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])))) *(((( 3.0)<(((( data["cc_bal_AMT_PAYMENT_TOTAL_CURRENT"])+(((((( 2.0)<(data["cc_bal_AMT_DRAWINGS_CURRENT"])) *1.))* 2.0)))/2.0)))*1.))))
 v["i501"] = np.tanh(((((((data["te_FLAG_OWN_CAR"])/ 2.0)) / 2.0)) *(((((((data["NAME_CONTRACT_STATUS_Canceled"])/ 2.0)) *(data["NAME_GOODS_CATEGORY_Insurance"])))-(((data["NAME_CONTRACT_STATUS_Canceled"])/ 2.0))))))
v["i502"] = np.tanh(((((data["inst_AMT_PAYMENT"])*(data["NAME_CONTRACT_STATUS_Canceled"])))*(np.minimum(((( 4.64522552490234375))),(((-1.0*(( data["cc_bal_AMT_INST_MIN_REGULARITY"])))))))))
 v["i503"] = np.tanh(( -1.0*(((( np.maximum(((((( -1.0*(( data["CNT_INSTALMENT"])))) *(0.339286)))) ,(((((( 6.0)) <(data["inst_AMT_PAYMENT"])) *1.))))) *(np.maximum(((data["NAME_CONTRACT_STATUS_Canceled"])) ,(( data["CNT_INSTALMENT"])))))))))
 v["i504"] = np.tanh(np.minimum(((((( 0.043478)+(((3.526320)-(data["PRODUCT_COMBINATION_Cash"])))) /2.0))),(((((( 0.043478)*(((data["PRODUCT_COMBINATION_Cash"])-(data["te_FLAG_OWN_CAR"])))))* 2.0)))))
 v["i505"] = np.tanh(np.minimum(((((data["NAME_GOODS_CATEGORY_Direct_Sales"])*(data["DAYS_LAST_DUE_1ST_VERSION"])))) ,(((((( 3.526320)*(data["DAYS_LAST_DUE_1ST_VERSION"])))*(((( data["NAME_GOODS_CATEGORY_Direct_Sales"])+(np.tanh(((((2.235290)<(data["CNT_INSTALMENT"])) *1.))))) /2.0)))))))
 v["i506"] = np.tanh(np.minimum(((np.maximum(((((((-1.0*(( data["ca__Closed"])))) <(data["ca__Closed"])) *1.))) ,(( data["CNT_INSTALMENT_FUTURE"]))))),(((((data["ty__Credit_card"])>(np.maximum(((data["CNT_INSTALMENT_FUTURE"])) ,(( 0.416667)))))*1.)))))
v["i507"] = np.tanh(((((( data["te_FLAG_DOCUMENT_19"])*(data["NAME_GOODS_CATEGORY_Direct_Sales"])))+(np.where(data["te_FLAG_DOCUMENT_19"]>0,(( data["te_FLAG_DOCUMENT_19"])*(data["NAME_GOODS_CATEGORY_Insurance"])) , data["cc_bal_AMT_INST_MIN_REGULARITY"])))/2.0))
v["i508"] = np.tanh(((((( data["cc_bal_AMT_INST_MIN_REGULARITY"])>(np.maximum(((data["cc_bal_AMT_DRAWINGS_CURRENT"])) ,(( 0.494118)))))*1.))*(( -1.0*(((((np.maximum(((data["cc_bal_AMT_RECEIVABLE_PRINCIPAL"])) ,(( 0.494118)))) >(((data["cc_bal_AMT_TOTAL_RECEIVABLE"])-(0.0)))) *1.)))))))
v["i509"] = np.tanh(((( -1.0*(( data["NAME_GOODS_CATEGORY_Direct_Sales"])))) *(((( -1.0*(( data["ty__Real_estate_loan"])))) -(((( data["te_FLAG_DOCUMENT_19"])+(((data["NAME_CASH_LOAN_PURPOSE_Business_development"])+(((data["ty__Interbank_credit"])+(data["FLAG_DOCUMENT_10"])))))) /2.0))))))
v["i510"] = np.tanh(np.where(data["CNT_CREDIT_PROLONG"]>0, data["te_FLAG_DOCUMENT_19"], np.minimum(((((np.maximum(((np.minimum(((( -1.0*(( data["te_FLAG_DOCUMENT_19"]))))),(( 0.323944))))),(( data["CNT_CREDIT_PROLONG"])))) *(data["AMT_REQ_CREDIT_BUREAU_MON"])))) ,(((( 1.0)* 2.0))))))
v["i511"] = np.tanh(( -1.0*(( np.maximum(((((( data["CNT_CREDIT_PROLONG"])>(((( 9.95406532287597656)) +(((data["FLAG_DOCUMENT_15"])-(np.maximum(((data["FLAG_DOCUMENT_15"])) ,(( data["NAME_GOODS_CATEGORY_Education"])))))))))*1.))) ,(((( data["FLAG_DOCUMENT_15"])-(data["CNT_CREDIT_PROLONG"])))))))))
 return v<categorify>
yhat_val_ft = model_ft.predict(X_val_ft)
 print(yhat_val_ft[60:70] )
gc.enable()
buro_bal = pd.read_csv('.. /input/bureau_balance.csv')
 print('Buro bal shape : ', buro_bal.shape)
 print('transform to dummies')
 buro_bal = pd.concat([buro_bal, pd.get_dummies(buro_bal.STATUS, prefix='buro_bal_status')], axis=1 ).drop('STATUS', axis=1)
 print('Counting buros')
 buro_counts = buro_bal[['SK_ID_BUREAU', 'MONTHS_BALANCE']].groupby('SK_ID_BUREAU' ).count()
buro_bal['buro_count'] = buro_bal['SK_ID_BUREAU'].map(buro_counts['MONTHS_BALANCE'])
 print('averaging buro bal')
 avg_buro_bal = buro_bal.groupby('SK_ID_BUREAU' ).mean()
avg_buro_bal.columns = ['avg_buro_' + f_ for f_ in avg_buro_bal.columns]
del buro_bal
gc.collect()
print('Read Bureau')
 buro = pd.read_csv('.. /input/bureau.csv')
 print('Go to dummies')
 buro_credit_active_dum = pd.get_dummies(buro.CREDIT_ACTIVE, prefix='ca_')
 buro_credit_currency_dum = pd.get_dummies(buro.CREDIT_CURRENCY, prefix='cu_')
 buro_credit_type_dum = pd.get_dummies(buro.CREDIT_TYPE, prefix='ty_')
 buro_full = pd.concat([buro, buro_credit_active_dum, buro_credit_currency_dum, buro_credit_type_dum], axis=1)
 del buro_credit_active_dum, buro_credit_currency_dum, buro_credit_type_dum
gc.collect()
print('Merge with buro avg')
 buro_full = buro_full.merge(right=avg_buro_bal.reset_index() , how='left', on='SK_ID_BUREAU', suffixes=('', '_bur_bal'))
print('Counting buro per SK_ID_CURR')
 nb_bureau_per_curr = buro_full[['SK_ID_CURR', 'SK_ID_BUREAU']].groupby('SK_ID_CURR' ).count()
buro_full['SK_ID_BUREAU'] = buro_full['SK_ID_CURR'].map(nb_bureau_per_curr['SK_ID_BUREAU'])
 print('Averaging bureau')
 avg_buro = buro_full.groupby('SK_ID_CURR' ).mean()
print(avg_buro.head())
 del buro, buro_full
gc.collect()
print('Read prev')
 prev = pd.read_csv('.. /input/previous_application.csv')
 prev_cat_features = [
f_ for f_ in prev.columns if prev[f_].dtype == 'object'
]
print('Go to dummies')
 prev_dum = pd.DataFrame()
for f_ in prev_cat_features:
prev_dum = pd.concat([prev_dum, pd.get_dummies(prev[f_], prefix=f_ ).astype(np.uint8)], axis=1)
 prev = pd.concat([prev, prev_dum], axis=1)
 del prev_dum
gc.collect()
print('Counting number of Prevs')
 nb_prev_per_curr = prev[['SK_ID_CURR', 'SK_ID_PREV']].groupby('SK_ID_CURR' ).count()
prev['SK_ID_PREV'] = prev['SK_ID_CURR'].map(nb_prev_per_curr['SK_ID_PREV'])
 print('Averaging prev')
 avg_prev = prev.groupby('SK_ID_CURR' ).mean()
print(avg_prev.head())
 del prev
gc.collect()
print('Reading POS_CASH')
 pos = pd.read_csv('.. /input/POS_CASH_balance.csv')
 print('Go to dummies')
 pos = pd.concat([pos, pd.get_dummies(pos['NAME_CONTRACT_STATUS'])], axis=1)
 print('Compute nb of prevs per curr')
 nb_prevs = pos[['SK_ID_CURR', 'SK_ID_PREV']].groupby('SK_ID_CURR' ).count()
pos['SK_ID_PREV'] = pos['SK_ID_CURR'].map(nb_prevs['SK_ID_PREV'])
 print('Go to averages')
 avg_pos = pos.groupby('SK_ID_CURR' ).mean()
del pos, nb_prevs
gc.collect()
print('Reading CC balance')
 cc_bal = pd.read_csv('.. /input/credit_card_balance.csv')
 print('Go to dummies')
 cc_bal = pd.concat([cc_bal, pd.get_dummies(cc_bal['NAME_CONTRACT_STATUS'], prefix='cc_bal_status_')], axis=1)
 nb_prevs = cc_bal[['SK_ID_CURR', 'SK_ID_PREV']].groupby('SK_ID_CURR' ).count()
cc_bal['SK_ID_PREV'] = cc_bal['SK_ID_CURR'].map(nb_prevs['SK_ID_PREV'])
 print('Compute average')
 avg_cc_bal = cc_bal.groupby('SK_ID_CURR' ).mean()
avg_cc_bal.columns = ['cc_bal_' + f_ for f_ in avg_cc_bal.columns]
del cc_bal, nb_prevs
gc.collect()
print('Reading Installments')
 inst = pd.read_csv('.. /input/installments_payments.csv')
 nb_prevs = inst[['SK_ID_CURR', 'SK_ID_PREV']].groupby('SK_ID_CURR' ).count()
inst['SK_ID_PREV'] = inst['SK_ID_CURR'].map(nb_prevs['SK_ID_PREV'])
 avg_inst = inst.groupby('SK_ID_CURR' ).mean()
avg_inst.columns = ['inst_' + f_ for f_ in avg_inst.columns]
print('Read data and test')
 data = pd.read_csv('.. /input/application_train.csv')
 test = pd.read_csv('.. /input/application_test.csv')
 print('Shapes : ', data.shape, test.shape)
 print('Read data and test')
 train = pd.read_csv('.. /input/application_train.csv')
 test = pd.read_csv('.. /input/application_test.csv')
 print('Shapes : ', train.shape, train.shape)
 categorical_feats = [
f for f in data.columns if data[f].dtype == 'object'
]
categorical_feats
for f_ in categorical_feats:
train[f_], indexer = pd.factorize(train[f_])
 test[f_] = indexer.get_indexer(test[f_])
 train = train.merge(right=avg_buro.reset_index() , how='left', on='SK_ID_CURR')
 test = test.merge(right=avg_buro.reset_index() , how='left', on='SK_ID_CURR')
 train = train.merge(right=avg_prev.reset_index() , how='left', on='SK_ID_CURR')
 test = test.merge(right=avg_prev.reset_index() , how='left', on='SK_ID_CURR')
 train = train.merge(right=avg_pos.reset_index() , how='left', on='SK_ID_CURR')
 test = test.merge(right=avg_pos.reset_index() , how='left', on='SK_ID_CURR')
 train = train.merge(right=avg_cc_bal.reset_index() , how='left', on='SK_ID_CURR')
 test = test.merge(right=avg_cc_bal.reset_index() , how='left', on='SK_ID_CURR')
 train = train.merge(right=avg_inst.reset_index() , how='left', on='SK_ID_CURR')
 test = test.merge(right=avg_inst.reset_index() , how='left', on='SK_ID_CURR')
 del avg_buro, avg_prev
gc.collect()
ID = test.SK_ID_CURR<rename_columns>
submission = pd.DataFrame()
submission['ForecastId'] = test_df['ForecastId']
submission['ConfirmedCases'] = test_df['cc_cases']
submission['Fatalities'] = test_df['ft_cases']
train.columns = train.columns.str.replace('[^A-Za-z0-9_]', '_')
 test.columns = test.columns.str.replace('[^A-Za-z0-9_]', '_' )<data_type_conversions>
submission.to_csv("submission.csv",index=False )