kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
17,896,962 | classifier = Sequential()
classifier.add(Conv2D(filters = 32, kernel_size =(3,3), activation = 'relu', input_shape =(dim,dim,3)))
classifier.add(MaxPool2D(pool_size =(2,2)))
classifier.add(Conv2D(64,(3,3),activation = 'relu'))
classifier.add(Conv2D(64,(3,3),activation = 'relu'))
classifier.add(MaxPool2D(pool_size =(2,2)))
classifier.add(Conv2D(128,(3,3),activation = 'relu'))
classifier.add(Conv2D(128,(3,3),activation = 'relu'))
classifier.add(MaxPool2D(pool_size =(2,2)))
classifier.add(Flatten())
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = num_classes , activation = 'softmax'))
classifier.compile(
optimizer = 'adam',
loss = 'categorical_crossentropy',
metrics = ['categorical_accuracy','accuracy']
)
classifier.summary()<train_model> | apps, prev = get_dataset()
apps_all = get_apps_all_with_prev_agg(apps, prev)
apps_all = get_apps_all_encoded(apps_all)
apps_all_train, apps_all_test = get_apps_all_train_test(apps_all)
clf = train_apps_all(apps_all_train ) | Home Credit Default Risk |
17,896,962 | classifier.fit_generator(train_generator, epochs = 100, steps_per_epoch = 70 )<find_best_params> | preds = clf.predict_proba(apps_all_test.drop(['SK_ID_CURR'], axis=1)) [:, 1 ]
apps_all_test['TARGET'] = preds
apps_all_test[['SK_ID_CURR', 'TARGET']].to_csv('prev_baseline_03.csv', index=False ) | Home Credit Default Risk |
17,896,962 | classes = train_generator.class_indices
print(classes )<predict_on_test> | bureau = pd.read_csv('.. /input/home-credit-default-risk/bureau.csv')
bureau_bal = pd.read_csv('.. /input/home-credit-default-risk/bureau_balance.csv' ) | Home Credit Default Risk |
17,896,962 | Y_pred = []
for idx in range(test_set.shape[0]):
img = image.load_img(path=test_set['Image'][idx],target_size=(dim,dim,3))
img = image.img_to_array(img)
test_img = img.reshape(( 1,dim,dim,3))
img_class = classifier.predict_classes(test_img)
prediction = img_class[0]
Y_pred.append(prediction )<find_best_params> | bureau_app = bureau.merge(app_train[['SK_ID_CURR', 'TARGET']], left_on='SK_ID_CURR', right_on='SK_ID_CURR', how='inner')
bureau_app.shape | Home Credit Default Risk |
17,896,962 | prediction_classes = [ inverted_classes.get(item,item)for item in Y_pred ]
print(prediction_classes )<save_to_csv> | num_columns = bureau_app.dtypes[bureau_app.dtypes != 'object'].index.tolist()
num_columns = [column for column in num_columns if column not in['SK_ID_BUREAU', 'SK_ID_CURR', 'TARGET']]
num_columns | Home Credit Default Risk |
17,896,962 | predictions = []
for idx in range(test_set.shape[0]):
predictions.append([test_set['Image'][idx].split('/')[6].split('.')[0],prediction_classes[idx]])
predictions = pd.DataFrame(predictions, columns=['ID','Country'])
predictions['ID'] = predictions['ID'].astype(int)
predictions.sort_values(by=['ID'], inplace=True)
predictions.to_csv(datetime.now().strftime("gallivanters_%Y%m%d_%H%M%S.csv"), index=False)
predictions.head(20 )<load_from_csv> | object_columns = bureau.dtypes[bureau.dtypes=='object'].index.tolist()
object_columns | Home Credit Default Risk |
17,896,962 | pd.read_csv?<load_from_csv> | show_category_by_target(bureau_app, object_columns ) | Home Credit Default Risk |
17,896,962 | fn = '.. /input/male-daan-schnell-mal-klassifizieren/train.csv'
df = pd.read_csv(fn)
df.head(5 )<load_from_csv> | def get_bureau_processed(bureau):
bureau['BUREAU_ENDDATE_FACT_DIFF'] = bureau['DAYS_CREDIT_ENDDATE'] - bureau['DAYS_ENDDATE_FACT']
bureau['BUREAU_CREDIT_FACT_DIFF'] = bureau['DAYS_CREDIT'] - bureau['DAYS_ENDDATE_FACT']
bureau['BUREAU_CREDIT_ENDDATE_DIFF'] = bureau['DAYS_CREDIT'] - bureau['DAYS_CREDIT_ENDDATE']
bureau['BUREAU_CREDIT_DEBT_RATIO']=bureau['AMT_CREDIT_SUM_DEBT']/bureau['AMT_CREDIT_SUM']
bureau['BUREAU_CREDIT_DEBT_DIFF'] = bureau['AMT_CREDIT_SUM_DEBT'] - bureau['AMT_CREDIT_SUM']
bureau['BUREAU_IS_DPD'] = bureau['CREDIT_DAY_OVERDUE'].apply(lambda x: 1 if x > 0 else 0)
bureau['BUREAU_IS_DPD_OVER120'] = bureau['CREDIT_DAY_OVERDUE'].apply(lambda x: 1 if x >120 else 0)
return bureau | Home Credit Default Risk |
17,896,962 | df = pd.read_csv(fn,index_col='Id')
df.head()<create_dataframe> | def get_bureau_day_amt_agg(bureau):
bureau_agg_dict = {
'SK_ID_BUREAU':['count'],
'DAYS_CREDIT':['min', 'max', 'mean'],
'CREDIT_DAY_OVERDUE':['min', 'max', 'mean'],
'DAYS_CREDIT_ENDDATE':['min', 'max', 'mean'],
'DAYS_ENDDATE_FACT':['min', 'max', 'mean'],
'AMT_CREDIT_MAX_OVERDUE': ['max', 'mean'],
'AMT_CREDIT_SUM': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_DEBT': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_OVERDUE': ['max', 'mean', 'sum'],
'AMT_ANNUITY': ['max', 'mean', 'sum'],
'BUREAU_ENDDATE_FACT_DIFF':['min', 'max', 'mean'],
'BUREAU_CREDIT_FACT_DIFF':['min', 'max', 'mean'],
'BUREAU_CREDIT_ENDDATE_DIFF':['min', 'max', 'mean'],
'BUREAU_CREDIT_DEBT_RATIO':['min', 'max', 'mean'],
'BUREAU_CREDIT_DEBT_DIFF':['min', 'max', 'mean'],
'BUREAU_IS_DPD':['mean', 'sum'],
'BUREAU_IS_DPD_OVER120':['mean', 'sum']
}
bureau_grp = bureau.groupby('SK_ID_CURR')
bureau_day_amt_agg = bureau_grp.agg(bureau_agg_dict)
bureau_day_amt_agg.columns = ['BUREAU_'+('_' ).join(column ).upper() for column in bureau_day_amt_agg.columns.ravel() ]
bureau_day_amt_agg = bureau_day_amt_agg.reset_index()
print('bureau_day_amt_agg shape:', bureau_day_amt_agg.shape)
return bureau_day_amt_agg | Home Credit Default Risk |
17,896,962 | data = df.values
data<prepare_x_and_y> | def get_bureau_active_agg(bureau):
cond_active = bureau['CREDIT_ACTIVE'] == 'Active'
bureau_active_grp = bureau[cond_active].groupby(['SK_ID_CURR'])
bureau_agg_dict = {
'SK_ID_BUREAU':['count'],
'DAYS_CREDIT':['min', 'max', 'mean'],
'CREDIT_DAY_OVERDUE':['min', 'max', 'mean'],
'DAYS_CREDIT_ENDDATE':['min', 'max', 'mean'],
'DAYS_ENDDATE_FACT':['min', 'max', 'mean'],
'AMT_CREDIT_MAX_OVERDUE': ['max', 'mean'],
'AMT_CREDIT_SUM': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_DEBT': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_OVERDUE': ['max', 'mean', 'sum'],
'AMT_ANNUITY': ['max', 'mean', 'sum'],
'BUREAU_ENDDATE_FACT_DIFF':['min', 'max', 'mean'],
'BUREAU_CREDIT_FACT_DIFF':['min', 'max', 'mean'],
'BUREAU_CREDIT_ENDDATE_DIFF':['min', 'max', 'mean'],
'BUREAU_CREDIT_DEBT_RATIO':['min', 'max', 'mean'],
'BUREAU_CREDIT_DEBT_DIFF':['min', 'max', 'mean'],
'BUREAU_IS_DPD':['mean', 'sum'],
'BUREAU_IS_DPD_OVER120':['mean', 'sum']
}
bureau_active_agg = bureau_active_grp.agg(bureau_agg_dict)
bureau_active_agg.columns = ['BUREAU_ACT_'+('_' ).join(column ).upper() for column in bureau_active_agg.columns.ravel() ]
bureau_active_agg = bureau_active_agg.reset_index()
print('bureau_active_agg shape:', bureau_active_agg.shape)
return bureau_active_agg | Home Credit Default Risk |
17,896,962 | nTrain=5000
Xtrain = data[:nTrain,:-1]
ytrain = data[:nTrain,-1]
Xtest = data[nTrain:,:-1]
ytest = data[nTrain:,-1]
Xtrain.shape,ytrain.shape,Xtest.shape,ytest.shape<data_type_conversions> | def get_bureau_bal_agg(bureau, bureau_bal):
bureau_bal = bureau_bal.merge(bureau[['SK_ID_CURR', 'SK_ID_BUREAU']], on='SK_ID_BUREAU', how='left')
bureau_bal['BUREAU_BAL_IS_DPD'] = bureau_bal['STATUS'].apply(lambda x: 1 if x in['1','2','3','4','5'] else 0)
bureau_bal['BUREAU_BAL_IS_DPD_OVER120'] = bureau_bal['STATUS'].apply(lambda x: 1 if x =='5' else 0)
bureau_bal_grp = bureau_bal.groupby('SK_ID_CURR')
bureau_bal_agg_dict = {
'SK_ID_CURR':['count'],
'MONTHS_BALANCE':['min', 'max', 'mean'],
'BUREAU_BAL_IS_DPD':['mean', 'sum'],
'BUREAU_BAL_IS_DPD_OVER120':['mean', 'sum']
}
bureau_bal_agg = bureau_bal_grp.agg(bureau_bal_agg_dict)
bureau_bal_agg.columns = [ 'BUREAU_BAL_'+('_' ).join(column ).upper() for column in bureau_bal_agg.columns.ravel() ]
bureau_bal_agg = bureau_bal_agg.reset_index()
print('bureau_bal_agg shape:', bureau_bal_agg.shape)
return bureau_bal_agg | Home Credit Default Risk |
17,896,962 | ytrain = ytrain.astype('int')
ytest = ytest.astype('int')
ytrain, ytrain.dtype<choose_model_class> | def get_bureau_agg(bureau, bureau_bal):
bureau = get_bureau_processed(bureau)
bureau_day_amt_agg = get_bureau_day_amt_agg(bureau)
bureau_active_agg = get_bureau_active_agg(bureau)
bureau_bal_agg = get_bureau_bal_agg(bureau, bureau_bal)
bureau_agg = bureau_day_amt_agg.merge(bureau_active_agg, on='SK_ID_CURR', how='left')
bureau_agg = bureau_agg.merge(bureau_bal_agg, on='SK_ID_CURR', how='left')
print('bureau_agg shape:', bureau_agg.shape)
return bureau_agg | Home Credit Default Risk |
17,896,962 | k=7<define_variables> | def get_apps_all_with_prev_bureau_agg(apps, prev, bureau, bureau_bal):
apps_all = get_apps_processed(apps)
prev_agg = get_prev_agg(prev)
bureau_agg = get_bureau_agg(bureau, bureau_bal)
print('prev_agg shape:', prev_agg.shape)
print('bueau_agg shape:', bureau_agg.shape)
print('apps_all before merge shape:', apps_all.shape)
apps_all = apps_all.merge(prev_agg, on='SK_ID_CURR', how='left')
apps_all = apps_all.merge(bureau_agg, on='SK_ID_CURR', how='left')
print('apps_all after merge with prev_agg, bureau_agg shape:', apps_all.shape)
return apps_all | Home Credit Default Risk |
17,896,962 | zufälliger_Index = np.random.randint(low=0,high=len(ytest))
zufälliger_Index<split> | apps_all = get_apps_all_with_prev_bureau_agg(apps, prev, bureau, bureau_bal ) | Home Credit Default Risk |
17,896,962 | Testzeile = Xtest[zufälliger_Index,:]
Testlabel = ytest[zufälliger_Index]
Testzeile,Testlabel<compute_test_metric> | apps_all = get_apps_all_encoded(apps_all)
apps_all_train, apps_all_test = get_apps_all_train_test(apps_all ) | Home Credit Default Risk |
17,896,962 | distanz = np.sqrt(((Xtrain - Testzeile)**2 ).sum(axis=1))<sort_values> | clf = train_apps_all(apps_all_train ) | Home Credit Default Risk |
17,896,962 | <sort_values><EOS> | preds = clf.predict_proba(apps_all_test.drop(['SK_ID_CURR'], axis=1)) [:, 1 ]
apps_all_test['TARGET'] = preds
apps_all_test[['SK_ID_CURR', 'TARGET']].to_csv('bureau_baseline_04.csv', index=False ) | Home Credit Default Risk |
6,039,066 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<define_search_space> | import numpy as np
import pandas as pd
import os | Home Credit Default Risk |
6,039,066 | np.argsort([1,7,4,9] )<sort_values> | app_train = pd.read_csv('.. /input/home-credit-simple-featuers/simple_features_train.csv')
app_test = pd.read_csv('.. /input/home-credit-simple-featuers/simple_features_test.csv' ) | Home Credit Default Risk |
6,039,066 | distanz[np.argsort(distanz)[:k]]
np.min(distanz )<sort_values> | sk_id = pd.read_csv('.. /input/home-credit-default-risk/application_test.csv' ) | Home Credit Default Risk |
6,039,066 | ytrain[np.argsort(distanz)[:k]]<define_search_space> | train = app_train.drop(columns = ['TARGET'])
train_labels = app_train['TARGET'] | Home Credit Default Risk |
6,039,066 | [1,0,1,0,1,1,0]<define_search_space> | X_train, X_test, y_train, y_test = train_test_split(
train, train_labels, test_size=0.2 ) | Home Credit Default Risk |
6,039,066 | np.around(np.mean(np.array([1,0,1,0,1,1,0])) )<define_search_space> | clf = LGBMClassifier(boosting_type = 'goss', n_estimators = 10000, learning_rate= 0.005134, num_leaves= 54, max_depth= 10, subsample_for_bin= 240000, reg_alpha= 0.436193, reg_lambda= 0.479169, colsample_bytree=0.508716, min_split_gain= 0.024766, subsample= 1, is_unbalance= False, silent=-1, verbose=-1)
clf.fit(X_train, y_train, eval_set=[(X_train, y_train),(X_test, y_test)],
eval_metric= 'auc', verbose= 100, early_stopping_rounds= 200)
| Home Credit Default Risk |
6,039,066 | np.median([1,0,1,0,1,1,0] )<sort_values> | Home Credit Default Risk | |
6,039,066 | <sort_values><EOS> | y_pred = clf.predict_proba(app_test, num_iteration=clf.best_iteration_)[:, 1]
submit = sk_id[['SK_ID_CURR']]
submit['TARGET'] = y_pred
submit.to_csv('sub1.csv', index = False)
| Home Credit Default Risk |
1,052,311 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<split> | train = pd.read_csv(".. /input/automation-of-feature-creation/train.csv")
test = pd.read_csv(".. /input/automation-of-feature-creation/test.csv")
tmp = pd.read_csv(".. /input/home-credit-default-risk/application_test.csv")
tmp_train = pd.read_csv(".. /input/home-credit-default-risk/application_train.csv" ) | Home Credit Default Risk |
1,052,311 | distanz[sorted_indices[:k]], sorted_indices[:k], ytrain[sorted_indices[:k]]<define_variables> | train['SK_ID_CURR'] = tmp_train['SK_ID_CURR']
test['SK_ID_CURR'] = tmp['SK_ID_CURR'] | Home Credit Default Risk |
1,052,311 | Auftretende_Trainingslabels = ytrain[sorted_indices[:k]]<define_variables> | probss = test['ProbTARGET1']
del test['ProbTARGET1']
del train['ProbTARGET1']
y = train['TARGET']
del train['TARGET'] | Home Credit Default Risk |
1,052,311 | if np.mean(Auftretende_Trainingslabels)>=0.5:
yhat = 1
else:
yhat = 0
yhat<define_search_space> | def mean_(x):
if '{' in x:
return x
if x=='Missing':
return -1
if '+inf' in x:
return float(x.replace(']','' ).replace('[','' ).split(';')[0])
if '-inf' in x:
return float(x.replace(']','' ).replace('[','' ).split(';')[1])
l = x.replace(']','' ).replace('[','' ).split(';')
return(float(l[0])+float(l[1])) /2
dictionnary = {}
for i in train.columns:
if train[i].dtype!='object':
continue
try :
for j in train[i].unique() :
dictionnary[j] = mean_(j)
except :
continue
for i in train.columns:
if train[i].dtype!='object':
continue
train[i] = train[i].map(dictionnary ) | Home Credit Default Risk |
1,052,311 | print('Häufigstes Label in [1,0,1,0,0]:',np.argmax(np.bincount([1,0,1,0,0])))
print('Häufigstes Label in [1,0,1,0,1]:',np.argmax(np.bincount([1,0,1,0,1])) )<define_search_space> | for i in test.columns:
if test[i].dtype!='object':
continue
try :
for j in test[i].unique() :
dictionnary[j] = mean(j)
except :
continue
for i in test.columns:
if test[i].dtype!='object':
continue
test[i] = test[i].map(dictionnary ) | Home Credit Default Risk |
1,052,311 | np.bincount([0,3,3,2,1,1] )<define_search_space> | to_dummy =[]
for i in test.columns:
if test[i].dtype==object:
try :
test[i]=test[i].astype(float)
except :
continue
if len(test[i].unique())<5:
to_dummy.append(i)
else :
del test[i]
test = pd.get_dummies(test,columns=to_dummy)
to_dummy =[]
for i in train.columns:
if train[i].dtype==object:
try :
train[i]=train[i].astype(float)
except :
continue
if len(train[i].unique())<5:
to_dummy.append(i)
else :
del train[i]
train = pd.get_dummies(train,columns=to_dummy)
| Home Credit Default Risk |
1,052,311 | np.bincount([0,3,3,3,2,2,2,2,2,2,2,1,1,4,4,4,4,5,6] )<define_search_space> | train.fillna(0,inplace=True)
test.fillna(0,inplace=True ) | Home Credit Default Risk |
1,052,311 | np.argmax(np.bincount([0,3,3,3,2,2,2,2,2,2,2,1,1,4,4,4,4,5,6]))<statistical_test> | col_dict = {}
for i in train.columns:
if '>' in i:
col_dict[i] = i.replace('>','' ).replace('<','')
train.rename(columns=col_dict,inplace = True)
test.rename(columns=col_dict, inplace =True ) | Home Credit Default Risk |
1,052,311 | def kNN_Vorhersage(Xtrain,Testzeile,k):
distanz =(((Xtrain - Testzeile)**2 ).sum(axis=1)) **0.5
sorted_indices = np.argsort(distanz)
Auftretende_Trainingslabels = ytrain[sorted_indices[:k]]
return np.argmax(np.bincount(Auftretende_Trainingslabels))
zufälliger_Index = np.random.randint(low=0,high=len(ytest))
Testzeile = Xtest[zufälliger_Index,:]
yhat = kNN_Vorhersage(Xtrain,Testzeile,k)
print(f'Vorhersage für {Testzeile}:{yhat}' )<choose_model_class> | for i in train.columns:
if train[i].dtype==object:
del train[i]
for i in test.columns:
if test[i].dtype==object:
del test[i]
for i in train.columns:
if i not in test.columns:
del train[i]
for j in test.columns:
if j not in train.columns:
del test[j] | Home Credit Default Risk |
1,052,311 | clf = DecisionTreeClassifier(max_depth=5)
<load_from_csv> | def train_model(data_, test_, y_, folds_):
oof_preds = np.zeros(data_.shape[0])
sub_preds = np.zeros(test_.shape[0])
feature_importance_df = pd.DataFrame()
feats = [f for f in data_.columns if f not in ['SK_ID_CURR']]
for n_fold,(trn_idx, val_idx)in enumerate(folds_.split(data_)) :
trn_x, trn_y = data_[feats].iloc[trn_idx], y_.iloc[trn_idx]
val_x, val_y = data_[feats].iloc[val_idx], y_.iloc[val_idx]
clf = LGBMClassifier(
n_estimators=6000,
learning_rate=0.02,
num_leaves=40,
colsample_bytree=.8,
subsample=.8,
max_depth=12,
reg_alpha=.1,
reg_lambda=.1,
min_split_gain=.008,
min_child_weight=2,
min_child_samples=35,
silent=-1,
verbose=-1,
)
clf.fit(trn_x, trn_y,
eval_set= [(trn_x, trn_y),(val_x, val_y)],
eval_metric='auc', verbose=500, early_stopping_rounds=300
)
oof_preds[val_idx] = clf.predict_proba(val_x, num_iteration=clf.best_iteration_)[:, 1]
sub_preds += clf.predict_proba(test_[feats], num_iteration=clf.best_iteration_)[:, 1] / folds_.n_splits
fold_importance_df = pd.DataFrame()
fold_importance_df["feature"] = feats
fold_importance_df["importance"] = clf.feature_importances_
fold_importance_df["fold"] = n_fold + 1
feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)
print('Fold %2d AUC : %.6f' %(n_fold + 1, roc_auc_score(val_y, oof_preds[val_idx])))
del clf, trn_x, trn_y, val_x, val_y
gc.collect()
print('Full AUC score %.6f' % roc_auc_score(y, oof_preds))
test_['TARGET'] = sub_preds
return oof_preds, test_[['SK_ID_CURR', 'TARGET']], feature_importance_df
def display_importances(feature_importance_df_):
cols = feature_importance_df_[["feature", "importance"]].groupby("feature" ).mean().sort_values(
by="importance", ascending=False)[:50].index
best_features = feature_importance_df_.loc[feature_importance_df_.feature.isin(cols)]
plt.figure(figsize=(8,10))
sns.barplot(x="importance", y="feature",
data=best_features.sort_values(by="importance", ascending=False))
plt.title('LightGBM Features(avg over folds)')
plt.savefig('lgbm_importances.png')
def display_roc_curve(y_, oof_preds_, folds_idx_):
plt.figure(figsize=(6,6))
scores = []
for n_fold,(_, val_idx)in enumerate(folds_idx_):
fpr, tpr, thresholds = roc_curve(y_.iloc[val_idx], oof_preds_[val_idx])
score = roc_auc_score(y_.iloc[val_idx], oof_preds_[val_idx])
scores.append(score)
plt.plot(fpr, tpr, lw=1, alpha=0.3, label='ROC fold %d(AUC = %0.4f)' %(n_fold + 1, score))
plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', label='Luck', alpha=.8)
fpr, tpr, thresholds = roc_curve(y_, oof_preds_)
score = roc_auc_score(y_, oof_preds_)
plt.plot(fpr, tpr, color='b',
label='Avg ROC(AUC = %0.4f $\pm$ %0.4f)' %(score, np.std(scores)) ,
lw=2, alpha=.8)
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('LightGBM ROC Curve')
plt.legend(loc="lower right")
plt.savefig('roc_curve.png')
def display_precision_recall(y_, oof_preds_, folds_idx_):
plt.figure(figsize=(6,6))
scores = []
for n_fold,(_, val_idx)in enumerate(folds_idx_):
fpr, tpr, thresholds = roc_curve(y_.iloc[val_idx], oof_preds_[val_idx])
score = average_precision_score(y_.iloc[val_idx], oof_preds_[val_idx])
scores.append(score)
plt.plot(fpr, tpr, lw=1, alpha=0.3, label='AP fold %d(AUC = %0.4f)' %(n_fold + 1, score))
precision, recall, thresholds = precision_recall_curve(y_, oof_preds_)
score = average_precision_score(y_, oof_preds_)
plt.plot(precision, recall, color='b',
label='Avg ROC(AUC = %0.4f $\pm$ %0.4f)' %(score, np.std(scores)) ,
lw=2, alpha=.8)
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('LightGBM Recall / Precision')
plt.legend(loc="best")
plt.savefig('recall_precision_curve.png' ) | Home Credit Default Risk |
1,052,311 | <prepare_x_and_y><EOS> | if __name__ == '__main__':
gc.enable()
folds = KFold(n_splits=5, shuffle=True, random_state=123)
oof_preds, test_preds, importances = train_model(train, test, y, folds)
test_preds.to_csv('first_automated_submission.csv', index=False)
folds_idx = [(trn_idx, val_idx)for trn_idx, val_idx in folds.split(train)]
display_importances(feature_importance_df_=importances)
display_roc_curve(y_=y, oof_preds_=oof_preds, folds_idx_=folds_idx)
display_precision_recall(y_=y, oof_preds_=oof_preds, folds_idx_=folds_idx ) | Home Credit Default Risk |
1,446,148 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<train_model> | warnings.simplefilter(action='ignore', category=FutureWarning ) | Home Credit Default Risk |
1,446,148 | clf.fit(Xtrain,ytrain )<predict_on_test> | def one_hot_encoder(df, nan_as_category = True):
original_columns = list(df.columns)
categorical_columns = [col for col in df.columns if df[col].dtype == 'object']
df = pd.get_dummies(df, columns= categorical_columns, dummy_na= nan_as_category)
new_columns = [c for c in df.columns if c not in original_columns]
return df, new_columns
| Home Credit Default Risk |
1,446,148 | yhat = clf.predict(Xtest )<save_to_csv> | def application_train_test(num_rows = None, nan_as_category = False):
df = pd.read_csv('.. /input/application_train.csv', nrows= num_rows)
test_df = pd.read_csv('.. /input/application_test.csv', nrows= num_rows)
print("Train samples: {}, test samples: {}".format(len(df), len(test_df)))
df = df.append(test_df ).reset_index()
df = df[df['CODE_GENDER'] != 'XNA']
docs = [_f for _f in df.columns if 'FLAG_DOC' in _f]
live = [_f for _f in df.columns if('FLAG_' in _f)&('FLAG_DOC' not in _f)&('_FLAG_' not in _f)]
df['DAYS_EMPLOYED'].replace(365243, np.nan, inplace= True)
inc_by_org = df[['AMT_INCOME_TOTAL', 'ORGANIZATION_TYPE']].groupby('ORGANIZATION_TYPE' ).median() ['AMT_INCOME_TOTAL']
df['NEW_CREDIT_TO_ANNUITY_RATIO'] = df['AMT_CREDIT'] / df['AMT_ANNUITY']
df['NEW_CREDIT_TO_GOODS_RATIO'] = df['AMT_CREDIT'] / df['AMT_GOODS_PRICE']
df['NEW_DOC_IND_KURT'] = df[docs].kurtosis(axis=1)
df['NEW_LIVE_IND_SUM'] = df[live].sum(axis=1)
df['NEW_INC_PER_CHLD'] = df['AMT_INCOME_TOTAL'] /(1 + df['CNT_CHILDREN'])
df['NEW_INC_BY_ORG'] = df['ORGANIZATION_TYPE'].map(inc_by_org)
df['NEW_EMPLOY_TO_BIRTH_RATIO'] = df['DAYS_EMPLOYED'] / df['DAYS_BIRTH']
df['NEW_ANNUITY_TO_INCOME_RATIO'] = df['AMT_ANNUITY'] /(1 + df['AMT_INCOME_TOTAL'])
df['NEW_SOURCES_PROD'] = df['EXT_SOURCE_1'] * df['EXT_SOURCE_2'] * df['EXT_SOURCE_3']
df['NEW_EXT_SOURCES_MEAN'] = df[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].mean(axis=1)
df['NEW_SCORES_STD'] = df[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].std(axis=1)
df['NEW_SCORES_STD'] = df['NEW_SCORES_STD'].fillna(df['NEW_SCORES_STD'].mean())
df['NEW_CAR_TO_BIRTH_RATIO'] = df['OWN_CAR_AGE'] / df['DAYS_BIRTH']
df['NEW_CAR_TO_EMPLOY_RATIO'] = df['OWN_CAR_AGE'] / df['DAYS_EMPLOYED']
df['NEW_PHONE_TO_BIRTH_RATIO'] = df['DAYS_LAST_PHONE_CHANGE'] / df['DAYS_BIRTH']
df['NEW_PHONE_TO_BIRTH_RATIO_EMPLOYER'] = df['DAYS_LAST_PHONE_CHANGE'] / df['DAYS_EMPLOYED']
df['NEW_CREDIT_TO_INCOME_RATIO'] = df['AMT_CREDIT'] / df['AMT_INCOME_TOTAL']
for bin_feature in ['CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY']:
df[bin_feature], uniques = pd.factorize(df[bin_feature])
df, cat_cols = one_hot_encoder(df, nan_as_category)
dropcolum=['FLAG_DOCUMENT_2','FLAG_DOCUMENT_4',
'FLAG_DOCUMENT_5','FLAG_DOCUMENT_6','FLAG_DOCUMENT_7',
'FLAG_DOCUMENT_8','FLAG_DOCUMENT_9','FLAG_DOCUMENT_10',
'FLAG_DOCUMENT_11','FLAG_DOCUMENT_12','FLAG_DOCUMENT_13',
'FLAG_DOCUMENT_14','FLAG_DOCUMENT_15','FLAG_DOCUMENT_16',
'FLAG_DOCUMENT_17','FLAG_DOCUMENT_18','FLAG_DOCUMENT_19',
'FLAG_DOCUMENT_20','FLAG_DOCUMENT_21']
df= df.drop(dropcolum,axis=1)
del test_df
gc.collect()
return df | Home Credit Default Risk |
1,446,148 | ser = pd.Series(yhat,name='y' ).astype('int')
ser.index.name='Id'
ser.to_csv('Submission.csv',header=True)
!head Submission.csv<import_modules> | def bureau_and_balance(num_rows = None, nan_as_category = True):
bureau = pd.read_csv('.. /input/bureau.csv', nrows = num_rows)
bb = pd.read_csv('.. /input/bureau_balance.csv', nrows = num_rows)
bb, bb_cat = one_hot_encoder(bb, nan_as_category)
bureau, bureau_cat = one_hot_encoder(bureau, nan_as_category)
bb_aggregations = {'MONTHS_BALANCE': ['min', 'max', 'size']}
for col in bb_cat:
bb_aggregations[col] = ['mean']
bb_agg = bb.groupby('SK_ID_BUREAU' ).agg(bb_aggregations)
bb_agg.columns = pd.Index([e[0] + "_" + e[1].upper() for e in bb_agg.columns.tolist() ])
bureau = bureau.join(bb_agg, how='left', on='SK_ID_BUREAU')
bureau.drop(['SK_ID_BUREAU'], axis=1, inplace= True)
del bb, bb_agg
gc.collect()
num_aggregations = {
'DAYS_CREDIT': [ 'mean', 'var'],
'DAYS_CREDIT_ENDDATE': [ 'mean'],
'DAYS_CREDIT_UPDATE': ['mean'],
'CREDIT_DAY_OVERDUE': ['mean'],
'AMT_CREDIT_MAX_OVERDUE': ['mean'],
'AMT_CREDIT_SUM': [ 'mean', 'sum'],
'AMT_CREDIT_SUM_DEBT': [ 'mean', 'sum'],
'AMT_CREDIT_SUM_OVERDUE': ['mean'],
'AMT_CREDIT_SUM_LIMIT': ['mean', 'sum'],
'AMT_ANNUITY': ['max', 'mean'],
'CNT_CREDIT_PROLONG': ['sum'],
'MONTHS_BALANCE_MIN': ['min'],
'MONTHS_BALANCE_MAX': ['max'],
'MONTHS_BALANCE_SIZE': ['mean', 'sum']
}
cat_aggregations = {}
for cat in bureau_cat: cat_aggregations[cat] = ['mean']
for cat in bb_cat: cat_aggregations[cat + "_MEAN"] = ['mean']
bureau_agg = bureau.groupby('SK_ID_CURR' ).agg({**num_aggregations, **cat_aggregations})
bureau_agg.columns = pd.Index(['BURO_' + e[0] + "_" + e[1].upper() for e in bureau_agg.columns.tolist() ])
active = bureau[bureau['CREDIT_ACTIVE_Active'] == 1]
active_agg = active.groupby('SK_ID_CURR' ).agg(num_aggregations)
active_agg.columns = pd.Index(['ACTIVE_' + e[0] + "_" + e[1].upper() for e in active_agg.columns.tolist() ])
bureau_agg = bureau_agg.join(active_agg, how='left', on='SK_ID_CURR')
del active, active_agg
gc.collect()
closed = bureau[bureau['CREDIT_ACTIVE_Closed'] == 1]
closed_agg = closed.groupby('SK_ID_CURR' ).agg(num_aggregations)
closed_agg.columns = pd.Index(['CLOSED_' + e[0] + "_" + e[1].upper() for e in closed_agg.columns.tolist() ])
bureau_agg = bureau_agg.join(closed_agg, how='left', on='SK_ID_CURR')
del closed, closed_agg, bureau
gc.collect()
return bureau_agg | Home Credit Default Risk |
1,446,148 | import numpy as np
import pandas as pd
import keras as ks
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import tqdm
from keras.models import Sequential, Model, Input
from keras.layers import Activation, Flatten, Dense, Dropout, ZeroPadding2D, Conv2D, MaxPool2D, BatchNormalization, GlobalAveragePooling2D, Average
from keras.optimizers import SGD, RMSprop, Adam
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from keras.utils import to_categorical
import os
import math
<define_variables> | def previous_applications(num_rows = None, nan_as_category = True):
prev = pd.read_csv('.. /input/previous_application.csv', nrows = num_rows)
prev, cat_cols = one_hot_encoder(prev, nan_as_category= True)
prev['DAYS_FIRST_DRAWING'].replace(365243, np.nan, inplace= True)
prev['DAYS_FIRST_DUE'].replace(365243, np.nan, inplace= True)
prev['DAYS_LAST_DUE_1ST_VERSION'].replace(365243, np.nan, inplace= True)
prev['DAYS_LAST_DUE'].replace(365243, np.nan, inplace= True)
prev['DAYS_TERMINATION'].replace(365243, np.nan, inplace= True)
prev['APP_CREDIT_PERC'] = prev['AMT_APPLICATION'] / prev['AMT_CREDIT']
num_aggregations = {
'AMT_ANNUITY': [ 'max', 'mean'],
'AMT_APPLICATION': [ 'max','mean'],
'AMT_CREDIT': [ 'max', 'mean'],
'APP_CREDIT_PERC': [ 'max', 'mean'],
'AMT_DOWN_PAYMENT': [ 'max', 'mean'],
'AMT_GOODS_PRICE': [ 'max', 'mean'],
'HOUR_APPR_PROCESS_START': [ 'max', 'mean'],
'RATE_DOWN_PAYMENT': [ 'max', 'mean'],
'DAYS_DECISION': [ 'max', 'mean'],
'CNT_PAYMENT': ['mean', 'sum'],
}
cat_aggregations = {}
for cat in cat_cols:
cat_aggregations[cat] = ['mean']
prev_agg = prev.groupby('SK_ID_CURR' ).agg({**num_aggregations, **cat_aggregations})
prev_agg.columns = pd.Index(['PREV_' + e[0] + "_" + e[1].upper() for e in prev_agg.columns.tolist() ])
approved = prev[prev['NAME_CONTRACT_STATUS_Approved'] == 1]
approved_agg = approved.groupby('SK_ID_CURR' ).agg(num_aggregations)
approved_agg.columns = pd.Index(['APPROVED_' + e[0] + "_" + e[1].upper() for e in approved_agg.columns.tolist() ])
prev_agg = prev_agg.join(approved_agg, how='left', on='SK_ID_CURR')
refused = prev[prev['NAME_CONTRACT_STATUS_Refused'] == 1]
refused_agg = refused.groupby('SK_ID_CURR' ).agg(num_aggregations)
refused_agg.columns = pd.Index(['REFUSED_' + e[0] + "_" + e[1].upper() for e in refused_agg.columns.tolist() ])
prev_agg = prev_agg.join(refused_agg, how='left', on='SK_ID_CURR')
del refused, refused_agg, approved, approved_agg, prev
gc.collect()
return prev_agg | Home Credit Default Risk |
1,446,148 | CATEGORIES = ['airplane','car','cat','dog','flower','fruit','motorbike','person']
IMG_WIDTH = 100
IMG_HEIGHT = 100
TRAIN_PATH = '.. /input/natural_images/natural_images/'
TEST_PATH = '.. /input/evaluate/evaluate/'<define_variables> | def pos_cash(num_rows = None, nan_as_category = True):
pos = pd.read_csv('.. /input/POS_CASH_balance.csv', nrows = num_rows)
pos, cat_cols = one_hot_encoder(pos, nan_as_category= True)
aggregations = {
'MONTHS_BALANCE': ['max', 'mean', 'size'],
'SK_DPD': ['max', 'mean'],
'SK_DPD_DEF': ['max', 'mean']
}
for cat in cat_cols:
aggregations[cat] = ['mean']
pos_agg = pos.groupby('SK_ID_CURR' ).agg(aggregations)
pos_agg.columns = pd.Index(['POS_' + e[0] + "_" + e[1].upper() for e in pos_agg.columns.tolist() ])
pos_agg['POS_COUNT'] = pos.groupby('SK_ID_CURR' ).size()
del pos
gc.collect()
return pos_agg | Home Credit Default Risk |
1,446,148 | folders = os.listdir(TRAIN_PATH)
images = []
for folder in folders:
files = os.listdir(TRAIN_PATH + folder)
images += [(folder, file, folder + '/' + file)for file in files]
image_locs = pd.DataFrame(images, columns=('class','filename','file_loc'))
display(image_locs.head(10))
display(image_locs.shape )<define_variables> | def installments_payments(num_rows = None, nan_as_category = True):
ins = pd.read_csv('.. /input/installments_payments.csv', nrows = num_rows)
ins, cat_cols = one_hot_encoder(ins, nan_as_category= True)
ins['PAYMENT_PERC'] = ins['AMT_PAYMENT'] / ins['AMT_INSTALMENT']
ins['PAYMENT_DIFF'] = ins['AMT_INSTALMENT'] - ins['AMT_PAYMENT']
ins['DPD'] = ins['DAYS_ENTRY_PAYMENT'] - ins['DAYS_INSTALMENT']
ins['DBD'] = ins['DAYS_INSTALMENT'] - ins['DAYS_ENTRY_PAYMENT']
ins['DPD'] = ins['DPD'].apply(lambda x: x if x > 0 else 0)
ins['DBD'] = ins['DBD'].apply(lambda x: x if x > 0 else 0)
aggregations = {
'NUM_INSTALMENT_VERSION': ['nunique'],
'DPD': ['max', 'mean', 'sum','min','std' ],
'DBD': ['max', 'mean', 'sum','min','std'],
'PAYMENT_PERC': [ 'max','mean', 'var','min','std'],
'PAYMENT_DIFF': [ 'max','mean', 'var','min','std'],
'AMT_INSTALMENT': ['max', 'mean', 'sum','min','std'],
'AMT_PAYMENT': ['min', 'max', 'mean', 'sum','std'],
'DAYS_ENTRY_PAYMENT': ['max', 'mean', 'sum','std']
}
for cat in cat_cols:
aggregations[cat] = ['mean']
ins_agg = ins.groupby('SK_ID_CURR' ).agg(aggregations)
ins_agg.columns = pd.Index(['INSTAL_' + e[0] + "_" + e[1].upper() for e in ins_agg.columns.tolist() ])
ins_agg['INSTAL_COUNT'] = ins.groupby('SK_ID_CURR' ).size()
del ins
gc.collect()
return ins_agg | Home Credit Default Risk |
1,446,148 | row_count = len(image_locs_shuffled.index)
val_split = 0.1
train_split = 1 - val_split
train_image_locs = image_locs_shuffled[:math.floor(train_split * row_count)]
val_image_locs = image_locs_shuffled[-math.ceil(val_split * row_count):]
display(train_image_locs.shape)
display(val_image_locs.shape )<choose_model_class> | def credit_card_balance(num_rows = None, nan_as_category = True):
cc = pd.read_csv('.. /input/credit_card_balance.csv', nrows = num_rows)
cc, cat_cols = one_hot_encoder(cc, nan_as_category= True)
cc.drop(['SK_ID_PREV'], axis= 1, inplace = True)
cc_agg = cc.groupby('SK_ID_CURR' ).agg([ 'max', 'mean', 'sum', 'var'])
cc_agg.columns = pd.Index(['CC_' + e[0] + "_" + e[1].upper() for e in cc_agg.columns.tolist() ])
cc_agg['CC_COUNT'] = cc.groupby('SK_ID_CURR' ).size()
del cc
gc.collect()
return cc_agg
| Home Credit Default Risk |
1,446,148 | train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True
)
test_datagen = ImageDataGenerator(
rescale=1./255
)<define_variables> | def kfold_lightgbm(df, num_folds, stratified = False, debug= False):
train_df = df[df['TARGET'].notnull() ]
test_df = df[df['TARGET'].isnull() ]
print("Starting LightGBM.Train shape: {}, test shape: {}".format(train_df.shape, test_df.shape))
del df
gc.collect()
if stratified:
folds = StratifiedKFold(n_splits= num_folds, shuffle=True, random_state=47)
else:
folds = KFold(n_splits= num_folds, shuffle=True, random_state=47)
oof_preds = np.zeros(train_df.shape[0])
sub_preds = np.zeros(test_df.shape[0])
feature_importance_df = pd.DataFrame()
feats = [f for f in train_df.columns if f not in ['TARGET','SK_ID_CURR','SK_ID_BUREAU','SK_ID_PREV','index']]
for n_fold,(train_idx, valid_idx)in enumerate(folds.split(train_df[feats], train_df['TARGET'])) :
train_x, train_y = train_df[feats].iloc[train_idx], train_df['TARGET'].iloc[train_idx]
valid_x, valid_y = train_df[feats].iloc[valid_idx], train_df['TARGET'].iloc[valid_idx]
clf = LGBMClassifier(
nthread=4,
n_estimators=10000,
learning_rate=0.02,
num_leaves=32,
colsample_bytree=0.9497036,
subsample=0.8715623,
max_depth=8,
reg_alpha=0.04,
reg_lambda=0.073,
min_split_gain=0.0222415,
min_child_weight=40,
silent=-1,
verbose=-1,
)
clf.fit(train_x, train_y, eval_set=[(train_x, train_y),(valid_x, valid_y)],
eval_metric= 'auc', verbose= 1000, early_stopping_rounds= 200)
oof_preds[valid_idx] = clf.predict_proba(valid_x, num_iteration=clf.best_iteration_)[:, 1]
sub_preds += clf.predict_proba(test_df[feats], num_iteration=clf.best_iteration_)[:, 1] / folds.n_splits
fold_importance_df = pd.DataFrame()
fold_importance_df["feature"] = feats
fold_importance_df["importance"] = clf.feature_importances_
fold_importance_df["fold"] = n_fold + 1
feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)
print('Fold %2d AUC : %.6f' %(n_fold + 1, roc_auc_score(valid_y, oof_preds[valid_idx])))
del clf, train_x, train_y, valid_x, valid_y
gc.collect()
print('Full AUC score %.6f' % roc_auc_score(train_df['TARGET'], oof_preds))
if not debug:
test_df['TARGET'] = sub_preds
test_df[['SK_ID_CURR', 'TARGET']].to_csv(submission_file_name, index= False)
display_importances(feature_importance_df)
return feature_importance_df | Home Credit Default Risk |
1,446,148 | train_generator = train_datagen.flow_from_dataframe(
train_image_locs,
directory=TRAIN_PATH,
x_col='file_loc',
target_size=(IMG_WIDTH, IMG_HEIGHT)
)
val_generator = test_datagen.flow_from_dataframe(
val_image_locs,
directory=TRAIN_PATH,
x_col='file_loc',
target_size=(IMG_WIDTH, IMG_HEIGHT),
shuffle=False
)
test_generator = test_datagen.flow_from_directory(
directory='.. /input/evaluate/',
target_size=(IMG_WIDTH, IMG_HEIGHT),
class_mode=None,
batch_size=1,
shuffle=False
)<choose_model_class> | def main(debug = False):
num_rows = 10000 if debug else None
df = application_train_test(num_rows)
with timer("Process bureau and bureau_balance"):
bureau = bureau_and_balance(num_rows)
print("Bureau df shape:", bureau.shape)
df = df.join(bureau, how='left', on='SK_ID_CURR')
del bureau
gc.collect()
with timer("Process previous_applications"):
prev = previous_applications(num_rows)
print("Previous applications df shape:", prev.shape)
df = df.join(prev, how='left', on='SK_ID_CURR')
del prev
gc.collect()
with timer("Process POS-CASH balance"):
pos = pos_cash(num_rows)
print("Pos-cash balance df shape:", pos.shape)
df = df.join(pos, how='left', on='SK_ID_CURR')
del pos
gc.collect()
with timer("Process installments payments"):
ins = installments_payments(num_rows)
print("Installments payments df shape:", ins.shape)
df = df.join(ins, how='left', on='SK_ID_CURR')
del ins
gc.collect()
with timer("Process credit card balance"):
cc = credit_card_balance(num_rows)
print("Credit card balance df shape:", cc.shape)
df = df.join(cc, how='left', on='SK_ID_CURR')
del cc
gc.collect()
with timer("Run LightGBM with kfold"):
feat_importance = kfold_lightgbm(df, num_folds= 7, stratified= False, debug= debug ) | Home Credit Default Risk |
1,446,148 | def build_model(weights_path=None):
model = Sequential()
model.add(Conv2D(32,(3,3), activation='relu', padding='same', input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)))
model.add(Conv2D(32,(3,3), activation ='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2,2), strides=2))
model.add(Conv2D(64,(3,3), activation='relu', padding='same'))
model.add(Conv2D(64,(3,3), activation ='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2,2), strides=2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(len(CATEGORIES), activation='softmax'))
if weights_path is not None:
model.load_weights(weights_path)
model.compile(Adam() , loss='categorical_crossentropy', metrics=['categorical_accuracy'])
return model<choose_model_class> | if __name__ == "__main__":
submission_file_name = "lightgbm.csv"
with timer("Full model run"):
main() | Home Credit Default Risk |
1,471,481 | def build_model_2(weights_path=None):
model = Sequential()
model.add(Conv2D(16,(3,3), activation='relu', padding='same', input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)))
model.add(Conv2D(16,(3,3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2,2), strides=2))
model.add(Dropout(0.1))
model.add(Conv2D(32,(3,3), activation='relu', padding='same'))
model.add(Conv2D(32,(3,3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2,2), strides=2))
model.add(Dropout(0.1))
model.add(Conv2D(64,(3,3), activation='relu', padding='same'))
model.add(Conv2D(64,(3,3), activation='relu', padding='same'))
model.add(Conv2D(64,(3,3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2,2), strides=2))
model.add(Dropout(0.1))
model.add(Conv2D(len(CATEGORIES),(1, 1)))
model.add(GlobalAveragePooling2D())
model.add(Activation('softmax'))
if weights_path is not None:
model.load_weights(weights_path)
model.compile(Adam() , loss='categorical_crossentropy', metrics=['categorical_accuracy'])
return model<choose_model_class> | df = pd.read_pickle('.. /input/save-dromosys-features/df.pkl.gz')
print("Raw shape: ", df.shape)
y = df['TARGET']
feats = [f for f in df.columns if f not in ['TARGET','SK_ID_CURR','SK_ID_BUREAU','SK_ID_PREV','index']]
X = df[feats]
print("X shape: ", X.shape, " y shape:", y.shape)
print("
Preparing data...")
X = X.fillna(X.mean() ).clip(-1e11,1e11 ) | Home Credit Default Risk |
1,471,481 | def build_model_3(weights_path=None):
model = Sequential()
model.add(Conv2D(8,(3,3), activation='relu', padding='same', input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2,2), strides=2))
model.add(Dropout(0.1))
model.add(Conv2D(16,(3,3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2,2), strides=2))
model.add(Dropout(0.1))
model.add(Conv2D(32,(3,3), activation='relu', padding='same'))
model.add(Conv2D(32,(3,3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2,2), strides=2))
model.add(Dropout(0.1))
model.add(Dense(64, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(len(CATEGORIES), activation='softmax'))
if weights_path is not None:
model.load_weights(weights_path)
model.compile(Adam() , loss='categorical_crossentropy', metrics=['categorical_accuracy'])
return model<split> | def rank_gauss(x):
N = x.shape[0]
temp = x.argsort()
rank_x = temp.argsort() / N
rank_x -= rank_x.mean()
rank_x *= 2
efi_x = erfinv(rank_x)
efi_x -= efi_x.mean()
return efi_x | Home Credit Default Risk |
1,471,481 | for(i, final_model)in ensemble_final_models:
display(final_model.evaluate_generator(generator=val_generator, steps=val_steps))<predict_on_test> | for i in X.columns:
X[i] = rank_gauss(X[i].values ) | Home Credit Default Risk |
1,471,481 | for(i, final_model)in ensemble_final_models:
val_generator.reset()
val_predictions = final_model.predict_generator(
val_generator,
steps=val_steps,
verbose=1
)
display(val_predictions.shape)
val_predictions_labels = np.argmax(val_predictions, axis=1)
val_true_labels = val_generator.classes[:val_predictions.shape[0]]
display(confusion_matrix(val_true_labels, val_predictions_labels))<choose_model_class> | training = y.notnull()
testing = y.isnull()
X_train = X[training].values
X_test = X[testing].values
y_train = np.array(y[training])
print(X_train.shape, X_test.shape, y_train.shape)
gc.collect() | Home Credit Default Risk |
1,471,481 | model_input = Input(shape=(IMG_WIDTH, IMG_HEIGHT, 3))
yModels=[m(model_input)for i, m in ensemble_final_models]
overall_model = Model(
model_input,
Average()(yModels),
name='ensemble'
)<predict_on_test> | class roc_callback(Callback):
def __init__(self,training_data,validation_data):
self.x = training_data[0]
self.y = training_data[1]
self.x_val = validation_data[0]
self.y_val = validation_data[1]
def on_train_begin(self, logs={}):
return
def on_train_end(self, logs={}):
return
def on_epoch_begin(self, epoch, logs={}):
return
def on_epoch_end(self, epoch, logs={}):
y_pred = self.model.predict(self.x)
roc = roc_auc_score(self.y, y_pred)
y_pred_val = self.model.predict(self.x_val)
roc_val = roc_auc_score(self.y_val, y_pred_val)
print('\rroc-auc: %s - roc-auc_val: %s' %(str(round(roc,4)) ,str(round(roc_val,4))),end=100*' '+'
')
return
def on_batch_begin(self, batch, logs={}):
return
def on_batch_end(self, batch, logs={}):
return | Home Credit Default Risk |
1,471,481 | test_generator.reset()
predictions = overall_model.predict_generator(
test_generator,
steps=test_generator.n,
verbose=1
)
predicted_class_indices = np.argmax(predictions, axis=1)
display(predicted_class_indices )<define_variables> | folds = KFold(n_splits=10, shuffle=True, random_state=42)
sub_preds = np.zeros(X_test.shape[0])
for n_fold,(trn_idx, val_idx)in enumerate(folds.split(X_train)) :
trn_x, trn_y = X_train[trn_idx], y_train[trn_idx]
val_x, val_y = X_train[val_idx], y_train[val_idx]
print('Setting up neural network...')
nn = Sequential()
nn.add(Dense(units = 400 , kernel_initializer = 'normal', input_dim = 718))
nn.add(PReLU())
nn.add(Dropout (.3))
nn.add(Dense(units = 160 , kernel_initializer = 'normal'))
nn.add(PReLU())
nn.add(BatchNormalization())
nn.add(Dropout (.3))
nn.add(Dense(units = 64 , kernel_initializer = 'normal'))
nn.add(PReLU())
nn.add(BatchNormalization())
nn.add(Dropout (.3))
nn.add(Dense(units = 26, kernel_initializer = 'normal'))
nn.add(PReLU())
nn.add(BatchNormalization())
nn.add(Dropout (.3))
nn.add(Dense(units = 12, kernel_initializer = 'normal'))
nn.add(PReLU())
nn.add(BatchNormalization())
nn.add(Dropout (.3))
nn.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))
nn.compile(loss='binary_crossentropy', optimizer='adam')
print('Fitting neural network...')
nn.fit(trn_x, trn_y, validation_data =(val_x, val_y), epochs=10, verbose=2,
callbacks=[roc_callback(training_data=(trn_x, trn_y),validation_data=(val_x, val_y)) ])
print('Predicting...')
sub_preds += nn.predict(X_test ).flatten().clip(0,1)/ folds.n_splits
gc.collect() | Home Credit Default Risk |
1,471,481 | <define_variables><EOS> | print('Saving results...')
sub = pd.DataFrame()
sub['SK_ID_CURR'] = df[testing]['SK_ID_CURR']
sub['TARGET'] = sub_preds
sub[['SK_ID_CURR', 'TARGET']].to_csv('sub_nn.csv', index= False)
print(sub.head() ) | Home Credit Default Risk |
1,462,214 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<save_to_csv> | import gc
import time
import numpy as np
import pandas as pd
from contextlib import contextmanager
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn.preprocessing import StandardScaler | Home Credit Default Risk |
1,462,214 | df = pd.DataFrame()
df['filename'] = filenames
df['label'] = predictions
df = df.sort_values(by='filename')
df.to_csv('results.csv', header=True, index=False )<import_modules> | @contextmanager
def timer(title):
t0 = time.time()
yield
print("{} - done in {:.0f}s".format(title, time.time() - t0))
def one_hot_encoder(df, nan_as_category = True):
original_columns = list(df.columns)
categorical_columns = [col for col in df.columns if df[col].dtype == 'object']
df = pd.get_dummies(df, columns= categorical_columns, dummy_na= nan_as_category)
new_columns = [c for c in df.columns if c not in original_columns]
return df, new_columns
def application_train_test(num_rows = None, nan_as_category = False):
df = pd.read_csv('.. /input/application_train.csv', nrows= num_rows)
test_df = pd.read_csv('.. /input/application_test.csv', nrows= num_rows)
print("Train samples: {}, test samples: {}".format(len(df), len(test_df)))
df = df.append(test_df ).reset_index()
df = df[df['CODE_GENDER'] != 'XNA']
docs = [_f for _f in df.columns if 'FLAG_DOC' in _f]
live = [_f for _f in df.columns if('FLAG_' in _f)&('FLAG_DOC' not in _f)&('_FLAG_' not in _f)]
df['DAYS_EMPLOYED'].replace(365243, np.nan, inplace= True)
inc_by_org = df[['AMT_INCOME_TOTAL', 'ORGANIZATION_TYPE']].groupby('ORGANIZATION_TYPE' ).median() ['AMT_INCOME_TOTAL']
df['NEW_CREDIT_TO_ANNUITY_RATIO'] = df['AMT_CREDIT'] / df['AMT_ANNUITY']
df['NEW_CREDIT_TO_GOODS_RATIO'] = df['AMT_CREDIT'] / df['AMT_GOODS_PRICE']
df['NEW_DOC_IND_AVG'] = df[docs].mean(axis=1)
df['NEW_DOC_IND_STD'] = df[docs].std(axis=1)
df['NEW_DOC_IND_KURT'] = df[docs].kurtosis(axis=1)
df['NEW_LIVE_IND_SUM'] = df[live].sum(axis=1)
df['NEW_LIVE_IND_STD'] = df[live].std(axis=1)
df['NEW_LIVE_IND_KURT'] = df[live].kurtosis(axis=1)
df['NEW_INC_PER_CHLD'] = df['AMT_INCOME_TOTAL'] /(1 + df['CNT_CHILDREN'])
df['NEW_INC_BY_ORG'] = df['ORGANIZATION_TYPE'].map(inc_by_org)
df['NEW_EMPLOY_TO_BIRTH_RATIO'] = df['DAYS_EMPLOYED'] / df['DAYS_BIRTH']
df['NEW_ANNUITY_TO_INCOME_RATIO'] = df['AMT_ANNUITY'] /(1 + df['AMT_INCOME_TOTAL'])
df['NEW_SOURCES_PROD'] = df['EXT_SOURCE_1'] * df['EXT_SOURCE_2'] * df['EXT_SOURCE_3']
df['NEW_EXT_SOURCES_MEAN'] = df[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].mean(axis=1)
df['NEW_SCORES_STD'] = df[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].std(axis=1)
df['NEW_SCORES_STD'] = df['NEW_SCORES_STD'].fillna(df['NEW_SCORES_STD'].mean())
df['NEW_CAR_TO_BIRTH_RATIO'] = df['OWN_CAR_AGE'] / df['DAYS_BIRTH']
df['NEW_CAR_TO_EMPLOY_RATIO'] = df['OWN_CAR_AGE'] / df['DAYS_EMPLOYED']
df['NEW_PHONE_TO_BIRTH_RATIO'] = df['DAYS_LAST_PHONE_CHANGE'] / df['DAYS_BIRTH']
df['NEW_PHONE_TO_EMPLOY_RATIO'] = df['DAYS_LAST_PHONE_CHANGE'] / df['DAYS_EMPLOYED']
df['NEW_CREDIT_TO_INCOME_RATIO'] = df['AMT_CREDIT'] / df['AMT_INCOME_TOTAL']
for bin_feature in ['CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY']:
df[bin_feature], uniques = pd.factorize(df[bin_feature])
df, cat_cols = one_hot_encoder(df, nan_as_category)
del test_df
gc.collect()
return df
def bureau_and_balance(num_rows = None, nan_as_category = True):
bureau = pd.read_csv('.. /input/bureau.csv', nrows = num_rows)
bb = pd.read_csv('.. /input/bureau_balance.csv', nrows = num_rows)
bb, bb_cat = one_hot_encoder(bb, nan_as_category)
bureau, bureau_cat = one_hot_encoder(bureau, nan_as_category)
bb_aggregations = {'MONTHS_BALANCE': ['min', 'max', 'size']}
for col in bb_cat:
bb_aggregations[col] = ['mean']
bb_agg = bb.groupby('SK_ID_BUREAU' ).agg(bb_aggregations)
bb_agg.columns = pd.Index([e[0] + "_" + e[1].upper() for e in bb_agg.columns.tolist() ])
bureau = bureau.join(bb_agg, how='left', on='SK_ID_BUREAU')
bureau.drop(['SK_ID_BUREAU'], axis=1, inplace= True)
del bb, bb_agg
gc.collect()
num_aggregations = {
'DAYS_CREDIT': ['min', 'max', 'mean', 'var'],
'DAYS_CREDIT_ENDDATE': ['min', 'max', 'mean'],
'DAYS_CREDIT_UPDATE': ['mean'],
'CREDIT_DAY_OVERDUE': ['max', 'mean'],
'AMT_CREDIT_MAX_OVERDUE': ['mean'],
'AMT_CREDIT_SUM': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_DEBT': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_OVERDUE': ['mean'],
'AMT_CREDIT_SUM_LIMIT': ['mean', 'sum'],
'AMT_ANNUITY': ['max', 'mean'],
'CNT_CREDIT_PROLONG': ['sum'],
'MONTHS_BALANCE_MIN': ['min'],
'MONTHS_BALANCE_MAX': ['max'],
'MONTHS_BALANCE_SIZE': ['mean', 'sum']
}
cat_aggregations = {}
for cat in bureau_cat: cat_aggregations[cat] = ['mean']
for cat in bb_cat: cat_aggregations[cat + "_MEAN"] = ['mean']
bureau_agg = bureau.groupby('SK_ID_CURR' ).agg({**num_aggregations, **cat_aggregations})
bureau_agg.columns = pd.Index(['BURO_' + e[0] + "_" + e[1].upper() for e in bureau_agg.columns.tolist() ])
active = bureau[bureau['CREDIT_ACTIVE_Active'] == 1]
active_agg = active.groupby('SK_ID_CURR' ).agg(num_aggregations)
cols = active_agg.columns.tolist()
active_agg.columns = pd.Index(['ACTIVE_' + e[0] + "_" + e[1].upper() for e in active_agg.columns.tolist() ])
bureau_agg = bureau_agg.join(active_agg, how='left', on='SK_ID_CURR')
del active, active_agg
gc.collect()
closed = bureau[bureau['CREDIT_ACTIVE_Closed'] == 1]
closed_agg = closed.groupby('SK_ID_CURR' ).agg(num_aggregations)
closed_agg.columns = pd.Index(['CLOSED_' + e[0] + "_" + e[1].upper() for e in closed_agg.columns.tolist() ])
bureau_agg = bureau_agg.join(closed_agg, how='left', on='SK_ID_CURR')
for e in cols:
bureau_agg['NEW_RATIO_BURO_' + e[0] + "_" + e[1].upper() ] = bureau_agg['ACTIVE_' + e[0] + "_" + e[1].upper() ] / bureau_agg['CLOSED_' + e[0] + "_" + e[1].upper() ]
del closed, closed_agg, bureau
gc.collect()
return bureau_agg
def previous_applications(num_rows = None, nan_as_category = True):
prev = pd.read_csv('.. /input/previous_application.csv', nrows = num_rows)
prev, cat_cols = one_hot_encoder(prev, nan_as_category= True)
prev['DAYS_FIRST_DRAWING'].replace(365243, np.nan, inplace= True)
prev['DAYS_FIRST_DUE'].replace(365243, np.nan, inplace= True)
prev['DAYS_LAST_DUE_1ST_VERSION'].replace(365243, np.nan, inplace= True)
prev['DAYS_LAST_DUE'].replace(365243, np.nan, inplace= True)
prev['DAYS_TERMINATION'].replace(365243, np.nan, inplace= True)
prev['APP_CREDIT_PERC'] = prev['AMT_APPLICATION'] / prev['AMT_CREDIT']
num_aggregations = {
'AMT_ANNUITY': ['min', 'max', 'mean'],
'AMT_APPLICATION': ['min', 'max', 'mean'],
'AMT_CREDIT': ['min', 'max', 'mean'],
'APP_CREDIT_PERC': ['min', 'max', 'mean', 'var'],
'AMT_DOWN_PAYMENT': ['min', 'max', 'mean'],
'AMT_GOODS_PRICE': ['min', 'max', 'mean'],
'HOUR_APPR_PROCESS_START': ['min', 'max', 'mean'],
'RATE_DOWN_PAYMENT': ['min', 'max', 'mean'],
'DAYS_DECISION': ['min', 'max', 'mean'],
'CNT_PAYMENT': ['mean', 'sum'],
}
cat_aggregations = {}
for cat in cat_cols:
cat_aggregations[cat] = ['mean']
prev_agg = prev.groupby('SK_ID_CURR' ).agg({**num_aggregations, **cat_aggregations})
prev_agg.columns = pd.Index(['PREV_' + e[0] + "_" + e[1].upper() for e in prev_agg.columns.tolist() ])
approved = prev[prev['NAME_CONTRACT_STATUS_Approved'] == 1]
approved_agg = approved.groupby('SK_ID_CURR' ).agg(num_aggregations)
cols = approved_agg.columns.tolist()
approved_agg.columns = pd.Index(['APPROVED_' + e[0] + "_" + e[1].upper() for e in approved_agg.columns.tolist() ])
prev_agg = prev_agg.join(approved_agg, how='left', on='SK_ID_CURR')
refused = prev[prev['NAME_CONTRACT_STATUS_Refused'] == 1]
refused_agg = refused.groupby('SK_ID_CURR' ).agg(num_aggregations)
refused_agg.columns = pd.Index(['REFUSED_' + e[0] + "_" + e[1].upper() for e in refused_agg.columns.tolist() ])
prev_agg = prev_agg.join(refused_agg, how='left', on='SK_ID_CURR')
del refused, refused_agg, approved, approved_agg, prev
for e in cols:
prev_agg['NEW_RATIO_PREV_' + e[0] + "_" + e[1].upper() ] = prev_agg['APPROVED_' + e[0] + "_" + e[1].upper() ] / prev_agg['REFUSED_' + e[0] + "_" + e[1].upper() ]
gc.collect()
return prev_agg
def pos_cash(num_rows = None, nan_as_category = True):
pos = pd.read_csv('.. /input/POS_CASH_balance.csv', nrows = num_rows)
pos, cat_cols = one_hot_encoder(pos, nan_as_category= True)
aggregations = {
'MONTHS_BALANCE': ['max', 'mean', 'size'],
'SK_DPD': ['max', 'mean'],
'SK_DPD_DEF': ['max', 'mean']
}
for cat in cat_cols:
aggregations[cat] = ['mean']
pos_agg = pos.groupby('SK_ID_CURR' ).agg(aggregations)
pos_agg.columns = pd.Index(['POS_' + e[0] + "_" + e[1].upper() for e in pos_agg.columns.tolist() ])
pos_agg['POS_COUNT'] = pos.groupby('SK_ID_CURR' ).size()
del pos
gc.collect()
return pos_agg
def installments_payments(num_rows = None, nan_as_category = True):
ins = pd.read_csv('.. /input/installments_payments.csv', nrows = num_rows)
ins, cat_cols = one_hot_encoder(ins, nan_as_category= True)
ins['PAYMENT_PERC'] = ins['AMT_PAYMENT'] / ins['AMT_INSTALMENT']
ins['PAYMENT_DIFF'] = ins['AMT_INSTALMENT'] - ins['AMT_PAYMENT']
ins['DPD'] = ins['DAYS_ENTRY_PAYMENT'] - ins['DAYS_INSTALMENT']
ins['DBD'] = ins['DAYS_INSTALMENT'] - ins['DAYS_ENTRY_PAYMENT']
ins['DPD'] = ins['DPD'].apply(lambda x: x if x > 0 else 0)
ins['DBD'] = ins['DBD'].apply(lambda x: x if x > 0 else 0)
aggregations = {
'NUM_INSTALMENT_VERSION': ['nunique'],
'DPD': ['max', 'mean', 'sum'],
'DBD': ['max', 'mean', 'sum'],
'PAYMENT_PERC': ['max', 'mean', 'sum', 'var'],
'PAYMENT_DIFF': ['max', 'mean', 'sum', 'var'],
'AMT_INSTALMENT': ['max', 'mean', 'sum'],
'AMT_PAYMENT': ['min', 'max', 'mean', 'sum'],
'DAYS_ENTRY_PAYMENT': ['max', 'mean', 'sum']
}
for cat in cat_cols:
aggregations[cat] = ['mean']
ins_agg = ins.groupby('SK_ID_CURR' ).agg(aggregations)
ins_agg.columns = pd.Index(['INSTAL_' + e[0] + "_" + e[1].upper() for e in ins_agg.columns.tolist() ])
ins_agg['INSTAL_COUNT'] = ins.groupby('SK_ID_CURR' ).size()
del ins
gc.collect()
return ins_agg
def credit_card_balance(num_rows = None, nan_as_category = True):
cc = pd.read_csv('.. /input/credit_card_balance.csv', nrows = num_rows)
cc, cat_cols = one_hot_encoder(cc, nan_as_category= True)
cc.drop(['SK_ID_PREV'], axis= 1, inplace = True)
cc_agg = cc.groupby('SK_ID_CURR' ).agg(['min', 'max', 'mean', 'sum', 'var'])
cc_agg.columns = pd.Index(['CC_' + e[0] + "_" + e[1].upper() for e in cc_agg.columns.tolist() ])
cc_agg['CC_COUNT'] = cc.groupby('SK_ID_CURR' ).size()
del cc
gc.collect()
return cc_agg | Home Credit Default Risk |
1,462,214 | import pandas as pd
import numpy as np
import cv2
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sys import getsizeof<load_from_csv> | warnings.simplefilter(action='ignore', category=FutureWarning)
debug = None
num_rows = 10000 if debug else None
df = application_train_test(num_rows)
with timer("Process bureau and bureau_balance"):
bureau = bureau_and_balance(num_rows)
print("Bureau df shape:", bureau.shape)
df = df.join(bureau, how='left', on='SK_ID_CURR')
del bureau
gc.collect()
with timer("Process previous_applications"):
prev = previous_applications(num_rows)
print("Previous applications df shape:", prev.shape)
df = df.join(prev, how='left', on='SK_ID_CURR')
del prev
gc.collect()
with timer("Process POS-CASH balance"):
pos = pos_cash(num_rows)
print("Pos-cash balance df shape:", pos.shape)
df = df.join(pos, how='left', on='SK_ID_CURR')
del pos
gc.collect()
with timer("Process installments payments"):
ins = installments_payments(num_rows)
print("Installments payments df shape:", ins.shape)
df = df.join(ins, how='left', on='SK_ID_CURR')
del ins
gc.collect()
with timer("Process credit card balance"):
cc = credit_card_balance(num_rows)
print("Credit card balance df shape:", cc.shape)
df = df.join(cc, how='left', on='SK_ID_CURR')
del cc
gc.collect() | Home Credit Default Risk |
1,462,214 | train = pd.read_csv('.. /input/ava/train.csv', index_col=0)
test = pd.read_csv('.. /input/ava/test.csv', index_col=0)
train<prepare_x_and_y> | feats = [f for f in df.columns if f not in ['TARGET','SK_ID_CURR','SK_ID_BUREAU','SK_ID_PREV','index']]
for c in feats:
ss = StandardScaler()
df.loc[~np.isfinite(df[c]),c] = np.nan
df.loc[~df[c].isnull() ,c] = ss.fit_transform(df.loc[~df[c].isnull() ,c].values.reshape(-1,1))
df[c].fillna(-99999.,inplace=True ) | Home Credit Default Risk |
1,462,214 | X_train = train['image'].values
y_train = train['label'].values
X_test = test['image'].values<normalization> | def Output(p):
return 1./(1.+np.exp(-p))
def GP1(data):
v = pd.DataFrame()
v["i0"] = 0.005976*np.tanh(((((np.minimum(((((((((np.tanh(( np.minimum(((data["DAYS_EMPLOYED"])) ,(( data["REGION_RATING_CLIENT_W_CITY"])))))) -(data["EXT_SOURCE_3"])))* 2.0)) +(data["NEW_CREDIT_TO_GOODS_RATIO"])))) ,(((-1.0*(( data["NEW_SOURCES_PROD"])))))))* 2.0)) * 2.0))
v["i1"] = 0.040171*np.tanh(((((((((((( data["DAYS_EMPLOYED"])>(((data["EXT_SOURCE_2"])+(np.maximum(((data["EXT_SOURCE_1"])) ,(( data["EXT_SOURCE_3"])))))))*1.))-(np.maximum(((data["EXT_SOURCE_3"])) ,(( data["EXT_SOURCE_2"])))))) * 2.0)) * 2.0)) * 2.0))
v["i2"] = 0.049975*np.tanh(((((((((((((((((np.tanh(( data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"])))-(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) -(np.tanh(( data["CC_AMT_PAYMENT_CURRENT_SUM"])))))* 2.0)) * 2.0)) * 2.0)) -(data["NEW_EXT_SOURCES_MEAN"])))* 2.0))
v["i3"] = 0.049570*np.tanh(((((((((data["NAME_INCOME_TYPE_Working"])+(((((((( -1.0*(( data["NEW_EXT_SOURCES_MEAN"])))) * 2.0)) -(((( data["CC_AMT_PAYMENT_CURRENT_MIN"])<(data["BURO_DAYS_CREDIT_ENDDATE_MAX"])) *1.))))* 2.0)))) * 2.0)) * 2.0)) * 2.0))
v["i4"] = 0.040171*np.tanh(((((( -1.0*(((((((data["APPROVED_DAYS_DECISION_MIN"])<(((( data["NEW_EXT_SOURCES_MEAN"])>(((data["INSTAL_DPD_MEAN"])* 2.0)))*1.))) *1.))+(((data["NEW_EXT_SOURCES_MEAN"])* 2.0)))))))* 2.0)) * 2.0))
v["i5"] = 0.040171*np.tanh(((((data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])-(((data["NEW_EXT_SOURCES_MEAN"])+(((np.tanh(( data["PREV_RATE_DOWN_PAYMENT_MAX"])))+(((((((( data["CODE_GENDER"])>(data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])) *1.))+(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)))))))) * 2.0))
v["i6"] = 0.049088*np.tanh(((((((((((((( -1.0*(( data["NEW_EXT_SOURCES_MEAN"])))) * 2.0)) +(np.tanh(((( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])+(data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])))))))+(((data["DAYS_BIRTH"])/ 2.0)))) * 2.0)) * 2.0)) * 2.0))
v["i7"] = 0.040171*np.tanh(((((data["NEW_EXT_SOURCES_MEAN"])* 2.0)) *(np.minimum(((data["NEW_EXT_SOURCES_MEAN"])) ,(((((((((( data["NEW_EXT_SOURCES_MEAN"])*(data["REFUSED_APP_CREDIT_PERC_MAX"])))+(data["ACTIVE_DAYS_CREDIT_ENDDATE_MEAN"])) /2.0)) +(( -1.0*(((8.0)))))) /2.0)))))))
v["i8"] = 0.047400*np.tanh(((((((((( -1.0*(((( data["NEW_EXT_SOURCES_MEAN"])+(np.maximum(((np.maximum(((( -1.0*(( data["NAME_EDUCATION_TYPE_Secondary___secondary_special"]))))),(( data["NEW_CAR_TO_BIRTH_RATIO"]))))),(( data["NEW_EMPLOY_TO_BIRTH_RATIO"])))))))))-(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) * 2.0)) * 2.0))
v["i9"] = 0.040171*np.tanh(((((((((((( data["DAYS_EMPLOYED"])-(data["NAME_EDUCATION_TYPE_Higher_education"])))+(data["NEW_CREDIT_TO_GOODS_RATIO"])) /2.0)) -(((data["EXT_SOURCE_2"])-(np.tanh(((( data["ACTIVE_DAYS_CREDIT_MAX"])-(data["BURO_CREDIT_TYPE_Mortgage_MEAN"])))))))))* 2.0)) * 2.0))
v["i10"] = 0.049975*np.tanh(( -1.0*(((((((( data["NEW_EXT_SOURCES_MEAN"])-(( -1.0*(( np.tanh(((((( data["CODE_GENDER"])-(((data["DAYS_EMPLOYED"])+(data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))))-(data["PREV_CNT_PAYMENT_MEAN"])))))))))) * 2.0)) * 2.0)))))
v["i11"] = 0.049620*np.tanh(((((((((((((((data["NEW_CREDIT_TO_GOODS_RATIO"])-(((( data["EXT_SOURCE_3"])>(np.tanh(( data["CC_AMT_DRAWINGS_CURRENT_MEAN"])))) *1.))))-(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) -(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) * 2.0)) * 2.0))
v["i12"] = 0.050000*np.tanh(((((((((np.minimum(((data["NEW_CREDIT_TO_GOODS_RATIO"])) ,(( data["NEW_DOC_IND_KURT"])))) -(((data["NEW_EXT_SOURCES_MEAN"])-(np.tanh(((( data["DAYS_EMPLOYED"])-(data["NEW_DOC_IND_KURT"])))))))))* 2.0)) * 2.0)) -(data["NAME_EDUCATION_TYPE_Higher_education"])))
v["i13"] = 0.050000*np.tanh(((((((((np.tanh(((( data["DAYS_EMPLOYED"])+(data["INSTAL_PAYMENT_DIFF_MAX"])))))-(data["NEW_EXT_SOURCES_MEAN"])))+(np.tanh(((( np.tanh(( data["REFUSED_DAYS_DECISION_MEAN"])))+(data["NEW_CREDIT_TO_GOODS_RATIO"])))))))* 2.0)) * 2.0))
v["i14"] = 0.047100*np.tanh(((((((((((data["NEW_CREDIT_TO_GOODS_RATIO"])-(data["NAME_EDUCATION_TYPE_Higher_education"])))-(data["CODE_GENDER"])))-(data["EXT_SOURCE_2"])))+(((((np.tanh(( data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"])))-(data["EXT_SOURCE_3"])))* 2.0)))) * 2.0))
v["i15"] = 0.049950*np.tanh(((((((data["NEW_CREDIT_TO_GOODS_RATIO"])-(data["NEW_EXT_SOURCES_MEAN"])))-(((((data["INSTAL_DBD_SUM"])-(np.tanh(( data["CC_AMT_RECIVABLE_MEAN"])))))-(np.maximum(((data["PREV_CNT_PAYMENT_MEAN"])) ,(( data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))))))) * 2.0))
v["i16"] = 0.046512*np.tanh(((((((data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])-(data["EXT_SOURCE_2"])))+(np.where(data["EXT_SOURCE_3"] < -99998, data["DAYS_EMPLOYED"],(((( data["NAME_INCOME_TYPE_Working"])-(data["CODE_GENDER"])))-(((data["EXT_SOURCE_3"])* 2.0)))))))* 2.0))
v["i17"] = 0.049820*np.tanh(((((np.where(data["APPROVED_AMT_DOWN_PAYMENT_MAX"]>0, data["REFUSED_DAYS_DECISION_MAX"], data["DAYS_LAST_PHONE_CHANGE"])) +(((data["NEW_DOC_IND_KURT"])+(((((data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])-(data["NEW_EXT_SOURCES_MEAN"])))-(data["CODE_GENDER"])))))))-(data["NEW_EXT_SOURCES_MEAN"])))
v["i18"] = 0.049119*np.tanh(((((data["DEF_30_CNT_SOCIAL_CIRCLE"])+(((((((((data["PREV_CNT_PAYMENT_MEAN"])-(data["POS_MONTHS_BALANCE_SIZE"])))-(data["NEW_EXT_SOURCES_MEAN"])))+(((((((data["INSTAL_DPD_MEAN"])* 2.0)) * 2.0)) * 2.0)))) * 2.0)))) * 2.0))
v["i19"] = 0.048000*np.tanh(((data["PREV_NAME_YIELD_GROUP_high_MEAN"])-(((((data["PREV_NAME_CONTRACT_STATUS_Approved_MEAN"])+(data["NEW_EXT_SOURCES_MEAN"])))+(((((np.maximum(((data["CODE_GENDER"])) ,(( data["APPROVED_RATE_DOWN_PAYMENT_MAX"])))) +(np.tanh(( data["BURO_CREDIT_ACTIVE_Closed_MEAN"])))))-(data["NEW_ANNUITY_TO_INCOME_RATIO"])))))))
v["i20"] = 0.049970*np.tanh(((((((data["REGION_RATING_CLIENT_W_CITY"])+(np.where(np.maximum(((np.minimum(((data["DAYS_EMPLOYED"])) ,(( data["FLAG_DOCUMENT_3"]))))),(( data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))>0,(-1.0*(( data["EXT_SOURCE_3"]))), data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"])))) * 2.0)) +(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])))
v["i21"] = 0.049799*np.tanh(((data["NEW_CREDIT_TO_GOODS_RATIO"])+(( -1.0*(((( data["NEW_EXT_SOURCES_MEAN"])+(((data["APPROVED_AMT_GOODS_PRICE_MIN"])-(((((((((data["INSTAL_PAYMENT_DIFF_MAX"])* 2.0)) -(data["POS_MONTHS_BALANCE_SIZE"])))* 2.0)) * 2.0)))))))))))
v["i22"] = 0.049870*np.tanh(((((np.where(data["NEW_CAR_TO_BIRTH_RATIO"]>0, data["REFUSED_DAYS_DECISION_MAX"], np.maximum(((data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"])) ,(( data["AMT_ANNUITY"])))))-(((data["APPROVED_RATE_DOWN_PAYMENT_MAX"])+(data["CODE_GENDER"])))))-(((data["NEW_EXT_SOURCES_MEAN"])-(data["PREV_NAME_YIELD_GROUP_high_MEAN"])))))
v["i23"] = 0.047896*np.tanh(((((((np.where(data["CC_AMT_BALANCE_MAX"] < -99998,(( data["DAYS_EMPLOYED"])-(((np.maximum(((data["APPROVED_AMT_DOWN_PAYMENT_MAX"])) ,(( data["FLOORSMAX_AVG"])))) * 2.0))),(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])* 2.0)))* 2.0)) -(data["NEW_EXT_SOURCES_MEAN"])))* 2.0))
v["i24"] = 0.046500*np.tanh(((((((((((((data["PREV_CNT_PAYMENT_MEAN"])-(data["POS_MONTHS_BALANCE_SIZE"])))* 2.0)) -(data["NEW_EXT_SOURCES_MEAN"])))+(((data["PREV_NAME_YIELD_GROUP_high_MEAN"])+(np.tanh(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])))))))+(data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))* 2.0))
v["i25"] = 0.048000*np.tanh(((data["NEW_ANNUITY_TO_INCOME_RATIO"])-(((data["NEW_EXT_SOURCES_MEAN"])-(((data["FLAG_DOCUMENT_3"])-(np.maximum(((((np.maximum(((data["NEW_CAR_TO_BIRTH_RATIO"])) ,(((((( data["INSTAL_AMT_PAYMENT_MIN"])* 2.0)) * 2.0)))))* 2.0))),(( data["BURO_CREDIT_ACTIVE_Closed_MEAN"]))))))))))
v["i26"] = 0.047900*np.tanh(((np.where(data["NAME_EDUCATION_TYPE_Higher_education"]>0, data["CC_AMT_RECIVABLE_MAX"], np.where(data["PREV_NAME_YIELD_GROUP_low_action_MEAN"]>0, data["CC_AMT_RECIVABLE_MEAN"],(( np.where(data["DAYS_EMPLOYED"]<0,(-1.0*(( data["BURO_CREDIT_ACTIVE_Closed_MEAN"]))),(-1.0*(( data["EXT_SOURCE_1"])))))* 2.0)))) * 2.0))
v["i27"] = 0.049976*np.tanh(((((((((((data["PREV_NAME_PRODUCT_TYPE_walk_in_MEAN"])-(((data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])-(data["APPROVED_DAYS_DECISION_MIN"])))))-(data["APPROVED_AMT_ANNUITY_MEAN"])))+(data["NEW_DOC_IND_KURT"])))-(((data["INSTAL_AMT_PAYMENT_MIN"])-(data["INSTAL_AMT_INSTALMENT_MAX"])))))* 2.0))
v["i28"] = 0.049718*np.tanh(((((np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]>0, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"], np.where(data["POS_MONTHS_BALANCE_SIZE"]>0, data["APPROVED_CNT_PAYMENT_MEAN"], np.where(data["INSTAL_AMT_PAYMENT_MIN"]>0, data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"],(-1.0*(( data["NEW_SOURCES_PROD"])))))))* 2.0)) * 2.0))
v["i29"] = 0.045700*np.tanh(((((((((data["INSTAL_PAYMENT_DIFF_SUM"])+(data["PREV_CNT_PAYMENT_MEAN"])))+(((((data["PREV_NAME_CLIENT_TYPE_New_MEAN"])+(((data["NEW_CREDIT_TO_GOODS_RATIO"])+(data["DEF_30_CNT_SOCIAL_CIRCLE"])))))+(data["PREV_NAME_YIELD_GROUP_XNA_MEAN"])))))-(data["CODE_GENDER"])))* 2.0))
v["i30"] = 0.049198*np.tanh(((((((((np.tanh(( data["AMT_ANNUITY"])))+(((data["PREV_CNT_PAYMENT_SUM"])-(np.maximum(((data["NEW_CAR_TO_EMPLOY_RATIO"])) ,(( data["POS_COUNT"])))))))) -(((data["INSTAL_AMT_PAYMENT_MIN"])+(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])))))* 2.0)) * 2.0))
v["i31"] = 0.049397*np.tanh(((((np.where(data["ACTIVE_DAYS_CREDIT_VAR"] < -99998,(((( data["INSTAL_PAYMENT_DIFF_MAX"])* 2.0)) -(data["APPROVED_AMT_ANNUITY_MEAN"])) ,(( data["APPROVED_CNT_PAYMENT_MEAN"])+(((data["ACTIVE_DAYS_CREDIT_MEAN"])-(data["BURO_CREDIT_ACTIVE_Closed_MEAN"])))))) +(data["DEF_60_CNT_SOCIAL_CIRCLE"])))* 2.0))
v["i32"] = 0.048984*np.tanh(((((data["REGION_RATING_CLIENT_W_CITY"])+(np.where(((data["APPROVED_AMT_ANNUITY_MAX"])-(((data["INSTAL_DPD_MEAN"])-(data["CODE_GENDER"])))) <0,(( data["REGION_RATING_CLIENT_W_CITY"])-(data["NEW_CAR_TO_BIRTH_RATIO"])) , data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"])))) -(data["PREV_NAME_PORTFOLIO_POS_MEAN"])))
v["i33"] = 0.049560*np.tanh(((((data["AMT_ANNUITY"])-(((np.where(data["CC_CNT_DRAWINGS_ATM_CURRENT_MAX"]>0, data["EXT_SOURCE_1"],(( np.maximum(((((data["NAME_FAMILY_STATUS_Married"])+(data["EXT_SOURCE_3"])))) ,(( data["APPROVED_AMT_DOWN_PAYMENT_MAX"])))) * 2.0)))-(data["REGION_RATING_CLIENT_W_CITY"])))))* 2.0))
v["i34"] = 0.049700*np.tanh(((((((np.maximum(((data["NEW_CREDIT_TO_GOODS_RATIO"])) ,(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])))) +(((((((data["PREV_NAME_CLIENT_TYPE_New_MEAN"])+(((data["APPROVED_CNT_PAYMENT_MEAN"])-(data["PREV_NAME_YIELD_GROUP_low_action_MEAN"])))))-(data["PREV_CODE_REJECT_REASON_XAP_MEAN"])))* 2.0)))) * 2.0)) * 2.0))
v["i35"] = 0.049646*np.tanh(np.where(data["POS_SK_DPD_DEF_MAX"]<0,(((((((( data["FLAG_WORK_PHONE"])+(((np.where(data["EXT_SOURCE_1"] < -99998, data["DAYS_EMPLOYED"], data["CC_CNT_DRAWINGS_CURRENT_MEAN"])) * 2.0)))) * 2.0)) * 2.0)) * 2.0), 3.141593))
v["i36"] = 0.049390*np.tanh(((data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])+(((data["BURO_CREDIT_ACTIVE_Active_MEAN"])+(((data["NEW_ANNUITY_TO_INCOME_RATIO"])+(((((np.where(data["CLOSED_DAYS_CREDIT_VAR"] < -99998,(( data["INSTAL_AMT_PAYMENT_SUM"])*(data["CLOSED_DAYS_CREDIT_VAR"])) , data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"])) * 2.0)) * 2.0))))))))
v["i37"] = 0.049750*np.tanh(((((np.where(np.where(data["PREV_NAME_CLIENT_TYPE_Repeater_MEAN"]>0, data["PREV_APP_CREDIT_PERC_MEAN"], data["NEW_CAR_TO_BIRTH_RATIO"])>0,(( data["CC_CNT_DRAWINGS_ATM_CURRENT_VAR"])* 2.0),(-1.0*(( np.where(data["OCCUPATION_TYPE_Core_staff"]<0, data["ENTRANCES_MEDI"], data["OCCUPATION_TYPE_Core_staff"])))))) * 2.0)) * 2.0))
v["i38"] = 0.049300*np.tanh(((((((((np.maximum(((np.minimum(((data["REGION_RATING_CLIENT_W_CITY"])) ,(( data["AMT_ANNUITY"]))))),(((( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])* 2.0)))))-(((data["APPROVED_AMT_ANNUITY_MEAN"])-(data["DEF_30_CNT_SOCIAL_CIRCLE"])))))+(data["INSTAL_PAYMENT_DIFF_MEAN"])))* 2.0)) * 2.0))
v["i39"] = 0.049060*np.tanh(((((data["ORGANIZATION_TYPE_Self_employed"])+(((np.where(data["POS_SK_DPD_DEF_MEAN"]<0, data["REG_CITY_NOT_LIVE_CITY"],(8.0)))-(np.where(data["BURO_AMT_CREDIT_SUM_DEBT_SUM"]<0, np.maximum(((data["NAME_FAMILY_STATUS_Married"])) ,(( data["POS_SK_DPD_DEF_MAX"]))), data["NEW_CAR_TO_EMPLOY_RATIO"])))))) * 2.0))
v["i40"] = 0.048602*np.tanh(((((np.where(np.maximum(((data["CC_AMT_CREDIT_LIMIT_ACTUAL_SUM"])) ,(( np.maximum(((np.where(data["INSTAL_DBD_MEAN"]<0, data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"], data["NAME_FAMILY_STATUS_Married"]))),(( data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])))))) <0,(-1.0*(( data["FLOORSMAX_AVG"]))), data["APPROVED_CNT_PAYMENT_MEAN"])) * 2.0)) * 2.0))
v["i41"] = 0.048139*np.tanh(np.where(np.maximum(((data["INSTAL_DPD_MEAN"])) ,(( data["BURO_CREDIT_TYPE_Microloan_MEAN"])))<0,(( np.where(data["NEW_EXT_SOURCES_MEAN"]>0, data["DAYS_ID_PUBLISH"], data["ACTIVE_DAYS_CREDIT_MEAN"])) -(np.where(data["NEW_CAR_TO_EMPLOY_RATIO"]>0, 3.141593, data["NAME_EDUCATION_TYPE_Higher_education"]))), 3.141593))
v["i42"] = 0.048501*np.tanh(((((((np.maximum(((data["APPROVED_CNT_PAYMENT_MEAN"])) ,(( data["FLAG_WORK_PHONE"])))) -(data["CODE_GENDER"])))+(np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0,(((( data["NEW_CREDIT_TO_ANNUITY_RATIO"])-(data["PREV_AMT_APPLICATION_MEAN"])))* 2.0), data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_MAX"])))) * 2.0))
v["i43"] = 0.049898*np.tanh(((((np.where(data["APPROVED_AMT_CREDIT_MEAN"]>0, data["APPROVED_CNT_PAYMENT_SUM"],(( data["AMT_ANNUITY"])-(data["PREV_PRODUCT_COMBINATION_POS_industry_with_interest_MEAN"])))) -(np.maximum(((data["NEW_DOC_IND_AVG"])) ,(( data["PREV_NAME_YIELD_GROUP_low_action_MEAN"])))))) -(((data["INSTAL_DBD_SUM"])+(data["NAME_INCOME_TYPE_State_servant"])))))
v["i44"] = 0.049002*np.tanh(((((np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"])) ,(((( np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["NEW_CREDIT_TO_GOODS_RATIO"])))) -(np.maximum(((np.where(data["POS_SK_DPD_DEF_MAX"]<0, data["NAME_FAMILY_STATUS_Married"], data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"]))),(( data["CLOSED_DAYS_CREDIT_MAX"])))))))))* 2.0)) * 2.0))
v["i45"] = 0.049600*np.tanh(((((((((((np.maximum(((np.where(data["CC_AMT_TOTAL_RECEIVABLE_MEAN"] < -99998, data["INSTAL_DPD_MEAN"], data["CC_AMT_RECEIVABLE_PRINCIPAL_MIN"]))),(( data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])))) * 2.0)) * 2.0)) * 2.0)) +(np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["APPROVED_CNT_PAYMENT_SUM"])))))) * 2.0))
v["i46"] = 0.049079*np.tanh(((((((((( -1.0*(( np.maximum(((data["EXT_SOURCE_1"])) ,(( data["POS_COUNT"])))))))+(np.where(np.maximum(((data["APPROVED_CNT_PAYMENT_SUM"])) ,(( data["INSTAL_DBD_SUM"])))<0, data["OCCUPATION_TYPE_Laborers"], data["APPROVED_CNT_PAYMENT_SUM"])))) * 2.0)) * 2.0)) * 2.0))
v["i47"] = 0.049180*np.tanh(((((np.minimum(((((((((data["DAYS_REGISTRATION"])-(data["BURO_CREDIT_TYPE_Mortgage_MEAN"])))-(data["OCCUPATION_TYPE_Core_staff"])))-(data["BURO_CREDIT_TYPE_Mortgage_MEAN"])))) ,(((( data["INSTAL_PAYMENT_DIFF_SUM"])-(data["APPROVED_HOUR_APPR_PROCESS_START_MAX"])))))) -(data["NAME_EDUCATION_TYPE_Incomplete_higher"])))* 2.0))
v["i48"] = 0.049515*np.tanh(((((((np.where(data["CC_AMT_RECEIVABLE_PRINCIPAL_SUM"] < -99998,(( data["AMT_ANNUITY"])-(data["APPROVED_AMT_ANNUITY_MEAN"])) , data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"])) +(np.maximum(((data["OCCUPATION_TYPE_Drivers"])) ,(( data["BURO_CREDIT_TYPE_Microloan_MEAN"])))))) * 2.0)) * 2.0))
v["i49"] = 0.049802*np.tanh(((data["INSTAL_PAYMENT_DIFF_MAX"])+(np.where(data["BURO_CREDIT_ACTIVE_Active_MEAN"]<0, np.where(data["BURO_DAYS_CREDIT_MAX"]<0, data["DAYS_LAST_PHONE_CHANGE"], data["BURO_CREDIT_ACTIVE_Active_MEAN"]),(( data["FLAG_WORK_PHONE"])-(((data["PREV_HOUR_APPR_PROCESS_START_MEAN"])-(((data["ACTIVE_DAYS_CREDIT_MAX"])* 2.0)))))))))
v["i50"] = 0.049043*np.tanh(( -1.0*(( np.where(data["EXT_SOURCE_1"]>0, data["NEW_EXT_SOURCES_MEAN"],(((((((( data["NEW_EXT_SOURCES_MEAN"])-(((np.tanh(( np.tanh(((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)))))) * 2.0)))) * 2.0)) * 2.0)) * 2.0))))))
v["i51"] = 0.047443*np.tanh(((((data["ORGANIZATION_TYPE_Construction"])+(((data["REG_CITY_NOT_LIVE_CITY"])+(((data["ORGANIZATION_TYPE_Business_Entity_Type_3"])+(np.maximum(((data["NEW_SCORES_STD"])) ,(((( np.maximum(((data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"])) ,(( data["PREV_CODE_REJECT_REASON_SCOFR_MEAN"])))) +(data["PREV_CNT_PAYMENT_MEAN"])))))))))))) * 2.0))
v["i52"] = 0.049973*np.tanh(((data["REGION_RATING_CLIENT_W_CITY"])+(np.where(data["NEW_EXT_SOURCES_MEAN"]<0,(((( np.where(data["POS_SK_DPD_DEF_MAX"]<0, data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"], data["POS_MONTHS_BALANCE_MEAN"])) * 2.0)) * 2.0),(((( data["POS_MONTHS_BALANCE_MEAN"])-(data["NEW_CAR_TO_BIRTH_RATIO"])))* 2.0)))))
v["i53"] = 0.047044*np.tanh(((((((np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]>0, data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"], data["NEW_CREDIT_TO_ANNUITY_RATIO"])) -(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])))-(((data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])-(data["NEW_ANNUITY_TO_INCOME_RATIO"])))))+(np.maximum(((data["ACTIVE_DAYS_CREDIT_ENDDATE_MAX"])) ,(( data["BURO_CREDIT_TYPE_Microloan_MEAN"]))))))
v["i54"] = 0.049700*np.tanh(((((((((((((data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__high_MEAN"])+(np.maximum(((data["PREV_PRODUCT_COMBINATION_Cash_Street__high_MEAN"])) ,(( data["CC_AMT_RECEIVABLE_PRINCIPAL_MEAN"])))))) -(data["INSTAL_AMT_PAYMENT_SUM"])))* 2.0)) * 2.0)) -(np.maximum(((data["PREV_APP_CREDIT_PERC_MEAN"])) ,(( data["BURO_CREDIT_TYPE_Mortgage_MEAN"])))))) * 2.0))
v["i55"] = 0.049609*np.tanh(((((np.where(data["EXT_SOURCE_3"] < -99998, data["REFUSED_DAYS_DECISION_MAX"],(( data["EXT_SOURCE_3"])*(data["EXT_SOURCE_3"])))) -(data["BURO_DAYS_CREDIT_MAX"])))+(np.where(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"] < -99998, data["EXT_SOURCE_3"], -1.0))))
v["i56"] = 0.049496*np.tanh(((np.maximum(((data["DAYS_ID_PUBLISH"])) ,(( data["ACTIVE_DAYS_CREDIT_MAX"])))) -(np.maximum(((data["BURO_CREDIT_TYPE_Mortgage_MEAN"])) ,(((((((( data["NEW_DOC_IND_AVG"])+(data["NAME_INCOME_TYPE_State_servant"])))+(data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])))-(data["NEW_CREDIT_TO_ANNUITY_RATIO"]))))))))
v["i57"] = 0.048440*np.tanh(((np.where(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"]<0, data["AMT_ANNUITY"], data["APPROVED_CNT_PAYMENT_MEAN"])) +(((np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["POS_NAME_CONTRACT_STATUS_Returned_to_the_store_MEAN"])))) +(((data["ORGANIZATION_TYPE_Construction"])+(((data["EXT_SOURCE_2"])*(data["INSTAL_DBD_SUM"])))))))))
v["i58"] = 0.044197*np.tanh(((((np.where(data["CODE_GENDER"]<0, np.maximum(((data["WALLSMATERIAL_MODE_Stone__brick"])) ,(( data["FLAG_DOCUMENT_3"]))), np.where(np.where(data["PREV_PRODUCT_COMBINATION_Cash_MEAN"]<0, data["FLAG_WORK_PHONE"], data["CC_CNT_DRAWINGS_CURRENT_VAR"])<0, data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"], data["FLAG_DOCUMENT_3"])))* 2.0)) * 2.0))
v["i59"] = 0.049650*np.tanh(((((data["WALLSMATERIAL_MODE_Stone__brick"])-(np.where(data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"], np.maximum(((np.maximum(((data["NEW_SOURCES_PROD"])) ,(( data["ACTIVE_AMT_CREDIT_SUM_MAX"]))))),(( np.maximum(((data["APPROVED_AMT_CREDIT_MIN"])) ,(( data["BURO_STATUS_0_MEAN_MEAN"])))))))))) -(data["NAME_INCOME_TYPE_Commercial_associate"])))
v["i60"] = 0.047884*np.tanh(np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_MAX"], np.where(((data["NEW_RATIO_BURO_AMT_CREDIT_SUM_LIMIT_MEAN"])*(data["NEW_EXT_SOURCES_MEAN"])) < -99998, data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"],(((( data["ORGANIZATION_TYPE_Self_employed"])+(((( data["PREV_NAME_GOODS_CATEGORY_Furniture_MEAN"])<(data["NEW_EXT_SOURCES_MEAN"])) *1.))))* 2.0))))
v["i61"] = 0.048710*np.tanh(np.where(data["EXT_SOURCE_3"] < -99998, data["REFUSED_CNT_PAYMENT_SUM"],(((((((( -1.0)-(np.minimum(((((data["EXT_SOURCE_2"])/ 2.0))),(( data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])))))) * 2.0)) -(data["EXT_SOURCE_3"])))-(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"]))))
v["i62"] = 0.049366*np.tanh(np.where(data["AMT_REQ_CREDIT_BUREAU_QRT"]>0, data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"],(( np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"]>0, data["CC_CNT_DRAWINGS_CURRENT_SUM"],(((( data["NEW_RATIO_PREV_AMT_CREDIT_MIN"])*(data["OCCUPATION_TYPE_Accountants"])))-(data["AMT_REQ_CREDIT_BUREAU_YEAR"])))) -(data["NEW_PHONE_TO_EMPLOY_RATIO"]))))
v["i63"] = 0.048000*np.tanh(((np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"]>0, data["BURO_DAYS_CREDIT_MEAN"], np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"] < -99998, data["DEF_30_CNT_SOCIAL_CIRCLE"],(((( data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"])* 2.0)) * 2.0)))) -(np.where(data["ACTIVE_MONTHS_BALANCE_MIN_MIN"]>0, data["ACTIVE_MONTHS_BALANCE_MIN_MIN"], data["NAME_FAMILY_STATUS_Married"]))))
v["i64"] = 0.048498*np.tanh(((np.where(data["AMT_GOODS_PRICE"]<0, np.maximum(((data["AMT_ANNUITY"])) ,(((((((((( data["AMT_ANNUITY"])-(data["PREV_AMT_ANNUITY_MIN"])))* 2.0)) -(data["PREV_AMT_ANNUITY_MIN"])))* 2.0)))) , data["INSTAL_AMT_INSTALMENT_MAX"])) -(data["NEW_DOC_IND_STD"])))
v["i65"] = 0.045500*np.tanh(((data["APPROVED_CNT_PAYMENT_SUM"])-(((data["INSTAL_COUNT"])-(np.where(data["NEW_CREDIT_TO_GOODS_RATIO"]<0,(( data["INSTAL_DBD_MAX"])-(data["PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN"])) ,(((((data["NEW_ANNUITY_TO_INCOME_RATIO"])-(data["NEW_EXT_SOURCES_MEAN"])))+(data["NEW_ANNUITY_TO_INCOME_RATIO"])) /2.0)))))))
v["i66"] = 0.049995*np.tanh(((((((np.maximum(((data["EXT_SOURCE_3"])) ,(( data["NEW_EXT_SOURCES_MEAN"])))) +(((data["NEW_EXT_SOURCES_MEAN"])*(((data["NEW_EXT_SOURCES_MEAN"])+(data["INSTAL_PAYMENT_DIFF_MEAN"])))))))+(((data["NEW_EXT_SOURCES_MEAN"])+(data["INSTAL_PAYMENT_DIFF_MEAN"])))))* 2.0))
v["i67"] = 0.039797*np.tanh(((((np.maximum(((data["PREV_CHANNEL_TYPE_Contact_center_MEAN"])) ,(( np.where(data["PREV_DAYS_DECISION_MIN"]>0,(( np.maximum(((((np.maximum(((data["INSTAL_DPD_MEAN"])) ,(( data["BURO_CREDIT_TYPE_Microloan_MEAN"])))) * 2.0))),(( data["CC_AMT_RECIVABLE_VAR"])))) * 2.0), data["NEW_CREDIT_TO_ANNUITY_RATIO"])))))* 2.0)) * 2.0))
v["i68"] = 0.049954*np.tanh(((((((data["NAME_FAMILY_STATUS_Separated"])+(np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(((( np.where(data["INSTAL_COUNT"]>0, data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"], data["PREV_DAYS_DECISION_MEAN"])) -(((( data["INSTAL_DPD_MAX"])<(data["PREV_NAME_TYPE_SUITE_Other_B_MEAN"])) *1.))))))))) * 2.0)) * 2.0))
v["i69"] = 0.045722*np.tanh(((((np.where(data["ACTIVE_MONTHS_BALANCE_SIZE_SUM"]>0, -1.0,(( np.where(data["INSTAL_AMT_INSTALMENT_SUM"]>0, data["ACTIVE_AMT_CREDIT_SUM_SUM"],(-1.0*(( np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0, data["PREV_NAME_YIELD_GROUP_low_action_MEAN"], data["PREV_NAME_PRODUCT_TYPE_XNA_MEAN"])))))) * 2.0)))* 2.0)) * 2.0))
v["i70"] = 0.048797*np.tanh(((np.where(np.maximum(((data["APPROVED_AMT_CREDIT_MAX"])) ,(( data["AMT_ANNUITY"])))<0, data["AMT_ANNUITY"],(-1.0*(( data["PREV_AMT_ANNUITY_MEAN"])))))+(((data["POS_SK_DPD_DEF_MAX"])+(np.maximum(((data["ACTIVE_DAYS_CREDIT_MAX"])) ,(( data["POS_NAME_CONTRACT_STATUS_Returned_to_the_store_MEAN"]))))))))
v["i71"] = 0.047984*np.tanh(( -1.0*(( np.where(data["REGION_RATING_CLIENT_W_CITY"]>0, data["BURO_CREDIT_TYPE_Mortgage_MEAN"], np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"]>0, data["CC_AMT_PAYMENT_TOTAL_CURRENT_SUM"],(( np.maximum(((np.maximum(((data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"])) ,(( data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"]))))),(( data["CC_AMT_PAYMENT_TOTAL_CURRENT_SUM"])))) +(data["NEW_PHONE_TO_EMPLOY_RATIO"]))))))))
v["i72"] = 0.049980*np.tanh(np.where(data["EXT_SOURCE_3"] < -99998, data["REFUSED_AMT_APPLICATION_MAX"],(( np.maximum(((np.maximum(((((( data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])>(((data["OCCUPATION_TYPE_Core_staff"])/ 2.0)))*1.))) ,(((( data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])*(data["BURO_CREDIT_TYPE_Microloan_MEAN"]))))))),(( data["BURO_CREDIT_TYPE_Microloan_MEAN"])))) * 2.0)))
v["i73"] = 0.049350*np.tanh(((((((((((data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])-(((( data["BURO_AMT_CREDIT_SUM_MEAN"])>(data["ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN"])) *1.))))-(data["BURO_AMT_CREDIT_SUM_MEAN"])))+(np.tanh(( data["BURO_AMT_CREDIT_SUM_DEBT_MEAN"])))))-(data["BURO_AMT_CREDIT_SUM_MEAN"])))-(data["BURO_AMT_CREDIT_SUM_MEAN"])))
v["i74"] = 0.049056*np.tanh(np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0, np.where(data["PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN"]<0, data["DAYS_REGISTRATION"], data["NEW_CREDIT_TO_ANNUITY_RATIO"]), np.where(data["OWN_CAR_AGE"] < -99998,(((( data["REGION_POPULATION_RELATIVE"])-(data["PREV_NAME_CONTRACT_TYPE_Cash_loans_MEAN"])))-(data["INSTAL_AMT_PAYMENT_SUM"])) , -2.0)))
v["i75"] = 0.041842*np.tanh(np.maximum(((data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])) ,(( np.where(data["PREV_NAME_PAYMENT_TYPE_XNA_MEAN"]<0, data["APPROVED_AMT_GOODS_PRICE_MAX"],(((((( data["PREV_CNT_PAYMENT_MEAN"])+(data["ORGANIZATION_TYPE_Construction"])))+(data["NEW_CREDIT_TO_GOODS_RATIO"])))+(((data["ORGANIZATION_TYPE_Transport__type_3"])+(data["PREV_PRODUCT_COMBINATION_Cash_Street__middle_MEAN"])))))))))
v["i76"] = 0.049296*np.tanh(((((( data["NEW_EXT_SOURCES_MEAN"])>(data["NAME_INCOME_TYPE_State_servant"])) *1.))+(((((((data["OCCUPATION_TYPE_Drivers"])-(data["NEW_DOC_IND_AVG"])))-(data["INSTAL_AMT_INSTALMENT_SUM"])))+(np.where(data["PREV_AMT_CREDIT_MEAN"]<0, data["REGION_POPULATION_RELATIVE"], data["OBS_60_CNT_SOCIAL_CIRCLE"]))))))
v["i77"] = 0.048658*np.tanh(((((data["CC_AMT_RECEIVABLE_PRINCIPAL_MEAN"])-(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"])))+(np.where(data["POS_SK_DPD_MEAN"] < -99998, data["APPROVED_CNT_PAYMENT_SUM"],(((((( data["APPROVED_AMT_APPLICATION_MAX"])-(data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])))-(data["WEEKDAY_APPR_PROCESS_START_MONDAY"])))-(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"]))))))
v["i78"] = 0.049800*np.tanh(np.where(data["EXT_SOURCE_1"] < -99998,(((( data["DAYS_BIRTH"])* 2.0)) * 2.0),(((((( data["ORGANIZATION_TYPE_Medicine"])-(data["EXT_SOURCE_1"])))-(((((data["DAYS_BIRTH"])* 2.0)) * 2.0)))) -(data["EXT_SOURCE_1"]))))
v["i79"] = 0.048000*np.tanh(np.maximum(((data["CC_CNT_DRAWINGS_ATM_CURRENT_MAX"])) ,(( np.where(data["ACTIVE_MONTHS_BALANCE_SIZE_SUM"]<0,(( np.where(data["CLOSED_AMT_CREDIT_MAX_OVERDUE_MEAN"] < -99998,(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MAX"])-(data["CC_AMT_PAYMENT_CURRENT_SUM"])) , data["CC_NAME_CONTRACT_STATUS_Active_SUM"])) -(data["NEW_PHONE_TO_EMPLOY_RATIO"])) , data["EXT_SOURCE_2"])))))
v["i80"] = 0.045128*np.tanh(np.where(data["APPROVED_AMT_APPLICATION_MAX"] < -99998, data["NEW_DOC_IND_AVG"], np.where(data["WEEKDAY_APPR_PROCESS_START_SATURDAY"]<0, np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0,(((data["POS_COUNT"])<(data["PREV_AMT_GOODS_PRICE_MAX"])) *1.) , np.maximum(((data["APPROVED_AMT_APPLICATION_MAX"])) ,(( data["APARTMENTS_MEDI"])))) , data["REFUSED_DAYS_DECISION_MAX"])))
v["i81"] = 0.049278*np.tanh(((((((((( data["NEW_CREDIT_TO_ANNUITY_RATIO"])<(((( data["NAME_HOUSING_TYPE_Rented_apartment"])+(data["NEW_DOC_IND_KURT"])) /2.0)))*1.))+(np.minimum(((np.minimum(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])) ,(( data["REG_CITY_NOT_LIVE_CITY"]))))),(( data["REGION_RATING_CLIENT_W_CITY"])))))) * 2.0)) * 2.0))
v["i82"] = 0.049000*np.tanh(((data["BURO_CREDIT_TYPE_Credit_card_MEAN"])-(np.where(data["PREV_NAME_CONTRACT_STATUS_Approved_MEAN"]>0, data["ACTIVE_MONTHS_BALANCE_SIZE_MEAN"], np.maximum(((data["BURO_CREDIT_TYPE_Car_loan_MEAN"])) ,(((( data["BURO_CREDIT_TYPE_Credit_card_MEAN"])+(((np.maximum(((data["DAYS_BIRTH"])) ,(( data["ORGANIZATION_TYPE_School"])))) +(data["PREV_NAME_CONTRACT_STATUS_Approved_MEAN"])))))))))))
v["i83"] = 0.049400*np.tanh(np.where(data["POS_SK_DPD_DEF_MAX"]>0, data["INSTAL_DPD_MAX"],(((( np.where(data["AMT_ANNUITY"]<0,(-1.0*(((((data["INSTAL_AMT_INSTALMENT_SUM"])>(np.maximum(((data["AMT_CREDIT"])) ,(( data["PREV_AMT_GOODS_PRICE_MAX"])))))*1.)))), data["NEW_CREDIT_TO_GOODS_RATIO"])) * 2.0)) * 2.0)))
v["i84"] = 0.046006*np.tanh(np.where(data["OWN_CAR_AGE"]>0,(( data["PREV_AMT_CREDIT_MEAN"])-(data["DAYS_BIRTH"])) ,(( data["FLAG_WORK_PHONE"])*(((data["ORGANIZATION_TYPE_Kindergarten"])+(((data["WEEKDAY_APPR_PROCESS_START_SUNDAY"])+(np.maximum(((data["DAYS_BIRTH"])) ,(( data["NEW_ANNUITY_TO_INCOME_RATIO"])))))))))))
v["i85"] = 0.009576*np.tanh(((((np.where(data["EXT_SOURCE_3"] < -99998, data["NEW_EXT_SOURCES_MEAN"], np.where(data["NEW_DOC_IND_KURT"]<0, data["ORGANIZATION_TYPE_Self_employed"],(( data["PREV_NAME_SELLER_INDUSTRY_XNA_MEAN"])*(np.where(data["CC_CNT_DRAWINGS_CURRENT_VAR"]<0, data["CLOSED_DAYS_CREDIT_MEAN"], data["CC_AMT_BALANCE_MEAN"])))))) * 2.0)) * 2.0))
v["i86"] = 0.042600*np.tanh(((((((( data["APPROVED_AMT_APPLICATION_MIN"])<(data["NEW_EXT_SOURCES_MEAN"])) *1.))-(((data["NEW_DOC_IND_AVG"])+(((data["NEW_EXT_SOURCES_MEAN"])-(np.tanh(((((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)) * 2.0)))))))))) -(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])))
v["i87"] = 0.048500*np.tanh(((( -1.0*(( np.where(data["REFUSED_AMT_GOODS_PRICE_MAX"]>0, data["DAYS_LAST_PHONE_CHANGE"],(-1.0*(((((((( data["CC_CNT_DRAWINGS_CURRENT_MAX"])-(np.where(data["BURO_CREDIT_TYPE_Car_loan_MEAN"]<0, data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"], data["POS_MONTHS_BALANCE_MAX"])))) * 2.0)) * 2.0)))))))))* 2.0))
v["i88"] = 0.049749*np.tanh(np.where(((data["PREV_DAYS_DECISION_MEAN"])+(np.tanh(((((( data["PREV_NAME_PAYMENT_TYPE_XNA_MEAN"])+(data["PREV_NAME_PAYMENT_TYPE_XNA_MEAN"])))+(data["REFUSED_AMT_ANNUITY_MIN"])))))) <0, data["NEW_CREDIT_TO_ANNUITY_RATIO"],(-1.0*(((( data["REFUSED_AMT_ANNUITY_MIN"])+(data["NEW_CREDIT_TO_ANNUITY_RATIO"])))))))
v["i89"] = 0.039400*np.tanh(np.where(data["INSTAL_DPD_MEAN"]<0, np.where(data["BURO_AMT_CREDIT_SUM_DEBT_SUM"]<0, np.where(data["ACTIVE_CREDIT_DAY_OVERDUE_MAX"] < -99998, data["PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN"], np.where(data["PREV_AMT_DOWN_PAYMENT_MEAN"]>0, data["INSTAL_AMT_PAYMENT_MIN"], data["PREV_CNT_PAYMENT_SUM"])) , data["ORGANIZATION_TYPE_Business_Entity_Type_3"]), data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"]))
v["i90"] = 0.045801*np.tanh(((((np.where(data["ACTIVE_AMT_CREDIT_SUM_SUM"]>0,(((( data["ACTIVE_AMT_CREDIT_SUM_SUM"])-(data["ACTIVE_AMT_CREDIT_SUM_MAX"])))* 2.0), np.where(data["DEF_60_CNT_SOCIAL_CIRCLE"]<0, data["POS_SK_DPD_DEF_MAX"],(-1.0*(( data["ACTIVE_AMT_CREDIT_SUM_MAX"])))))) +(data["NAME_EDUCATION_TYPE_Lower_secondary"])))* 2.0))
v["i91"] = 0.049800*np.tanh(((((data["PREV_CNT_PAYMENT_MEAN"])-(((( data["ACTIVE_DAYS_CREDIT_ENDDATE_MEAN"])<(np.tanh(( data["PREV_AMT_CREDIT_MEAN"])))) *1.))))*(((((data["PREV_DAYS_DECISION_MAX"])+(data["PREV_NAME_YIELD_GROUP_high_MEAN"])))+(((data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])+(data["OBS_60_CNT_SOCIAL_CIRCLE"])))))))
v["i92"] = 0.049738*np.tanh(np.where(data["NEW_DOC_IND_STD"]>0, np.where(data["CLOSED_MONTHS_BALANCE_SIZE_SUM"] < -99998, data["INSTAL_PAYMENT_DIFF_MAX"],(((( np.where(data["BURO_STATUS_1_MEAN_MEAN"]<0, data["PREV_NAME_CONTRACT_TYPE_Revolving_loans_MEAN"], data["PREV_NAME_PRODUCT_TYPE_XNA_MEAN"])) -(data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])))-(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]))), data["INSTAL_PAYMENT_DIFF_MAX"]))
v["i93"] = 0.049512*np.tanh(((np.maximum(((data["ORGANIZATION_TYPE_Transport__type_3"])) ,(( data["INSTAL_AMT_PAYMENT_MIN"])))) +(((data["NAME_EDUCATION_TYPE_Lower_secondary"])+(((data["INSTAL_AMT_PAYMENT_MIN"])+(((data["ORGANIZATION_TYPE_Construction"])-(np.where(data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"]>0, data["INSTAL_DAYS_ENTRY_PAYMENT_SUM"], data["APPROVED_AMT_CREDIT_MIN"]))))))))))
v["i94"] = 0.047402*np.tanh(np.where(data["NEW_DOC_IND_STD"]<0, data["APPROVED_DAYS_DECISION_MIN"], np.maximum(((data["CC_CNT_DRAWINGS_CURRENT_VAR"])) ,(( np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( np.maximum(((((data["POS_SK_DPD_MAX"])/ 2.0))),(((((data["PREV_NAME_CASH_LOAN_PURPOSE_Medicine_MEAN"])<(((data["NEW_RATIO_PREV_DAYS_DECISION_MAX"])/ 2.0)))*1.))))))))))))
v["i95"] = 0.044002*np.tanh(np.where(data["CC_AMT_PAYMENT_TOTAL_CURRENT_SUM"]>0, data["NEW_RATIO_BURO_MONTHS_BALANCE_MIN_MIN"],(( data["NAME_HOUSING_TYPE_Municipal_apartment"])+(np.maximum(((data["INSTAL_PAYMENT_DIFF_SUM"])) ,(( np.maximum(((data["ORGANIZATION_TYPE_Transport__type_3"])) ,(( np.where(data["REFUSED_AMT_CREDIT_MIN"] < -99998, data["NEW_SCORES_STD"], data["ACTIVE_AMT_CREDIT_SUM_SUM"])))))))))))
v["i96"] = 0.050000*np.tanh(np.where(data["ACTIVE_DAYS_CREDIT_MEAN"]<0, np.where(data["BURO_DAYS_CREDIT_MAX"]>0, data["CC_AMT_BALANCE_VAR"], np.maximum(((data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])) ,(((((data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])<(data["NEW_EXT_SOURCES_MEAN"])) *1.))))) , np.where(data["BURO_AMT_CREDIT_SUM_DEBT_MEAN"]<0, data["POS_NAME_CONTRACT_STATUS_Signed_MEAN"], data["BURO_DAYS_CREDIT_MAX"])))
v["i97"] = 0.048906*np.tanh(((((( np.maximum(((data["INSTAL_DBD_MAX"])) ,(( data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"])))) +(np.where(data["OBS_60_CNT_SOCIAL_CIRCLE"]<0, data["ORGANIZATION_TYPE_Transport__type_3"],(( data["ACTIVE_DAYS_CREDIT_ENDDATE_MAX"])+(data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"])))))/2.0)) -(((( data["NEW_RATIO_PREV_AMT_CREDIT_MIN"])>(data["PREV_PRODUCT_COMBINATION_POS_household_without_interest_MEAN"])) *1.))))
v["i98"] = 0.049860*np.tanh(((np.where(data["CODE_GENDER"]<0, data["NEW_ANNUITY_TO_INCOME_RATIO"], np.where(((data["DAYS_BIRTH"])-(data["NEW_DOC_IND_AVG"])) <0, data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"], data["DAYS_ID_PUBLISH"])))-(((( data["DAYS_BIRTH"])+(((data["OCCUPATION_TYPE_Medicine_staff"])* 2.0)))/2.0))))
v["i99"] = 0.041003*np.tanh(np.maximum(((data["BURO_STATUS_1_MEAN_MEAN"])) ,(((( data["DAYS_REGISTRATION"])*(((((data["ACTIVE_DAYS_CREDIT_MIN"])-(data["INSTAL_AMT_INSTALMENT_MAX"])))*(np.where(((data["BURO_CREDIT_ACTIVE_Closed_MEAN"])+(data["DAYS_REGISTRATION"])) < -99998, data["APPROVED_AMT_CREDIT_MAX"], data["PREV_AMT_CREDIT_MIN"])))))))))
v["i100"] = 0.049800*np.tanh(np.where(data["EXT_SOURCE_3"] < -99998, np.where(data["PREV_AMT_APPLICATION_MEAN"] < -99998, data["PREV_NAME_PAYMENT_TYPE_XNA_MEAN"],(( data["NEW_EXT_SOURCES_MEAN"])* 2.0)) ,(( data["EXT_SOURCE_3"])*(((((((data["INSTAL_PAYMENT_DIFF_MAX"])+(data["NEW_EXT_SOURCES_MEAN"])) /2.0)) +(data["INSTAL_PAYMENT_DIFF_MEAN"])) /2.0)))))
v["i101"] = 0.039360*np.tanh(((((data["PREV_NAME_CASH_LOAN_PURPOSE_XNA_MEAN"])*(np.maximum(((((( data["BURO_CREDIT_TYPE_Credit_card_MEAN"])+(data["ORGANIZATION_TYPE_Military"])) /2.0))),(( np.where(((data["BURO_CREDIT_TYPE_Credit_card_MEAN"])*(data["PREV_CNT_PAYMENT_MEAN"])) < -99998, data["PREV_CNT_PAYMENT_SUM"], data["REFUSED_RATE_DOWN_PAYMENT_MAX"])))))))* 2.0))
v["i102"] = 0.047204*np.tanh(( -1.0*(( np.maximum(((((data["YEARS_BUILD_MEDI"])-(data["NEW_DOC_IND_AVG"])))) ,(((( data["OCCUPATION_TYPE_High_skill_tech_staff"])+(np.maximum(((data["CLOSED_AMT_CREDIT_SUM_MEAN"])) ,(( np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])) ,(((( data["PREV_CHANNEL_TYPE_Channel_of_corporate_sales_MEAN"])+(data["OCCUPATION_TYPE_High_skill_tech_staff"])))))))))))))))))
v["i103"] = 0.049790*np.tanh(np.where(data["PREV_NAME_GOODS_CATEGORY_Audio_Video_MEAN"]>0, data["INSTAL_PAYMENT_DIFF_SUM"], np.where(data["ACTIVE_MONTHS_BALANCE_MAX_MAX"]>0, data["NEW_RATIO_BURO_AMT_ANNUITY_MAX"],(-1.0*(((( data["ORGANIZATION_TYPE_Medicine"])+(np.maximum(((data["BURO_CREDIT_TYPE_Mortgage_MEAN"])) ,(((((data["CC_AMT_PAYMENT_TOTAL_CURRENT_MAX"])>(data["BURO_AMT_CREDIT_SUM_DEBT_MAX"])) *1.))))))))))))
v["i104"] = 0.049046*np.tanh(( -1.0*(((((data["ORGANIZATION_TYPE_School"])>(np.where(((( data["AMT_GOODS_PRICE"])+(data["NEW_CREDIT_TO_ANNUITY_RATIO"])) /2.0)>0, data["PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN"],(( data["NEW_CREDIT_TO_ANNUITY_RATIO"])+(((( data["NEW_CREDIT_TO_ANNUITY_RATIO"])<(np.tanh(( data["PREV_PRODUCT_COMBINATION_Cash_MEAN"])))) *1.))))))*1.)))))
v["i105"] = 0.049750*np.tanh(np.where(data["AMT_REQ_CREDIT_BUREAU_QRT"]>0, data["DAYS_LAST_PHONE_CHANGE"],(( np.where(data["INSTAL_DPD_MEAN"]>0, data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"], np.maximum(((((data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"])*(data["CC_AMT_BALANCE_MEAN"])))) ,(((( data["PREV_DAYS_DECISION_MAX"])-(data["APPROVED_APP_CREDIT_PERC_MIN"])))))))* 2.0)))
v["i106"] = 0.045968*np.tanh(np.where(((data["DAYS_ID_PUBLISH"])-(data["DAYS_BIRTH"])) <0,(( data["DAYS_ID_PUBLISH"])*(((((( data["CNT_FAM_MEMBERS"])+(((data["INSTAL_DAYS_ENTRY_PAYMENT_SUM"])-(data["DAYS_BIRTH"])))) /2.0)) * 2.0))), data["DAYS_ID_PUBLISH"]))
v["i107"] = 0.049699*np.tanh(((np.maximum(((data["APPROVED_CNT_PAYMENT_SUM"])) ,(( np.where(data["NEW_CAR_TO_EMPLOY_RATIO"] < -99998, data["DAYS_BIRTH"], data["INSTAL_AMT_INSTALMENT_MEAN"])))))-(((data["INSTAL_AMT_PAYMENT_SUM"])+(((( np.minimum(((data["INSTAL_AMT_PAYMENT_SUM"])) ,(( data["PREV_AMT_CREDIT_MAX"])))) <(data["INSTAL_AMT_PAYMENT_SUM"])) *1.))))))
v["i108"] = 0.007000*np.tanh(np.where(np.where(data["CLOSED_DAYS_CREDIT_ENDDATE_MAX"]<0, data["ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN"], data["BURO_AMT_CREDIT_SUM_SUM"])>0,(10.0),(( np.where(data["PREV_NAME_GOODS_CATEGORY_Photo___Cinema_Equipment_MEAN"]<0, np.where(data["NEW_RATIO_PREV_HOUR_APPR_PROCESS_START_MAX"]>0, data["WALLSMATERIAL_MODE_Stone__brick"], data["POS_SK_DPD_DEF_MAX"]), data["NEW_RATIO_PREV_HOUR_APPR_PROCESS_START_MEAN"])) * 2.0)))
v["i109"] = 0.046106*np.tanh(np.where(data["ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN"]<0,(-1.0*(((((( data["NEW_LIVE_IND_SUM"])-(((np.maximum(((data["PREV_AMT_APPLICATION_MIN"])) ,(((( data["BURO_CREDIT_ACTIVE_Sold_MEAN"])+(data["BURO_CREDIT_TYPE_Microloan_MEAN"])))))) * 2.0)))) -(data["POS_SK_DPD_MAX"]))))), data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]))
v["i110"] = 0.042843*np.tanh(( -1.0*(( np.where(data["AMT_INCOME_TOTAL"]>0, data["BURO_AMT_CREDIT_SUM_MEAN"], np.where(data["CC_AMT_RECEIVABLE_PRINCIPAL_VAR"]>0, data["ACTIVE_DAYS_CREDIT_ENDDATE_MAX"], np.where(data["FLAG_WORK_PHONE"]>0, data["ACTIVE_DAYS_CREDIT_ENDDATE_MEAN"], np.where(data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"]>0, data["CC_AMT_DRAWINGS_POS_CURRENT_MAX"], data["NEW_CREDIT_TO_GOODS_RATIO"]))))))))
v["i111"] = 0.049018*np.tanh(np.where(data["BURO_STATUS_X_MEAN_MEAN"] < -99998,(( np.where(data["PREV_NAME_YIELD_GROUP_high_MEAN"]>0, data["FLAG_WORK_PHONE"], np.tanh(( data["LIVINGAREA_AVG"])))) -(((data["EXT_SOURCE_2"])+(data["NEW_INC_BY_ORG"])))) , np.tanh(((( data["EXT_SOURCE_2"])* 2.0)))))
v["i112"] = 0.047002*np.tanh(np.where(data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"]<0, np.where(data["BURO_DAYS_CREDIT_MAX"]<0, np.where(data["NEW_RATIO_PREV_DAYS_DECISION_MIN"]<0, data["INSTAL_PAYMENT_DIFF_SUM"], data["BURO_DAYS_CREDIT_MAX"]), data["ACTIVE_AMT_CREDIT_SUM_SUM"]),(( data["ACTIVE_AMT_CREDIT_SUM_SUM"])+(( -1.0*(( data["NEW_RATIO_PREV_DAYS_DECISION_MIN"])))))))
v["i113"] = 0.040984*np.tanh(np.where(data["REFUSED_DAYS_DECISION_MEAN"]>0,(( data["PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN"])* 2.0), np.maximum(((data["CC_AMT_INST_MIN_REGULARITY_VAR"])) ,(((( np.maximum(((data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"])) ,(( np.where(data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"]>0, data["INSTAL_PAYMENT_DIFF_SUM"], data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])))))* 2.0))))))
v["i114"] = 0.045032*np.tanh(((( np.maximum(((data["BURO_AMT_CREDIT_SUM_DEBT_MAX"])) ,(((( data["BURO_AMT_CREDIT_SUM_MAX"])* 2.0)))))>(np.maximum(((((((data["PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN"])+(((data["BURO_AMT_CREDIT_SUM_MEAN"])* 2.0)))) +(((data["BURO_AMT_CREDIT_SUM_DEBT_MAX"])* 2.0))))),(( data["BURO_AMT_CREDIT_SUM_MEAN"])))))*1.))
v["i115"] = 0.045600*np.tanh(np.where(((( data["NEW_EXT_SOURCES_MEAN"])+(data["NEW_PHONE_TO_BIRTH_RATIO"])) /2.0)>0, data["APARTMENTS_MEDI"], np.where(((( data["POS_COUNT"])>(data["ORGANIZATION_TYPE_Business_Entity_Type_3"])) *1.) >0, data["NEW_EXT_SOURCES_MEAN"],(( data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"])*(data["NEW_PHONE_TO_BIRTH_RATIO"])))))
v["i116"] = 0.042206*np.tanh(((data["DAYS_BIRTH"])*(((((((((((( data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])>(((data["DAYS_BIRTH"])/ 2.0)))*1.))+(data["PREV_CHANNEL_TYPE_Stone_MEAN"])))+(data["PREV_NAME_GOODS_CATEGORY_Consumer_Electronics_MEAN"])))+(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])))+(data["PREV_NAME_GOODS_CATEGORY_Consumer_Electronics_MEAN"])))))
v["i117"] = 0.042064*np.tanh(np.where(data["INSTAL_AMT_INSTALMENT_MEAN"]>0, data["PREV_WEEKDAY_APPR_PROCESS_START_THURSDAY_MEAN"],(( data["PREV_CODE_REJECT_REASON_SCO_MEAN"])*(np.where(data["CC_AMT_DRAWINGS_POS_CURRENT_MAX"] < -99998, data["NEW_EMPLOY_TO_BIRTH_RATIO"], np.where(data["CLOSED_MONTHS_BALANCE_SIZE_MEAN"] < -99998, data["ACTIVE_CREDIT_DAY_OVERDUE_MAX"],(((data["NEW_SCORES_STD"])>(data["CLOSED_MONTHS_BALANCE_SIZE_MEAN"])) *1.)))))))
v["i118"] = 0.046301*np.tanh(np.where(data["AMT_INCOME_TOTAL"]>0, data["NEW_DOC_IND_KURT"], np.where(data["NAME_EDUCATION_TYPE_Higher_education"]>0, data["AMT_CREDIT"], np.minimum(((((((data["DAYS_BIRTH"])* 2.0)) * 2.0))),(((((((data["DAYS_BIRTH"])<(data["PREV_NAME_PORTFOLIO_POS_MEAN"])) *1.))* 2.0)))))))
v["i119"] = 0.049880*np.tanh(np.where(((( -1.0)>(data["AMT_GOODS_PRICE"])) *1.) >0, data["CC_AMT_DRAWINGS_ATM_CURRENT_MAX"],(( np.where(data["ACTIVE_DAYS_CREDIT_ENDDATE_MEAN"]>0, data["INSTAL_AMT_PAYMENT_MAX"],(((((data["WEEKDAY_APPR_PROCESS_START_SUNDAY"])<(((data["NEW_EXT_SOURCES_MEAN"])/ 2.0)))*1.))* 2.0)))* 2.0)))
v["i120"] = 0.048360*np.tanh(np.where(np.tanh(((((data["APPROVED_DAYS_DECISION_MAX"])+(data["PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN"])) /2.0)))<0, data["PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN"], np.where(data["BURO_AMT_CREDIT_SUM_DEBT_SUM"]>0,(((data["APPROVED_DAYS_DECISION_MAX"])+(data["REFUSED_APP_CREDIT_PERC_VAR"])) /2.0),(( data["PREV_PRODUCT_COMBINATION_Cash_MEAN"])-(data["CC_NAME_CONTRACT_STATUS_Active_VAR"])))))
v["i121"] = 0.045502*np.tanh(np.where(np.where(((((data["INSTAL_DPD_MAX"])/ 2.0)) -(data["ORGANIZATION_TYPE_Transport__type_3"])) <0, data["NEW_EXT_SOURCES_MEAN"], data["APPROVED_RATE_DOWN_PAYMENT_MEAN"])<0,(((data["PREV_NAME_PORTFOLIO_Cash_MEAN"])<(((data["NEW_EXT_SOURCES_MEAN"])* 2.0)))*1.) , data["REGION_RATING_CLIENT"]))
v["i122"] = 0.048897*np.tanh(((( data["BURO_DAYS_CREDIT_MAX"])>(((np.minimum(((((data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])-(data["ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN"])))) ,(((( data["BURO_DAYS_CREDIT_MAX"])-(data["ORGANIZATION_TYPE_Transport__type_3"])))))) +(((( data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])<(((data["ORGANIZATION_TYPE_Transport__type_3"])* 2.0)))*1.))))) *1.))
v["i123"] = 0.035168*np.tanh(np.where(data["DAYS_EMPLOYED"]>0, np.minimum(((data["REGION_RATING_CLIENT_W_CITY"])) ,(( data["REGION_POPULATION_RELATIVE"]))),(( np.where(data["HOUR_APPR_PROCESS_START"]<0, data["INSTAL_AMT_PAYMENT_MAX"], np.where(data["CC_AMT_PAYMENT_CURRENT_MEAN"]>0, data["DAYS_EMPLOYED"], data["NEW_DOC_IND_STD"])))* 2.0)))
v["i124"] = 0.048513*np.tanh(np.where(data["INSTAL_DBD_MEAN"]<0, np.where(data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]<0, np.where(data["PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN"]>0,(( data["PREV_NAME_SELLER_INDUSTRY_Consumer_electronics_MEAN"])* 2.0),(((data["CC_AMT_RECIVABLE_VAR"])>(data["INSTAL_COUNT"])) *1.)), data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]),(-1.0*(( data["APPROVED_DAYS_DECISION_MIN"])))))
v["i125"] = 0.004000*np.tanh(( -1.0*(( np.maximum(((np.maximum(((((((((data["ORGANIZATION_TYPE_Industry__type_9"])+(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])) /2.0)) +(data["AMT_GOODS_PRICE"])) /2.0))),(( np.where(data["PREV_AMT_GOODS_PRICE_MIN"]<0, data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"], data["PREV_WEEKDAY_APPR_PROCESS_START_SATURDAY_MEAN"])))))) ,(( data["NAME_HOUSING_TYPE_Office_apartment"])))))))
v["i126"] = 0.028997*np.tanh(np.where(data["INSTAL_PAYMENT_DIFF_MAX"]>0, data["PREV_CODE_REJECT_REASON_XAP_MEAN"],(((( data["PREV_CODE_REJECT_REASON_HC_MEAN"])-(((( data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"])+(((( data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"])>(data["INSTAL_PAYMENT_DIFF_MAX"])) *1.))) /2.0)))) -(((( data["PREV_CHANNEL_TYPE_Channel_of_corporate_sales_MEAN"])+(data["NAME_FAMILY_STATUS_Married"])) /2.0)))))
v["i127"] = 0.002994*np.tanh(( -1.0*(((((((data["PREV_CHANNEL_TYPE_Regional___Local_MEAN"])>(((((( data["PREV_CHANNEL_TYPE_Regional___Local_MEAN"])>(((((-1.0*(( data["WEEKDAY_APPR_PROCESS_START_MONDAY"])))) >(((( data["REFUSED_CNT_PAYMENT_MEAN"])>(data["WEEKDAY_APPR_PROCESS_START_MONDAY"])) *1.))) *1.))) *1.))-(data["AMT_GOODS_PRICE"])))) *1.))* 2.0)))))
v["i128"] = 0.005997*np.tanh(np.where(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"]>0, data["POS_NAME_CONTRACT_STATUS_Active_MEAN"], np.where(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"]<0, data["NEW_DOC_IND_KURT"],(((((( data["REFUSED_DAYS_DECISION_MAX"])>(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])) *1.))>(np.where(data["NAME_CONTRACT_TYPE_Cash_loans"]>0, data["NAME_EDUCATION_TYPE_Secondary___secondary_special"], data["APPROVED_AMT_CREDIT_MEAN"])))*1.))))
v["i129"] = 0.046680*np.tanh(np.where(data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]>0,(14.66528892517089844),(( data["NEW_DOC_IND_KURT"])*(np.where(data["NAME_HOUSING_TYPE_Office_apartment"]>0, -3.0,(((( data["CLOSED_DAYS_CREDIT_MIN"])*(data["POS_COUNT"])))-(data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"])))))))
v["i130"] = 0.019768*np.tanh(np.where(data["APPROVED_APP_CREDIT_PERC_VAR"] < -99998, data["PREV_WEEKDAY_APPR_PROCESS_START_FRIDAY_MEAN"], np.where(data["PREV_NAME_GOODS_CATEGORY_Photo___Cinema_Equipment_MEAN"]<0, np.where(data["APARTMENTS_MEDI"] < -99998, data["APPROVED_CNT_PAYMENT_MEAN"], np.where(np.tanh(( data["PREV_WEEKDAY_APPR_PROCESS_START_FRIDAY_MEAN"])) <0, data["NAME_CONTRACT_TYPE_Cash_loans"], data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"])) , data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])))
v["i131"] = 0.006600*np.tanh(((data["ACTIVE_AMT_CREDIT_SUM_SUM"])*(np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]>0, data["CC_MONTHS_BALANCE_VAR"], np.where(data["CC_AMT_RECEIVABLE_PRINCIPAL_VAR"]>0, data["REFUSED_AMT_CREDIT_MIN"],(((((data["PREV_WEEKDAY_APPR_PROCESS_START_FRIDAY_MEAN"])<(np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_SUM"])) ,(( data["REFUSED_AMT_CREDIT_MIN"])))))*1.))* 2.0))))))
v["i132"] = 0.049586*np.tanh(( -1.0*(( np.where(data["INSTAL_AMT_PAYMENT_MAX"] < -99998, data["YEARS_BEGINEXPLUATATION_MEDI"], np.where(data["INSTAL_AMT_PAYMENT_MAX"]<0, np.maximum(((np.maximum(((data["AMT_CREDIT"])) ,(((((data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"])>(data["CC_AMT_RECIVABLE_VAR"])) *1.)))))),(( data["EXT_SOURCE_2"]))), data["DEF_60_CNT_SOCIAL_CIRCLE"]))))))
v["i133"] = 0.049999*np.tanh(np.where(data["PREV_NAME_CLIENT_TYPE_Refreshed_MEAN"]>0, data["BURO_CREDIT_TYPE_Credit_card_MEAN"],(((( np.where(( -1.0*(((((data["ACTIVE_DAYS_CREDIT_ENDDATE_MAX"])>(data["AMT_REQ_CREDIT_BUREAU_QRT"])) *1.))))<0, data["BURO_DAYS_CREDIT_MAX"],(( data["PREV_NAME_GOODS_CATEGORY_Computers_MEAN"])*(data["BURO_DAYS_CREDIT_MAX"])))) * 2.0)) * 2.0)))
v["i134"] = 0.047500*np.tanh(np.where(data["REFUSED_HOUR_APPR_PROCESS_START_MEAN"]<0, np.where(data["ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN"]>0, 3.0, np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0, data["FLAG_WORK_PHONE"],(-1.0*(( data["FLAG_WORK_PHONE"]))))), np.where(data["ACTIVE_MONTHS_BALANCE_MAX_MAX"]<0, data["PREV_CODE_REJECT_REASON_HC_MEAN"], data["INSTAL_AMT_PAYMENT_MIN"])))
v["i135"] = 0.046005*np.tanh(np.where(data["NEW_DOC_IND_STD"]<0, data["INSTAL_PAYMENT_DIFF_SUM"], np.maximum(((np.maximum(((((data["BURO_STATUS_1_MEAN_MEAN"])*(( -1.0*(( data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"]))))))),(( data["CC_CNT_DRAWINGS_CURRENT_MEAN"]))))),(( np.where(data["REGION_RATING_CLIENT_W_CITY"]<0, data["CLOSED_AMT_CREDIT_SUM_DEBT_SUM"], data["NEW_DOC_IND_STD"]))))))
v["i136"] = 0.045999*np.tanh(np.where(data["CC_AMT_BALANCE_MAX"]<0, np.where(data["POS_SK_DPD_DEF_MEAN"]>0, data["NEW_ANNUITY_TO_INCOME_RATIO"], np.where(data["BURO_CREDIT_TYPE_Car_loan_MEAN"] < -99998,(-1.0*(( data["NEW_CREDIT_TO_INCOME_RATIO"]))),(( data["DEF_30_CNT_SOCIAL_CIRCLE"])*(( -1.0*(( data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__middle_MEAN"]))))))), data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"]))
v["i137"] = 0.049992*np.tanh(((((data["NEW_ANNUITY_TO_INCOME_RATIO"])*(np.tanh(( np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"])) ,(( data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))))))) -(np.maximum(((np.maximum(((data["CC_AMT_PAYMENT_TOTAL_CURRENT_SUM"])) ,(( data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"]))))),(( np.maximum(((data["NEW_SOURCES_PROD"])) ,(( data["INSTAL_AMT_PAYMENT_SUM"])))))))))
v["i138"] = 0.050000*np.tanh(((((( data["NEW_RATIO_PREV_DAYS_DECISION_MAX"])>(data["POS_SK_DPD_MEAN"])) *1.))+(((((data["CC_AMT_DRAWINGS_ATM_CURRENT_SUM"])-(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"])))+(((( data["APPROVED_APP_CREDIT_PERC_MIN"])<(np.where(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"]<0, data["POS_SK_DPD_MEAN"], data["APPROVED_APP_CREDIT_PERC_MIN"])))*1.))))))
v["i139"] = 0.036994*np.tanh(((data["AMT_REQ_CREDIT_BUREAU_QRT"])*(((((data["ORGANIZATION_TYPE_Military"])* 2.0)) -(((( data["AMT_CREDIT"])+(np.maximum(((data["AMT_INCOME_TOTAL"])) ,(((( np.maximum(((((data["INSTAL_DPD_MEAN"])* 2.0))),(( data["CC_AMT_RECIVABLE_VAR"])))) * 2.0)))))) /2.0))))))
v["i140"] = 0.039200*np.tanh(np.where(data["INSTAL_AMT_PAYMENT_MIN"]>0, np.where(data["PREV_RATE_DOWN_PAYMENT_MAX"]<0, data["PREV_CODE_REJECT_REASON_LIMIT_MEAN"], data["INSTAL_AMT_PAYMENT_MIN"]),(( data["NEW_RATIO_BURO_AMT_CREDIT_SUM_LIMIT_MEAN"])*(np.where(data["LIVINGAPARTMENTS_AVG"]>0, data["ACTIVE_DAYS_CREDIT_UPDATE_MEAN"],(((data["PREV_AMT_ANNUITY_MEAN"])>(data["APPROVED_CNT_PAYMENT_MEAN"])) *1.))))))
v["i141"] = 0.044997*np.tanh(np.where(data["PREV_AMT_ANNUITY_MIN"] < -99998, data["NEW_DOC_IND_AVG"],(( data["PREV_NAME_YIELD_GROUP_high_MEAN"])*(((((((data["INSTAL_DBD_SUM"])* 2.0)) * 2.0)) *(((( data["APPROVED_DAYS_DECISION_MEAN"])<(np.maximum(((data["AMT_INCOME_TOTAL"])) ,(( data["INSTAL_DBD_SUM"])))))*1.)))))))
v["i142"] = 0.044000*np.tanh(((data["DAYS_ID_PUBLISH"])*(np.where(data["POS_MONTHS_BALANCE_MEAN"]<0, data["NEW_RATIO_PREV_DAYS_DECISION_MIN"], np.where(data["INSTAL_AMT_PAYMENT_MIN"]<0, data["CC_AMT_BALANCE_VAR"],(( data["FLOORSMIN_MODE"])*(data["NEW_RATIO_PREV_APP_CREDIT_PERC_MIN"])))))))
v["i143"] = 0.017800*np.tanh(np.where(data["LANDAREA_MEDI"]>0, data["CC_MONTHS_BALANCE_VAR"],(((data["DAYS_ID_PUBLISH"])+(np.where(data["REFUSED_CNT_PAYMENT_MEAN"]<0,(-1.0*(( np.maximum(((data["ORGANIZATION_TYPE_Industry__type_9"])) ,(( data["ORGANIZATION_TYPE_School"])))))) ,(-1.0*(( data["BASEMENTAREA_MODE"])))))) /2.0)))
v["i144"] = 0.049495*np.tanh(((((((((( data["DAYS_BIRTH"])*(data["NAME_FAMILY_STATUS_Married"])))+(((((np.minimum(((((data["DAYS_BIRTH"])*(data["CODE_GENDER"])))) ,(((-1.0*(( data["BURO_CREDIT_TYPE_Car_loan_MEAN"])))))))* 2.0)) * 2.0)))/2.0)) * 2.0)) * 2.0))
v["i145"] = 0.047997*np.tanh(((np.maximum(((data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"])) ,(((((data["NAME_FAMILY_STATUS_Married"])<(np.maximum(((data["ACTIVE_DAYS_CREDIT_MAX"])) ,(( np.where(data["APPROVED_AMT_APPLICATION_MAX"]>0, np.where(data["ACTIVE_DAYS_CREDIT_MAX"]>0, data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"], data["APPROVED_AMT_APPLICATION_MAX"]), data["CLOSED_AMT_CREDIT_SUM_DEBT_MAX"])))))) *1.))))) * 2.0))
v["i146"] = 0.007402*np.tanh(((( -1.0*(( np.where(data["ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN"]>0, data["NEW_RATIO_PREV_RATE_DOWN_PAYMENT_MEAN"], np.where(data["ACTIVE_AMT_CREDIT_SUM_SUM"]>0, np.maximum(((data["ACTIVE_MONTHS_BALANCE_SIZE_MEAN"])) ,(( data["LIVE_CITY_NOT_WORK_CITY"]))),(((data["CLOSED_AMT_CREDIT_SUM_MEAN"])>(np.tanh(( data["DEF_60_CNT_SOCIAL_CIRCLE"])))) *1.))))))) * 2.0))
v["i147"] = 0.034997*np.tanh(np.where(data["INSTAL_AMT_PAYMENT_MIN"]<0,(( data["CODE_GENDER"])*(data["PREV_CNT_PAYMENT_MEAN"])) , np.where(data["CODE_GENDER"]<0,(( data["BURO_DAYS_CREDIT_MEAN"])*(data["PREV_CNT_PAYMENT_MEAN"])) , data["BURO_DAYS_CREDIT_MEAN"])))
v["i148"] = 0.047001*np.tanh(((data["OBS_30_CNT_SOCIAL_CIRCLE"])*(((((np.maximum(((np.where(data["REGION_RATING_CLIENT"]<0,(( np.maximum(((data["ORGANIZATION_TYPE_School"])) ,(( data["REFUSED_CNT_PAYMENT_SUM"])))) * 2.0),(-1.0*(( data["APPROVED_AMT_GOODS_PRICE_MEAN"])))))) ,(( data["INSTAL_AMT_INSTALMENT_SUM"])))) * 2.0)) * 2.0))))
v["i149"] = 0.047705*np.tanh(np.where(data["REGION_POPULATION_RELATIVE"]>0, np.where(data["NEW_INC_BY_ORG"]>0,(( data["NEW_DOC_IND_KURT"])-(data["WEEKDAY_APPR_PROCESS_START_SUNDAY"])) , data["WEEKDAY_APPR_PROCESS_START_WEDNESDAY"]),(((((( data["PREV_PRODUCT_COMBINATION_POS_household_without_interest_MEAN"])* 2.0)) * 2.0)) *(data["NEW_DOC_IND_STD"]))))
return Output(v.sum(axis=1)-2.432490)
def GP2(data):
v = pd.DataFrame()
v["i0"] = 0.040171*np.tanh(((((((np.where(data["EXT_SOURCE_3"] < -99998, np.tanh(( data["DAYS_EMPLOYED"])) ,(((-1.0*(( data["EXT_SOURCE_3"])))) +(np.minimum(((data["DAYS_BIRTH"])) ,(( data["NEW_CREDIT_TO_GOODS_RATIO"])))))))-(data["EXT_SOURCE_2"])))* 2.0)) * 2.0))
v["i1"] = 0.049975*np.tanh(((((((((((-1.0)-(np.where(data["CC_AMT_PAYMENT_CURRENT_VAR"] < -99998,(( data["NEW_EXT_SOURCES_MEAN"])* 2.0),(((((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)) * 2.0)) * 2.0)))))* 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i2"] = 0.040171*np.tanh(((((((((((np.where(data["EXT_SOURCE_2"]>0, data["NEW_RATIO_PREV_DAYS_DECISION_MAX"],(( -2.0)/ 2.0)))-(data["EXT_SOURCE_3"])))-(data["EXT_SOURCE_2"])))* 2.0)) * 2.0)) * 2.0))
v["i3"] = 0.040171*np.tanh(((((((( data["OCCUPATION_TYPE_Laborers"])+(data["NEW_CREDIT_TO_GOODS_RATIO"])) /2.0)) +(((((((data["NEW_EXT_SOURCES_MEAN"])*(-2.0)))+(np.minimum(((data["NEW_CREDIT_TO_GOODS_RATIO"])) ,(((-1.0*(( data["EXT_SOURCE_3"])))))))))* 2.0)))) * 2.0))
v["i4"] = 0.047400*np.tanh(((((data["NEW_EXT_SOURCES_MEAN"])* 2.0)) *(np.minimum(((np.minimum(((data["NEW_RATIO_BURO_DAYS_CREDIT_ENDDATE_MAX"])) ,(((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)))))) ,(((((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)) *(np.minimum(((data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"])) ,(( data["NEW_RATIO_PREV_AMT_DOWN_PAYMENT_MAX"])))))))))))
v["i5"] = 0.040181*np.tanh(((((data["DAYS_BIRTH"])+(((( -1.0*(( np.maximum(((data["NAME_EDUCATION_TYPE_Higher_education"])) ,(( data["EXT_SOURCE_3"])))))))-(((data["EXT_SOURCE_2"])+(np.where(data["EXT_SOURCE_3"] < -99998, data["EXT_SOURCE_2"], data["EXT_SOURCE_3"])))))))) * 2.0))
v["i6"] = 0.047400*np.tanh(((((data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])-(((((((((((data["NEW_EXT_SOURCES_MEAN"])* 2.0)) -(np.tanh(( data["CC_AMT_DRAWINGS_POS_CURRENT_MAX"])))))-(data["NEW_CREDIT_TO_GOODS_RATIO"])))-(np.tanh(( data["DAYS_EMPLOYED"])))))* 2.0)))) * 2.0))
v["i7"] = 0.040171*np.tanh(((((np.tanh(( data["DAYS_EMPLOYED"])))+(((((np.where(data["AMT_ANNUITY"]<0, data["AMT_ANNUITY"], np.tanh(( np.tanh(( data["NEW_CREDIT_TO_GOODS_RATIO"])))))) +(( -1.0*(( data["NEW_EXT_SOURCES_MEAN"])))))) * 2.0)))) * 2.0))
v["i8"] = 0.049660*np.tanh(((((((((( -1.0*(((((((data["DAYS_EMPLOYED"])<(((((data["PREV_AMT_DOWN_PAYMENT_MAX"])* 2.0)) -(data["NEW_CREDIT_TO_GOODS_RATIO"])))) *1.))+(data["NEW_EXT_SOURCES_MEAN"])))))) * 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i9"] = 0.049975*np.tanh(((((data["NEW_CREDIT_TO_GOODS_RATIO"])+(((((np.tanh(((( data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])+(((np.tanh(((((data["DAYS_EMPLOYED"])+(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])) /2.0)))) * 2.0)))))) -(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)))) * 2.0))
v["i10"] = 0.048200*np.tanh(((( -1.0*(((((( data["NEW_EXT_SOURCES_MEAN"])+(((np.where(data["REFUSED_DAYS_DECISION_MAX"]<0, data["NEW_EXT_SOURCES_MEAN"], data["EXT_SOURCE_3"])) +(data["CODE_GENDER"])))))+(np.maximum(((data["APPROVED_APP_CREDIT_PERC_MAX"])) ,(( data["NAME_EDUCATION_TYPE_Higher_education"])))))))))* 2.0))
v["i11"] = 0.050000*np.tanh(((((((data["PREV_NAME_CLIENT_TYPE_New_MEAN"])+(data["PREV_CNT_PAYMENT_MEAN"])))+(data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))+(((data["NEW_CREDIT_TO_GOODS_RATIO"])-(((np.maximum(((data["NEW_EXT_SOURCES_MEAN"])) ,(((( data["NEW_EXT_SOURCES_MEAN"])+(data["BURO_CREDIT_ACTIVE_Closed_MEAN"])))))) * 2.0))))))
v["i12"] = 0.049996*np.tanh(((data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])+(((data["NEW_EXT_SOURCES_MEAN"])*(np.minimum(((data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])) ,(( np.minimum(((((data["CC_CNT_INSTALMENT_MATURE_CUM_SUM"])+(np.minimum(((data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])) ,(((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)))))))) ,(( data["NEW_EXT_SOURCES_MEAN"])))))))))))
v["i13"] = 0.049540*np.tanh(((((((np.maximum(((data["DAYS_EMPLOYED"])) ,(( data["APPROVED_APP_CREDIT_PERC_MAX"])))) -(data["NEW_EXT_SOURCES_MEAN"])))+(((( -1.0*(((( data["APPROVED_APP_CREDIT_PERC_MAX"])-(data["PREV_NAME_PRODUCT_TYPE_walk_in_MEAN"])))))) +(np.tanh(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])))))))* 2.0))
v["i14"] = 0.049550*np.tanh(((np.tanh(( data["PREV_CNT_PAYMENT_MEAN"])))+(((((data["NEW_CREDIT_TO_GOODS_RATIO"])-(data["CODE_GENDER"])))-(((((data["NEW_EXT_SOURCES_MEAN"])* 2.0)) +(np.maximum(((data["NAME_EDUCATION_TYPE_Higher_education"])) ,(( data["NEW_CREDIT_TO_ANNUITY_RATIO"]))))))))))
v["i15"] = 0.048505*np.tanh(((( -1.0*(((( data["NEW_EXT_SOURCES_MEAN"])-(((((np.tanh(( data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))+(np.maximum(((data["DAYS_EMPLOYED"])) ,(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])))))) +(((data["INSTAL_PAYMENT_DIFF_MAX"])-(data["APPROVED_AMT_ANNUITY_MAX"])))))))))) * 2.0))
v["i16"] = 0.049643*np.tanh(((data["FLAG_DOCUMENT_3"])+(( -1.0*(((( data["CODE_GENDER"])+(((((((data["NEW_EXT_SOURCES_MEAN"])+(((( data["CC_CNT_DRAWINGS_ATM_CURRENT_SUM"])<(data["NEW_CAR_TO_EMPLOY_RATIO"])) *1.))))* 2.0)) +(data["BURO_CREDIT_ACTIVE_Closed_MEAN"]))))))))))
v["i17"] = 0.049376*np.tanh(((((((data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])+(np.where(data["NEW_DOC_IND_KURT"]>0, data["DAYS_EMPLOYED"], data["NEW_DOC_IND_KURT"])))) -(((((( data["NEW_EXT_SOURCES_MEAN"])+(((data["APPROVED_RATE_DOWN_PAYMENT_MAX"])-(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])))) /2.0)) * 2.0)))) * 2.0))
v["i18"] = 0.049800*np.tanh(((((((((data["PREV_CNT_PAYMENT_MEAN"])-(((( data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])<(data["EXT_SOURCE_2"])) *1.))))-(np.where(data["DAYS_EMPLOYED"]>0, data["EXT_SOURCE_1"],(((data["PREV_DAYS_DECISION_MIN"])<(data["EXT_SOURCE_3"])) *1.))))) * 2.0)) * 2.0))
v["i19"] = 0.049904*np.tanh(((((data["NEW_DOC_IND_KURT"])-(((data["NEW_EXT_SOURCES_MEAN"])-(np.where(data["INSTAL_AMT_PAYMENT_MIN"]<0, data["PREV_DAYS_DECISION_MIN"], data["NEW_RATIO_PREV_DAYS_DECISION_MAX"])))))) -(((data["POS_COUNT"])-(((data["APPROVED_CNT_PAYMENT_MEAN"])-(data["INSTAL_AMT_PAYMENT_MIN"])))))))
v["i20"] = 0.049787*np.tanh(((((((((((((data["NEW_ANNUITY_TO_INCOME_RATIO"])-(data["CODE_GENDER"])))+(((data["NEW_CREDIT_TO_GOODS_RATIO"])+(data["PREV_NAME_YIELD_GROUP_high_MEAN"])))))-(data["PREV_CODE_REJECT_REASON_XAP_MEAN"])))+(data["DEF_30_CNT_SOCIAL_CIRCLE"])))-(data["NEW_EXT_SOURCES_MEAN"])))* 2.0))
v["i21"] = 0.049800*np.tanh(((np.where(data["PREV_AMT_DOWN_PAYMENT_MAX"]>0, data["CC_AMT_BALANCE_MEAN"],(-1.0*(((((((( data["EXT_SOURCE_3"])* 2.0)) -(np.minimum(((( -1.0*(( data["CODE_GENDER"]))))),(( data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])))))) -(data["REGION_RATING_CLIENT"])))))))* 2.0))
v["i22"] = 0.049000*np.tanh(((((((data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])+(((data["PREV_NAME_CLIENT_TYPE_New_MEAN"])* 2.0)))) +(((((data["INSTAL_PAYMENT_DIFF_MAX"])-(data["NEW_EXT_SOURCES_MEAN"])))+(data["INSTAL_PAYMENT_DIFF_MEAN"])))))+(((data["PREV_CNT_PAYMENT_MEAN"])+(data["PREV_NAME_YIELD_GROUP_XNA_MEAN"])))))
v["i23"] = 0.049944*np.tanh(((((np.where(((data["EXT_SOURCE_3"])-(data["NAME_INCOME_TYPE_Working"])) >0, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"], np.where(data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"]>0, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"], np.where(data["POS_MONTHS_BALANCE_SIZE"]>0, data["REFUSED_CNT_PAYMENT_SUM"], data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])))) * 2.0)) * 2.0))
v["i24"] = 0.050000*np.tanh(((((((np.where(data["INSTAL_DPD_MEAN"]<0,(((data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])+(((data["CC_CNT_INSTALMENT_MATURE_CUM_SUM"])*(data["NEW_EXT_SOURCES_MEAN"])))) /2.0),(-1.0*(( data["NEW_SOURCES_PROD"])))))* 2.0)) * 2.0)) * 2.0))
v["i25"] = 0.048001*np.tanh(((((((np.where(data["NEW_CAR_TO_BIRTH_RATIO"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"], np.maximum(((data["BURO_CREDIT_ACTIVE_Active_MEAN"])) ,(( np.maximum(((data["REG_CITY_NOT_LIVE_CITY"])) ,(( np.where(data["INSTAL_DPD_MEAN"]>0, data["INSTAL_DPD_MEAN"], data["CC_CNT_DRAWINGS_ATM_CURRENT_VAR"])))))))))* 2.0)) * 2.0)) * 2.0))
v["i26"] = 0.049758*np.tanh(((data["REGION_RATING_CLIENT_W_CITY"])+(((data["NEW_CREDIT_TO_GOODS_RATIO"])+(((((((((data["PREV_CNT_PAYMENT_SUM"])-(np.maximum(((data["POS_COUNT"])) ,(( data["EXT_SOURCE_1"])))))) * 2.0)) * 2.0)) +(data["NAME_INCOME_TYPE_Working"])))))))
v["i27"] = 0.049720*np.tanh(((((data["NEW_CREDIT_TO_GOODS_RATIO"])-(data["CODE_GENDER"])))-(((3.0)*(((data["PREV_AMT_ANNUITY_MIN"])+(((((data["POS_MONTHS_BALANCE_SIZE"])-(data["INSTAL_PAYMENT_DIFF_MAX"])))-(data["INSTAL_PAYMENT_DIFF_MAX"])))))))))
v["i28"] = 0.049994*np.tanh(((data["FLAG_WORK_PHONE"])+(((((((data["DEF_30_CNT_SOCIAL_CIRCLE"])+(np.maximum(((data["PREV_CNT_PAYMENT_MEAN"])) ,(( data["NEW_CREDIT_TO_GOODS_RATIO"])))))) +(np.where(data["CC_AMT_BALANCE_SUM"] < -99998, data["NEW_ANNUITY_TO_INCOME_RATIO"], data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"])))) * 2.0))))
v["i29"] = 0.049830*np.tanh(np.where(data["EXT_SOURCE_1"] < -99998,(( data["DAYS_BIRTH"])-(np.where(data["CODE_GENDER"]<0, data["NEW_CAR_TO_EMPLOY_RATIO"], data["PREV_NAME_YIELD_GROUP_low_action_MEAN"]))),(((( data["NEW_ANNUITY_TO_INCOME_RATIO"])-(data["NEW_EXT_SOURCES_MEAN"])))-(data["DAYS_BIRTH"]))))
v["i30"] = 0.048802*np.tanh(((((data["INSTAL_PAYMENT_DIFF_MAX"])-(((data["INSTAL_DBD_SUM"])-(data["REGION_RATING_CLIENT_W_CITY"])))))-(((np.maximum(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])) ,(( data["PREV_RATE_DOWN_PAYMENT_MAX"])))) -(((((data["INSTAL_AMT_INSTALMENT_MAX"])-(data["APPROVED_AMT_ANNUITY_MEAN"])))* 2.0))))))
v["i31"] = 0.049376*np.tanh(((((((data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])-(data["CC_MONTHS_BALANCE_VAR"])))+(((((data["INSTAL_PAYMENT_DIFF_MAX"])-(data["APPROVED_AMT_ANNUITY_MEAN"])))+(((data["AMT_ANNUITY"])-(np.maximum(((data["PREV_NAME_YIELD_GROUP_low_action_MEAN"])) ,(( data["NAME_INCOME_TYPE_State_servant"])))))))))) * 2.0))
v["i32"] = 0.049495*np.tanh(np.where(data["NEW_CAR_TO_EMPLOY_RATIO"]>0,(( data["REFUSED_DAYS_DECISION_MIN"])* 2.0),(((((( np.maximum(((data["DEF_60_CNT_SOCIAL_CIRCLE"])) ,(( np.maximum(((data["APPROVED_CNT_PAYMENT_MEAN"])) ,(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])))))))+(( -1.0*(( data["CODE_GENDER"])))))) * 2.0)) * 2.0)))
v["i33"] = 0.046499*np.tanh(((((((((((np.maximum(((data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])) ,(((-1.0*(( np.maximum(((((( data["NEW_EXT_SOURCES_MEAN"])+(((data["NAME_FAMILY_STATUS_Married"])* 2.0)))/2.0))),(( data["FLOORSMAX_MEDI"])))))))))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i34"] = 0.049768*np.tanh(((np.where(np.maximum(((data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"])) ,(( data["PREV_NAME_YIELD_GROUP_low_action_MEAN"])))>0, data["CC_CNT_DRAWINGS_POS_CURRENT_MEAN"],(((( data["NEW_ANNUITY_TO_INCOME_RATIO"])-(((data["INSTAL_DBD_SUM"])* 2.0)))) +(((data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])-(data["PREV_PRODUCT_COMBINATION_POS_industry_with_interest_MEAN"])))))) * 2.0))
v["i35"] = 0.049610*np.tanh(np.where(data["NEW_CAR_TO_BIRTH_RATIO"]>0, data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"],(((( data["PREV_CNT_PAYMENT_MEAN"])-(data["OCCUPATION_TYPE_Core_staff"])))-(((data["PREV_AMT_ANNUITY_MEAN"])-(((data["DAYS_ID_PUBLISH"])-(((data["PREV_NAME_YIELD_GROUP_low_normal_MEAN"])-(data["PREV_NAME_YIELD_GROUP_high_MEAN"]))))))))))
v["i36"] = 0.048788*np.tanh(((((((((data["APPROVED_CNT_PAYMENT_MEAN"])-(((data["POS_MONTHS_BALANCE_MAX"])-(data["PREV_DAYS_DECISION_MEAN"])))))-(data["APPROVED_AMT_ANNUITY_MEAN"])))-(((data["INSTAL_AMT_PAYMENT_MIN"])-(np.minimum(((data["AMT_ANNUITY"])) ,(( data["NEW_DOC_IND_KURT"])))))))) * 2.0))
v["i37"] = 0.049586*np.tanh(((((np.where(np.maximum(((np.maximum(((np.maximum(((data["INSTAL_DPD_MEAN"])) ,(( np.maximum(((data["POS_NAME_CONTRACT_STATUS_Returned_to_the_store_MEAN"])) ,(( data["REG_CITY_NOT_LIVE_CITY"])))))))) ,(( data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"]))))),(( data["BURO_AMT_CREDIT_SUM_DEBT_MAX"])))<0, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"],(9.0)))* 2.0)) * 2.0))
v["i38"] = 0.049900*np.tanh(((((((((np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]>0,(-1.0*(( data["NEW_CREDIT_TO_ANNUITY_RATIO"]))),(( data["NEW_CREDIT_TO_ANNUITY_RATIO"])+(((data["FLAG_DOCUMENT_3"])* 2.0)))))* 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i39"] = 0.049965*np.tanh(((((data["DAYS_LAST_PHONE_CHANGE"])-(np.maximum(((((data["CODE_GENDER"])-(data["DAYS_REGISTRATION"])))) ,(( data["APPROVED_HOUR_APPR_PROCESS_START_MAX"])))))) -(np.where(data["POS_SK_DPD_DEF_MAX"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_LIMIT_MEAN"], np.maximum(((data["NAME_INCOME_TYPE_State_servant"])) ,(( data["BURO_CREDIT_TYPE_Mortgage_MEAN"])))))))
v["i40"] = 0.047400*np.tanh(((((( -1.0*(( np.where(np.where(data["POS_SK_DPD_DEF_MEAN"]<0, data["PREV_APP_CREDIT_PERC_MEAN"], data["CC_AMT_DRAWINGS_POS_CURRENT_VAR"])<0, data["LIVINGAREA_AVG"],(((((data["DEF_30_CNT_SOCIAL_CIRCLE"])* 2.0)) <(np.tanh(( data["INSTAL_DBD_SUM"])))) *1.))))))* 2.0)) * 2.0))
v["i41"] = 0.047999*np.tanh(((((data["APPROVED_DAYS_DECISION_MIN"])+(data["INSTAL_PAYMENT_DIFF_MEAN"])))+(((((((data["APPROVED_CNT_PAYMENT_MEAN"])-(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])))* 2.0)) +(((data["ORGANIZATION_TYPE_Self_employed"])+(np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN"]))))))))))
v["i42"] = 0.049985*np.tanh(np.where(data["NAME_EDUCATION_TYPE_Higher_education"]>0, data["CC_CNT_DRAWINGS_POS_CURRENT_MIN"],(((( data["APPROVED_CNT_PAYMENT_SUM"])-(((data["PREV_NAME_YIELD_GROUP_low_action_MEAN"])+(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])))))-(((data["POS_COUNT"])+(((( data["POS_COUNT"])>(data["AMT_ANNUITY"])) *1.)))))))
v["i43"] = 0.049000*np.tanh(((np.where(np.where(((((data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])* 2.0)) +(np.maximum(((data["ACTIVE_DAYS_CREDIT_MAX"])) ,(( data["NEW_EXT_SOURCES_MEAN"])))))<0, data["NEW_EXT_SOURCES_MEAN"], data["ACTIVE_DAYS_CREDIT_MAX"])<0, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"],(-1.0*(( data["NEW_SOURCES_PROD"])))))* 2.0))
v["i44"] = 0.033210*np.tanh(np.where(((( data["BURO_AMT_CREDIT_SUM_LIMIT_MEAN"])+(data["BURO_CREDIT_TYPE_Mortgage_MEAN"])) /2.0)>0, data["CC_CNT_DRAWINGS_CURRENT_MAX"],(( data["AMT_ANNUITY"])+(((np.where(data["CC_CNT_DRAWINGS_CURRENT_MAX"]>0, data["CC_CNT_DRAWINGS_CURRENT_MAX"],(( data["AMT_ANNUITY"])-(data["APPROVED_AMT_ANNUITY_MEAN"])))) * 2.0)))))
v["i45"] = 0.046000*np.tanh(((data["BURO_CREDIT_ACTIVE_Active_MEAN"])*(((((np.where(data["BURO_CREDIT_ACTIVE_Active_MEAN"]<0,(( np.where(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"] < -99998, data["BURO_DAYS_CREDIT_MAX"], np.maximum(((data["INSTAL_COUNT"])) ,(( data["ACTIVE_DAYS_CREDIT_MAX"])))))* 2.0), data["ACTIVE_DAYS_CREDIT_MAX"])) * 2.0)) * 2.0))))
v["i46"] = 0.049161*np.tanh(((((((np.maximum(((((((np.maximum(((data["PREV_PRODUCT_COMBINATION_Cash_Street__high_MEAN"])) ,(( data["ORGANIZATION_TYPE_Construction"])))) +(np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["INSTAL_PAYMENT_DIFF_MEAN"])))))) +(data["DEF_60_CNT_SOCIAL_CIRCLE"])))) ,(( data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])))) * 2.0)) * 2.0)) * 2.0))
v["i47"] = 0.043843*np.tanh(((np.where(data["ACTIVE_MONTHS_BALANCE_SIZE_MEAN"]>0, data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"],(((( data["OCCUPATION_TYPE_Drivers"])+(np.maximum(((data["INSTAL_DPD_MEAN"])) ,(( data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"])))))) +(((data["FLAG_WORK_PHONE"])-(data["PREV_HOUR_APPR_PROCESS_START_MEAN"])))))) -(data["NEW_EMPLOY_TO_BIRTH_RATIO"])))
v["i48"] = 0.045000*np.tanh(((((((((np.where(data["INSTAL_AMT_PAYMENT_SUM"]>0, data["ACTIVE_AMT_CREDIT_SUM_SUM"], np.where(data["NAME_FAMILY_STATUS_Married"]>0, data["ORGANIZATION_TYPE_Business_Entity_Type_3"],(-1.0*(( data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])))))) * 2.0)) +(data["BURO_CREDIT_TYPE_Microloan_MEAN"])))* 2.0)) +(data["REGION_RATING_CLIENT_W_CITY"])))
v["i49"] = 0.046200*np.tanh(((((np.where(data["INSTAL_DAYS_ENTRY_PAYMENT_SUM"]<0, data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"],(( np.maximum(((data["NAME_EDUCATION_TYPE_Lower_secondary"])) ,(( data["PREV_CODE_REJECT_REASON_SCOFR_MEAN"])))) +(((data["ORGANIZATION_TYPE_Self_employed"])+(((( data["INSTAL_DPD_MEAN"])>(data["ORGANIZATION_TYPE_Military"])) *1.))))))) * 2.0)) * 2.0))
v["i50"] = 0.048723*np.tanh(np.where(np.where(data["NEW_EXT_SOURCES_MEAN"]<0, np.where(data["POS_SK_DPD_DEF_MAX"]<0, data["EXT_SOURCE_3"], data["POS_SK_DPD_DEF_MAX"]), data["POS_SK_DPD_DEF_MAX"])< -99998, -3.0,(((( -2.0)-(data["EXT_SOURCE_3"])))-(data["BURO_DAYS_CREDIT_VAR"]))))
v["i51"] = 0.032400*np.tanh(np.where(((( data["REGION_RATING_CLIENT_W_CITY"])<(data["AMT_REQ_CREDIT_BUREAU_QRT"])) *1.) >0, data["NEW_RATIO_PREV_AMT_DOWN_PAYMENT_MEAN"], np.where(data["ACTIVE_MONTHS_BALANCE_MAX_MAX"]>0, data["AMT_ANNUITY"], np.where(data["APPROVED_AMT_ANNUITY_MAX"]>0, data["APPROVED_CNT_PAYMENT_SUM"],(( data["OWN_CAR_AGE"])*(data["OCCUPATION_TYPE_Core_staff"]))))))
v["i52"] = 0.048728*np.tanh(((((((( -1.0*(((((( data["PREV_AMT_ANNUITY_MEAN"])-(((( data["INSTAL_AMT_PAYMENT_SUM"])<(data["PREV_AMT_GOODS_PRICE_MAX"])) *1.))))-(np.where(data["PREV_AMT_GOODS_PRICE_MAX"]<0, data["AMT_CREDIT"], data["INSTAL_AMT_PAYMENT_MAX"])))))))* 2.0)) * 2.0)) * 2.0))
v["i53"] = 0.042920*np.tanh(((np.where(data["EXT_SOURCE_3"] < -99998, data["NEW_RATIO_PREV_DAYS_DECISION_MAX"],(((((((( data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])-(data["BURO_AMT_CREDIT_SUM_MEAN"])))* 2.0)) * 2.0)) -(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])))) -(((data["BURO_CREDIT_TYPE_Mortgage_MEAN"])-(data["FLAG_WORK_PHONE"])))))
v["i54"] = 0.049915*np.tanh(((np.minimum(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])) ,(((((-1.0*(( data["NEW_CREDIT_TO_ANNUITY_RATIO"])))) * 2.0)))))+(np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( np.where(data["ACTIVE_MONTHS_BALANCE_SIZE_MEAN"] < -99998, np.maximum(((data["INSTAL_PAYMENT_DIFF_MEAN"])) ,(( data["NEW_CREDIT_TO_GOODS_RATIO"]))), data["BURO_CREDIT_TYPE_Microloan_MEAN"])))))))
v["i55"] = 0.048997*np.tanh(((np.maximum(((data["APPROVED_CNT_PAYMENT_SUM"])) ,(((((((data["INSTAL_PAYMENT_PERC_SUM"])<(data["POS_SK_DPD_DEF_MAX"])) *1.))* 2.0)))))-(np.maximum(((np.maximum(((np.maximum(((data["ORGANIZATION_TYPE_School"])) ,(( data["PREV_NAME_YIELD_GROUP_low_action_MEAN"]))))),(( data["PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN"]))))),(( data["CODE_GENDER"]))))))
v["i56"] = 0.049560*np.tanh(((((((((np.maximum(((((data["NEW_EXT_SOURCES_MEAN"])*(data["PREV_PRODUCT_COMBINATION_Cash_Street__middle_MEAN"])))) ,(((((data["NEW_EXT_SOURCES_MEAN"])>(data["PREV_NAME_CASH_LOAN_PURPOSE_Urgent_needs_MEAN"])) *1.))))) -(((data["NEW_DOC_IND_STD"])-(data["PREV_PRODUCT_COMBINATION_Cash_Street__middle_MEAN"])))))* 2.0)) * 2.0)) * 2.0))
v["i57"] = 0.049800*np.tanh(((((np.where(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"]<0,(((( data["AMT_ANNUITY"])-(data["PREV_NAME_GOODS_CATEGORY_Furniture_MEAN"])))-(data["NEW_DOC_IND_STD"])) , data["APPROVED_CNT_PAYMENT_MEAN"])) -(np.where(data["APPROVED_CNT_PAYMENT_MEAN"]<0, data["INSTAL_AMT_PAYMENT_SUM"], data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])))) * 2.0))
v["i58"] = 0.047195*np.tanh(((data["REGION_RATING_CLIENT_W_CITY"])+(((data["NEW_SCORES_STD"])+(((((((data["ORGANIZATION_TYPE_Construction"])+(data["NAME_FAMILY_STATUS_Separated"])))+(np.maximum(((np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"]))))),(( data["CC_AMT_BALANCE_MIN"])))))) * 2.0))))))
v["i59"] = 0.049000*np.tanh(((data["PREV_CNT_PAYMENT_MEAN"])-(((((((data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])* 2.0)) -(data["PREV_NAME_PAYMENT_TYPE_XNA_MEAN"])))-(((((((((((data["INSTAL_DPD_MEAN"])* 2.0)) * 2.0)) * 2.0)) * 2.0)) *(data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"])))))))
v["i60"] = 0.048607*np.tanh(((data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__high_MEAN"])+(((((np.minimum(((data["POS_SK_DPD_DEF_MAX"])) ,(((-1.0*(( data["BURO_AMT_CREDIT_SUM_MEAN"])))))))-(((np.where(data["NEW_RATIO_BURO_MONTHS_BALANCE_SIZE_MEAN"] < -99998, data["EXT_SOURCE_2"], data["AMT_REQ_CREDIT_BUREAU_QRT"])) / 2.0)))) -(data["INSTAL_DBD_SUM"])))))
v["i61"] = 0.049003*np.tanh(((((((((((np.where(data["NEW_DOC_IND_KURT"]>0, np.where(data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"]<0,(( data["CC_AMT_RECEIVABLE_PRINCIPAL_MEAN"])-(data["CC_AMT_PAYMENT_TOTAL_CURRENT_SUM"])) , data["ACTIVE_DAYS_CREDIT_MEAN"]), data["INSTAL_PAYMENT_DIFF_MAX"])) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i62"] = 0.029562*np.tanh(((np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]>0, data["CC_AMT_RECIVABLE_MIN"], np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_MIN"] < -99998, data["DAYS_LAST_PHONE_CHANGE"],(( np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( np.maximum(((data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])) ,(( data["CC_AMT_RECIVABLE_MIN"])))))))+(data["REGION_POPULATION_RELATIVE"])))))* 2.0))
v["i63"] = 0.049969*np.tanh(np.where(data["EXT_SOURCE_1"] < -99998, np.maximum(((( -1.0*(( data["CODE_GENDER"]))))),(( data["DAYS_BIRTH"]))), np.where(data["ACTIVE_DAYS_CREDIT_VAR"] < -99998, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"], data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])))
v["i64"] = 0.034802*np.tanh(((((data["APPROVED_AMT_APPLICATION_MAX"])-(np.where(data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]>0, -1.0,(((data["POS_COUNT"])>(data["APPROVED_CNT_PAYMENT_SUM"])) *1.))))) -(np.maximum(((((((data["INSTAL_AMT_INSTALMENT_MEAN"])* 2.0)) * 2.0))),(( data["NEW_CAR_TO_EMPLOY_RATIO"]))))))
v["i65"] = 0.048998*np.tanh(((((((( data["APPROVED_AMT_GOODS_PRICE_MIN"])<(data["NEW_EXT_SOURCES_MEAN"])) *1.))+(((((data["NEW_EXT_SOURCES_MEAN"])*(np.maximum(((data["POS_MONTHS_BALANCE_SIZE"])) ,(( data["CLOSED_MONTHS_BALANCE_MIN_MIN"])))))) -(np.maximum(((data["NEW_DOC_IND_STD"])) ,(( data["DAYS_BIRTH"])))))))) * 2.0))
v["i66"] = 0.046998*np.tanh(((((np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]>0, data["APPROVED_CNT_PAYMENT_SUM"], np.where(data["INSTAL_AMT_PAYMENT_SUM"]>0, data["OCCUPATION_TYPE_Laborers"],(((data["INSTAL_AMT_PAYMENT_SUM"])<(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])/ 2.0)))*1.))))-(np.tanh(( data["APPROVED_APP_CREDIT_PERC_MIN"])))))* 2.0))
v["i67"] = 0.039784*np.tanh(np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_MAX"],(( np.maximum(((data["OCCUPATION_TYPE_Drivers"])) ,(( np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["CC_CNT_DRAWINGS_CURRENT_MAX"])))))))+(np.where(data["DAYS_LAST_PHONE_CHANGE"]>0, data["ORGANIZATION_TYPE_Business_Entity_Type_3"], data["AMT_ANNUITY"])))))
v["i68"] = 0.049100*np.tanh(np.where(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_SUM"]<0,(((((((((((( np.tanh(( data["NEW_CREDIT_TO_GOODS_RATIO"])))* 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) *(((data["AMT_ANNUITY"])-(data["OCCUPATION_TYPE_High_skill_tech_staff"])))) , data["CC_AMT_RECEIVABLE_PRINCIPAL_VAR"]))
v["i69"] = 0.037440*np.tanh(np.where(data["BURO_CREDIT_TYPE_Car_loan_MEAN"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"],(( np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( np.maximum(((data["ORGANIZATION_TYPE_Transport__type_3"])) ,(( np.maximum(((data["INSTAL_AMT_PAYMENT_MAX"])) ,(( data["CC_CNT_DRAWINGS_CURRENT_VAR"])))))))))) -(np.maximum(((data["CC_AMT_PAYMENT_TOTAL_CURRENT_SUM"])) ,(( data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])))))))
v["i70"] = 0.049961*np.tanh(((((np.where(data["APPROVED_CNT_PAYMENT_SUM"] < -99998, data["NEW_DOC_IND_STD"],(( data["APPROVED_CNT_PAYMENT_SUM"])-(np.maximum(((data["REFUSED_HOUR_APPR_PROCESS_START_MIN"])) ,(( np.maximum(((data["CC_AMT_PAYMENT_CURRENT_MEAN"])) ,(( np.maximum(((data["FLOORSMIN_MEDI"])) ,(( data["POS_COUNT"])))))))))))))* 2.0)) * 2.0))
v["i71"] = 0.049982*np.tanh(((data["POS_SK_DPD_MAX"])+(((((((((((data["POS_SK_DPD_DEF_MAX"])+(((data["NAME_EDUCATION_TYPE_Lower_secondary"])+(((( data["OCCUPATION_TYPE_Core_staff"])<(((data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])* 2.0)))*1.))))))* 2.0)) * 2.0)) * 2.0)) * 2.0))))
v["i72"] = 0.049400*np.tanh(((((( data["NEW_EXT_SOURCES_MEAN"])>(data["INSTAL_PAYMENT_PERC_MEAN"])) *1.))-(((((data["AMT_REQ_CREDIT_BUREAU_YEAR"])+(((data["ORGANIZATION_TYPE_Military"])+(data["BURO_CREDIT_TYPE_Mortgage_MEAN"])))))-(np.where(data["INSTAL_PAYMENT_PERC_MEAN"]<0, data["NEW_DOC_IND_KURT"], data["NEW_RATIO_BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"]))))))
v["i73"] = 0.049278*np.tanh(((data["ORGANIZATION_TYPE_Self_employed"])+(((np.maximum(((data["ORGANIZATION_TYPE_Transport__type_3"])) ,(( data["BURO_CREDIT_TYPE_Microloan_MEAN"])))) +(((data["INSTAL_DPD_MEAN"])+(np.maximum(((data["CC_AMT_BALANCE_MIN"])) ,(( np.where(data["INSTAL_AMT_PAYMENT_MIN"]<0, data["INSTAL_DBD_MAX"], data["PREV_CODE_REJECT_REASON_LIMIT_MEAN"])))))))))))
v["i74"] = 0.049119*np.tanh(((((np.where(data["BURO_AMT_CREDIT_SUM_DEBT_SUM"]>0, data["BURO_DAYS_CREDIT_MEAN"], np.maximum(((((np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_SUM"])) ,(( data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])))) * 2.0))),(((( data["EXT_SOURCE_3"])-(data["BURO_DAYS_CREDIT_MEAN"])))))))* 2.0)) * 2.0))
v["i75"] = 0.045722*np.tanh(((np.maximum(((np.maximum(((data["BURO_CREDIT_ACTIVE_Sold_MEAN"])) ,(( np.maximum(((data["WALLSMATERIAL_MODE_Stone__brick"])) ,(( data["CC_AMT_RECEIVABLE_PRINCIPAL_VAR"])))))))) ,(( data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])))) -(np.maximum(((np.maximum(((data["INSTAL_AMT_INSTALMENT_SUM"])) ,(( data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"]))))),(( data["NEW_CAR_TO_BIRTH_RATIO"]))))))
v["i76"] = 0.049090*np.tanh(np.maximum(((data["CC_CNT_DRAWINGS_CURRENT_MEAN"])) ,(( np.where(data["CLOSED_DAYS_CREDIT_MAX"]<0, np.maximum(((np.where(data["CC_CNT_DRAWINGS_CURRENT_MEAN"] < -99998, data["DEF_30_CNT_SOCIAL_CIRCLE"], data["ORGANIZATION_TYPE_Construction"]))),(((( data["OBS_60_CNT_SOCIAL_CIRCLE"])*(data["APPROVED_AMT_CREDIT_MAX"]))))), data["ACTIVE_AMT_CREDIT_SUM_SUM"])))))
v["i77"] = 0.049867*np.tanh(((data["NEW_RATIO_PREV_AMT_CREDIT_MIN"])*(((-1.0)+(np.maximum(((data["NEW_EXT_SOURCES_MEAN"])) ,(( np.where(data["NEW_CAR_TO_BIRTH_RATIO"] < -99998, data["PREV_NAME_PORTFOLIO_Cash_MEAN"],(-1.0*(( data["REFUSED_AMT_APPLICATION_MIN"]))))))))))))
v["i78"] = 0.049750*np.tanh(((np.where(data["BURO_DAYS_CREDIT_ENDDATE_MEAN"]>0, data["ACTIVE_DAYS_CREDIT_MAX"], np.where(data["POS_SK_DPD_DEF_MAX"]<0, np.where(data["BURO_DAYS_CREDIT_MEAN"]<0, data["BURO_DAYS_CREDIT_MIN"], np.where(data["PREV_CHANNEL_TYPE_Contact_center_MEAN"]>0, data["PREV_CHANNEL_TYPE_Contact_center_MEAN"], data["POS_NAME_CONTRACT_STATUS_Signed_MEAN"])) , data["POS_MONTHS_BALANCE_MEAN"])))* 2.0))
v["i79"] = 0.049999*np.tanh(np.where(data["ACTIVE_MONTHS_BALANCE_MAX_MAX"]>0, data["NEW_RATIO_BURO_AMT_ANNUITY_MAX"], np.where(data["ACTIVE_MONTHS_BALANCE_MAX_MAX"]>0, data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"], np.where(data["BURO_AMT_CREDIT_SUM_MEAN"]>0, data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"], np.maximum(((np.maximum(((data["NEW_CREDIT_TO_GOODS_RATIO"])) ,(( data["BURO_CREDIT_TYPE_Microloan_MEAN"]))))),(( data["INSTAL_PAYMENT_DIFF_MEAN"])))))))
v["i80"] = 0.049750*np.tanh(((((((((data["AMT_ANNUITY"])-(data["WEEKDAY_APPR_PROCESS_START_MONDAY"])))-(np.where(data["APPROVED_AMT_GOODS_PRICE_MAX"]>0,(( data["NEW_LIVE_IND_SUM"])* 2.0),(( data["INSTAL_AMT_INSTALMENT_SUM"])/ 2.0)))))* 2.0)) * 2.0))
v["i81"] = 0.049100*np.tanh(((((((np.where(((data["PREV_PRODUCT_COMBINATION_Cash_Street__high_MEAN"])*(np.maximum(((data["APPROVED_AMT_GOODS_PRICE_MIN"])) ,(( data["NAME_CONTRACT_TYPE_Cash_loans"])))))>0, data["POS_MONTHS_BALANCE_MEAN"], np.where(data["PREV_NAME_YIELD_GROUP_high_MEAN"] < -99998, data["REGION_POPULATION_RELATIVE"], data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) * 2.0)) * 2.0))
v["i82"] = 0.048000*np.tanh(((np.where(data["EXT_SOURCE_2"] < -99998, np.tanh(( data["PREV_APP_CREDIT_PERC_VAR"])) , np.where(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__middle_MEAN"] < -99998, data["NEW_DOC_IND_AVG"],(-1.0*(((((((( data["EXT_SOURCE_2"])+(data["OCCUPATION_TYPE_High_skill_tech_staff"])) /2.0)) +(data["NEW_DOC_IND_AVG"])) /2.0)))))))* 2.0))
v["i83"] = 0.039998*np.tanh(np.where(data["CC_AMT_PAYMENT_CURRENT_MEAN"]>0, data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"], np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0, np.maximum(((data["FLAG_WORK_PHONE"])) ,(((( data["POS_MONTHS_BALANCE_MEAN"])-(data["CODE_GENDER"]))))),(-1.0*(( np.maximum(((data["FLAG_WORK_PHONE"])) ,(( data["POS_MONTHS_BALANCE_MEAN"])))))))))
v["i84"] = 0.046880*np.tanh(((((np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"]<0, np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"] < -99998, np.where(data["PREV_CODE_REJECT_REASON_HC_MEAN"]>0, data["PREV_CODE_REJECT_REASON_HC_MEAN"], data["INSTAL_PAYMENT_DIFF_MAX"]), data["BURO_AMT_CREDIT_SUM_DEBT_SUM"]),(-1.0*(((( data["INSTAL_PAYMENT_DIFF_MAX"])* 2.0)))))) * 2.0)) * 2.0))
v["i85"] = 0.036178*np.tanh(( -1.0*(((((( data["NEW_RATIO_PREV_AMT_CREDIT_MIN"])-(np.minimum(((data["REFUSED_AMT_DOWN_PAYMENT_MIN"])) ,(( data["REGION_POPULATION_RELATIVE"])))))) -(np.where(data["EXT_SOURCE_3"] < -99998, data["REFUSED_DAYS_DECISION_MEAN"],(((data["NEW_RATIO_BURO_DAYS_CREDIT_VAR"])<(data["REGION_POPULATION_RELATIVE"])) *1.))))))))
v["i86"] = 0.019979*np.tanh(((data["ORGANIZATION_TYPE_Transport__type_3"])-(((( data["EXT_SOURCE_2"])+(((np.where(( -1.0*(( data["EXT_SOURCE_2"])))<0, data["BURO_AMT_CREDIT_SUM_MEAN"],(((data["APPROVED_AMT_CREDIT_MAX"])<(((( data["OCCUPATION_TYPE_High_skill_tech_staff"])<(data["APPROVED_AMT_CREDIT_MAX"])) *1.))) *1.))) * 2.0)))/2.0))))
v["i87"] = 0.047481*np.tanh(((data["ORGANIZATION_TYPE_Transport__type_3"])+(((data["ORGANIZATION_TYPE_Construction"])+(np.maximum(((data["BURO_STATUS_1_MEAN_MEAN"])) ,(((((data["APPROVED_CNT_PAYMENT_SUM"])+(((data["INSTAL_COUNT"])*(np.where(data["APPROVED_CNT_PAYMENT_SUM"]<0, data["ACTIVE_AMT_CREDIT_SUM_DEBT_MAX"], data["FLAG_WORK_PHONE"])))))/2.0)))))))))
v["i88"] = 0.046499*np.tanh(( -1.0*(( np.maximum(((data["YEARS_BUILD_MEDI"])) ,(( np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]<0, np.where(data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"]<0, np.where(data["PREV_WEEKDAY_APPR_PROCESS_START_SUNDAY_MEAN"]<0, data["NEW_EMPLOY_TO_BIRTH_RATIO"], data["APPROVED_DAYS_DECISION_MEAN"]), data["NEW_RATIO_PREV_DAYS_DECISION_MAX"]), data["BURO_CREDIT_TYPE_Mortgage_MEAN"]))))))))
v["i89"] = 0.047614*np.tanh(np.where(data["BURO_STATUS_1_MEAN_MEAN"]>0, data["NEW_ANNUITY_TO_INCOME_RATIO"], np.where(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"]<0, np.where(data["ACTIVE_DAYS_CREDIT_ENDDATE_MAX"] < -99998,(( data["PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN"])+(data["DAYS_REGISTRATION"])) , data["PREV_NAME_CONTRACT_TYPE_Revolving_loans_MEAN"]),(((( data["WALLSMATERIAL_MODE_Stone__brick"])* 2.0)) * 2.0))))
v["i90"] = 0.049597*np.tanh(np.maximum(((np.where(data["APPROVED_AMT_GOODS_PRICE_MIN"]>0, data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"], np.where(data["FLAG_DOCUMENT_8"]>0, data["CC_AMT_INST_MIN_REGULARITY_VAR"],(( data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])*(data["APPROVED_CNT_PAYMENT_MEAN"])))))) ,(((( data["CC_AMT_BALANCE_MEAN"])-(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"]))))))
v["i91"] = 0.046517*np.tanh(((((((np.where(data["ACTIVE_DAYS_CREDIT_MAX"]>0,(( np.where(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"]>0, data["CC_CNT_DRAWINGS_ATM_CURRENT_MAX"], data["ACTIVE_DAYS_CREDIT_MAX"])) +(data["NAME_HOUSING_TYPE_Municipal_apartment"])) , data["INSTAL_PAYMENT_DIFF_MEAN"])) +(data["NAME_HOUSING_TYPE_Municipal_apartment"])))* 2.0)) * 2.0))
v["i92"] = 0.034976*np.tanh(((np.where(data["PREV_AMT_GOODS_PRICE_MEAN"] < -99998, data["NEW_DOC_IND_STD"],(( np.where(data["CLOSED_DAYS_CREDIT_MAX"]>0, data["INSTAL_AMT_PAYMENT_MIN"], np.where(data["ORGANIZATION_TYPE_Transport__type_3"]>0, data["ORGANIZATION_TYPE_Transport__type_3"],(( data["OBS_60_CNT_SOCIAL_CIRCLE"])*(data["PREV_AMT_GOODS_PRICE_MEAN"])))))* 2.0)))* 2.0))
v["i93"] = 0.015000*np.tanh(((np.maximum(((np.where(data["WEEKDAY_APPR_PROCESS_START_SATURDAY"]<0, data["ORGANIZATION_TYPE_Business_Entity_Type_3"], data["REFUSED_HOUR_APPR_PROCESS_START_MAX"]))),(( data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])))) -(np.maximum(((np.maximum(((data["WEEKDAY_APPR_PROCESS_START_SUNDAY"])) ,(( data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"]))))),(( np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_MEAN"])) ,(( data["OCCUPATION_TYPE_Accountants"])))))))))
v["i94"] = 0.049002*np.tanh(((( data["NEW_EXT_SOURCES_MEAN"])<(((np.tanh(( np.where(data["NEW_EXT_SOURCES_MEAN"]<0, data["NEW_SOURCES_PROD"], np.tanh(( np.tanh(((((data["ORGANIZATION_TYPE_Construction"])>(np.tanh(( data["APPROVED_AMT_DOWN_PAYMENT_MIN"])))) *1.))))))))) * 2.0)))*1.))
v["i95"] = 0.041000*np.tanh(((data["FLAG_WORK_PHONE"])+(np.where(data["APPROVED_DAYS_DECISION_MIN"]>0, np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]>0, data["CC_AMT_DRAWINGS_CURRENT_MEAN"],(((( data["INSTAL_DPD_MEAN"])+(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])+(data["APPROVED_DAYS_DECISION_MIN"])))))* 2.0)) , data["NEW_CREDIT_TO_ANNUITY_RATIO"]))))
v["i96"] = 0.048802*np.tanh(np.where(data["EXT_SOURCE_1"] < -99998,(( data["DAYS_BIRTH"])* 2.0),(((((((-1.0*(((((( data["DAYS_BIRTH"])* 2.0)) * 2.0)))))-(data["EXT_SOURCE_1"])))-(data["EXT_SOURCE_1"])))-(data["EXT_SOURCE_1"]))))
v["i97"] = 0.049802*np.tanh(np.where(data["ACTIVE_DAYS_CREDIT_MIN"] < -99998, data["AMT_INCOME_TOTAL"], np.where(data["CC_AMT_RECIVABLE_MEAN"]>0, data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"],(( np.where(data["NEW_INC_BY_ORG"]<0, data["CLOSED_DAYS_CREDIT_MEAN"], data["PREV_NAME_PRODUCT_TYPE_x_sell_MEAN"])) -(data["DAYS_EMPLOYED"])))))
v["i98"] = 0.011046*np.tanh(((np.where(((( data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"])>(data["CC_CNT_DRAWINGS_ATM_CURRENT_MAX"])) *1.) >0, data["INSTAL_AMT_PAYMENT_MIN"],(( np.maximum(((data["POS_SK_DPD_DEF_MAX"])) ,(((((data["PREV_DAYS_DECISION_MEAN"])>(((( data["POS_SK_DPD_DEF_MAX"])>(data["NEW_RATIO_PREV_DAYS_DECISION_MAX"])) *1.))) *1.))))) * 2.0)))* 2.0))
v["i99"] = 0.049544*np.tanh(((np.maximum(((data["ORGANIZATION_TYPE_Transport__type_3"])) ,(( np.maximum(((data["POS_NAME_CONTRACT_STATUS_Returned_to_the_store_MEAN"])) ,(( np.maximum(((data["CC_CNT_DRAWINGS_CURRENT_VAR"])) ,(( np.where(data["NEW_RATIO_PREV_APP_CREDIT_PERC_MEAN"] < -99998, data["NEW_SCORES_STD"], data["OBS_60_CNT_SOCIAL_CIRCLE"])))))))))))+(np.minimum(((data["REGION_RATING_CLIENT_W_CITY"])) ,(( data["DAYS_ID_PUBLISH"]))))))
v["i100"] = 0.047080*np.tanh(np.where(data["EXT_SOURCE_3"] < -99998,(( data["EXT_SOURCE_2"])* 2.0),(((( data["NAME_FAMILY_STATUS_Married"])*(data["DAYS_BIRTH"])))+(((((( data["EXT_SOURCE_3"])/ 2.0)) <(((data["ORGANIZATION_TYPE_Construction"])-(data["NAME_FAMILY_STATUS_Married"])))) *1.)))))
v["i101"] = 0.049910*np.tanh(np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]>0, data["CC_MONTHS_BALANCE_VAR"], np.where(data["EXT_SOURCE_1"]<0,(((data["NEW_EXT_SOURCES_MEAN"])>(((data["PREV_CHANNEL_TYPE_Credit_and_cash_offices_MEAN"])/ 2.0)))*1.) ,(((( data["REGION_RATING_CLIENT_W_CITY"])-(data["NEW_EXT_SOURCES_MEAN"])))-(data["NAME_INCOME_TYPE_Working"])))))
v["i102"] = 0.049000*np.tanh(((((np.tanh(( np.tanh(( data["NEW_EXT_SOURCES_MEAN"])))))-(np.maximum(((((( data["WEEKDAY_APPR_PROCESS_START_SUNDAY"])>(((data["NEW_EXT_SOURCES_MEAN"])/ 2.0)))*1.))) ,(( np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])) ,(( data["PREV_CHANNEL_TYPE_Channel_of_corporate_sales_MEAN"])))))))))-(data["NEW_EXT_SOURCES_MEAN"])))
v["i103"] = 0.047481*np.tanh(((((((((data["FLAG_DOCUMENT_3"])*(np.where(data["BURO_MONTHS_BALANCE_SIZE_SUM"]<0, np.maximum(((((data["PREV_PRODUCT_COMBINATION_POS_household_without_interest_MEAN"])* 2.0))),(( data["REGION_POPULATION_RELATIVE"]))), data["PREV_PRODUCT_COMBINATION_POS_household_without_interest_MEAN"])))) * 2.0)) -(data["WEEKDAY_APPR_PROCESS_START_SUNDAY"])))* 2.0))
v["i104"] = 0.037198*np.tanh(np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(((( data["EXT_SOURCE_3"])*(np.where(((data["EXT_SOURCE_3"])*(data["EXT_SOURCE_2"])) < -99998, data["EXT_SOURCE_2"], np.tanh(((( data["NEW_CREDIT_TO_GOODS_RATIO"])*(data["EXT_SOURCE_2"])))))))))))
v["i105"] = 0.049050*np.tanh(np.where(data["PREV_AMT_DOWN_PAYMENT_MIN"]>0, data["INSTAL_AMT_PAYMENT_MIN"],(((( np.where(data["PREV_NAME_PRODUCT_TYPE_walk_in_MEAN"]>0, data["NEW_ANNUITY_TO_INCOME_RATIO"], data["PREV_AMT_DOWN_PAYMENT_MIN"])) -(np.where(data["TOTALAREA_MODE"] < -99998, data["PREV_NAME_YIELD_GROUP_middle_MEAN"], data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])))) * 2.0)))
v["i106"] = 0.043662*np.tanh(np.where(data["NEW_DOC_IND_KURT"]>0, np.where(data["BURO_DAYS_CREDIT_MIN"]>0, np.maximum(((data["PREV_WEEKDAY_APPR_PROCESS_START_FRIDAY_MEAN"])) ,(( data["NEW_RATIO_BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"]))), np.where(data["NEW_INC_PER_CHLD"]<0, data["ACTIVE_MONTHS_BALANCE_MIN_MIN"],(-1.0*(( data["ACTIVE_MONTHS_BALANCE_MIN_MIN"]))))),(( data["NEW_DOC_IND_KURT"])* 2.0)))
v["i107"] = 0.023067*np.tanh(((((((( data["EXT_SOURCE_2"])<(( -1.0*(((((((( -1.0*(( data["EXT_SOURCE_2"])))) >(((data["PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN"])*(((( data["BURO_DAYS_CREDIT_MAX"])<(data["PREV_AMT_GOODS_PRICE_MEAN"])) *1.))))) *1.))* 2.0)))))) *1.))* 2.0)) * 2.0))
v["i108"] = 0.050000*np.tanh(( -1.0*(( np.where(data["APPROVED_AMT_CREDIT_MAX"]>0, data["NEW_RATIO_PREV_AMT_CREDIT_MAX"], np.where(np.where(data["CLOSED_AMT_CREDIT_SUM_SUM"]>0, data["NEW_EMPLOY_TO_BIRTH_RATIO"],(((data["CODE_GENDER"])+(data["POS_MONTHS_BALANCE_SIZE"])) /2.0)) <0, data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"],(-1.0*(( data["REFUSED_AMT_CREDIT_MIN"])))))))))
v["i109"] = 0.001990*np.tanh(((data["POS_NAME_CONTRACT_STATUS_Returned_to_the_store_MEAN"])-(np.where(data["APPROVED_AMT_ANNUITY_MEAN"]>0, data["PREV_NAME_SELLER_INDUSTRY_XNA_MEAN"],(( np.maximum(((((np.maximum(((data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])) ,(( np.minimum(((data["BURO_DAYS_CREDIT_UPDATE_MEAN"])) ,(( data["BURO_CREDIT_TYPE_Consumer_credit_MEAN"])))))))* 2.0))),(( data["INSTAL_AMT_INSTALMENT_MEAN"])))) * 2.0)))))
v["i110"] = 0.035600*np.tanh(((( -1.0*(( np.maximum(((((( data["NAME_FAMILY_STATUS_Married"])+(data["NAME_INCOME_TYPE_Commercial_associate"])) /2.0))),(( data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"])))))))-(np.maximum(((((( data["PREV_WEEKDAY_APPR_PROCESS_START_SATURDAY_MEAN"])+(data["OCCUPATION_TYPE_High_skill_tech_staff"])) /2.0))),(((( data["OCCUPATION_TYPE_Accountants"])+(data["ORGANIZATION_TYPE_Medicine"]))))))))
v["i111"] = 0.026365*np.tanh(((((((((data["ORGANIZATION_TYPE_Medicine"])>(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"])) *1.))>(data["DAYS_BIRTH"])) *1.))-(np.where(data["DAYS_BIRTH"]>0, data["NEW_INC_BY_ORG"],(( data["PREV_NAME_PORTFOLIO_POS_MEAN"])+(((( data["POS_SK_DPD_MEAN"])>(data["CC_AMT_BALANCE_VAR"])) *1.)))))))
v["i112"] = 0.049648*np.tanh(np.where(data["NEW_RATIO_PREV_AMT_APPLICATION_MIN"] < -99998,(((((data["PREV_NAME_CASH_LOAN_PURPOSE_XNA_MEAN"])*(( -1.0*(( data["NAME_HOUSING_TYPE_House___apartment"])))))) +(((data["POS_SK_DPD_MAX"])-(data["NEW_DOC_IND_AVG"])))) /2.0),(( data["PREV_CHANNEL_TYPE_Stone_MEAN"])-(( -1.0*(( data["PREV_NAME_TYPE_SUITE_Spouse__partner_MEAN"])))))))
v["i113"] = 0.028480*np.tanh(np.where(data["PREV_CHANNEL_TYPE_Credit_and_cash_offices_MEAN"] < -99998, data["NEW_DOC_IND_STD"], np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"] < -99998, data["POS_SK_DPD_DEF_MAX"], np.where(data["PREV_CHANNEL_TYPE_Credit_and_cash_offices_MEAN"]>0, data["BURO_CREDIT_TYPE_Credit_card_MEAN"],(((data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"])>(((( data["PREV_NAME_PAYMENT_TYPE_Cash_through_the_bank_MEAN"])<(data["ACTIVE_MONTHS_BALANCE_MIN_MIN"])) *1.))) *1.)))))
v["i114"] = 0.048956*np.tanh(np.where(data["ACTIVE_AMT_CREDIT_SUM_SUM"]<0, np.where(data["AMT_GOODS_PRICE"]<0, np.where(data["NEW_RATIO_BURO_MONTHS_BALANCE_SIZE_SUM"] < -99998, data["OCCUPATION_TYPE_Laborers"], data["POS_COUNT"]), data["NAME_EDUCATION_TYPE_Higher_education"]),(( data["ACTIVE_AMT_CREDIT_SUM_SUM"])+(( -1.0*(( data["ACTIVE_AMT_CREDIT_SUM_MAX"])))))))
v["i115"] = 0.039000*np.tanh(np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_MEAN"]>0, np.where(data["NEW_EXT_SOURCES_MEAN"]<0,(( data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])* 2.0), data["PREV_CODE_REJECT_REASON_SCO_MEAN"]),(-1.0*(( np.where(data["NEW_EXT_SOURCES_MEAN"]<0, data["INSTAL_AMT_PAYMENT_SUM"],(( data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])+(data["PREV_CODE_REJECT_REASON_SCO_MEAN"]))))))))
v["i116"] = 0.049997*np.tanh(( -1.0*(((( np.where(data["CC_AMT_RECIVABLE_VAR"]>0, data["ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN"],(-1.0*(((((data["ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN"])>(data["BURO_AMT_CREDIT_SUM_MEAN"])) *1.))))))+(((( data["BURO_AMT_CREDIT_SUM_MEAN"])>(data["ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN"])) *1.)))))))
v["i117"] = 0.049001*np.tanh(np.where(data["CC_CNT_DRAWINGS_ATM_CURRENT_VAR"]>0, data["CLOSED_DAYS_CREDIT_MIN"], np.where(data["NEW_CREDIT_TO_GOODS_RATIO"]>0, np.maximum(((data["NEW_ANNUITY_TO_INCOME_RATIO"])) ,(( data["APPROVED_AMT_APPLICATION_MAX"]))),(((data["NEW_CREDIT_TO_GOODS_RATIO"])<(np.where(data["APPROVED_AMT_APPLICATION_MEAN"]<0, data["APPROVED_AMT_APPLICATION_MEAN"], data["CC_NAME_CONTRACT_STATUS_Active_SUM"])))*1.))))
v["i118"] = 0.029997*np.tanh(np.where(data["NEW_RATIO_PREV_APP_CREDIT_PERC_MEAN"]>0, data["NEW_RATIO_PREV_APP_CREDIT_PERC_MEAN"], np.where(data["CC_AMT_RECEIVABLE_PRINCIPAL_VAR"] < -99998, np.where(data["ACTIVE_DAYS_CREDIT_UPDATE_MEAN"]<0, data["NEW_CREDIT_TO_GOODS_RATIO"], data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]), np.where(data["CC_CNT_DRAWINGS_CURRENT_MAX"]<0, data["CLOSED_DAYS_CREDIT_VAR"], data["PREV_NAME_PAYMENT_TYPE_XNA_MEAN"]))))
v["i119"] = 0.049942*np.tanh(np.where(data["PREV_NAME_CLIENT_TYPE_Refreshed_MEAN"]>0, data["BURO_CREDIT_TYPE_Credit_card_MEAN"], np.maximum(((data["BURO_CREDIT_ACTIVE_Sold_MEAN"])) ,(( np.where(data["PREV_AMT_DOWN_PAYMENT_MIN"]>0, data["FLOORSMIN_MODE"], np.where(data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"]>0, data["BURO_CREDIT_TYPE_Credit_card_MEAN"],(( data["CC_AMT_RECEIVABLE_PRINCIPAL_MEAN"])-(data["CC_AMT_CREDIT_LIMIT_ACTUAL_MEAN"])))))))))
v["i120"] = 0.014008*np.tanh(( -1.0*(( np.where(data["NONLIVINGAREA_AVG"]>0, data["NEW_INC_BY_ORG"],(( np.maximum(((data["AMT_REQ_CREDIT_BUREAU_QRT"])) ,(((((np.maximum(((data["WEEKDAY_APPR_PROCESS_START_SATURDAY"])) ,(( data["NEW_RATIO_PREV_AMT_CREDIT_MAX"])))) +(np.maximum(((data["NAME_HOUSING_TYPE_Office_apartment"])) ,(( data["PREV_CHANNEL_TYPE_Channel_of_corporate_sales_MEAN"])))))/2.0)))))* 2.0))))))
v["i121"] = 0.047508*np.tanh(np.where(data["FLAG_PHONE"]<0,(( data["INSTAL_DBD_SUM"])*(np.where(data["NEW_DOC_IND_STD"]>0, np.where(data["BURO_DAYS_CREDIT_MAX"]>0, data["PREV_NAME_YIELD_GROUP_high_MEAN"], data["FLAG_PHONE"]), 3.0))), data["OWN_CAR_AGE"]))
v["i122"] = 0.022005*np.tanh(((np.maximum(((data["CC_AMT_RECIVABLE_VAR"])) ,(( np.maximum(((((( data["POS_MONTHS_BALANCE_MEAN"])<(np.minimum(((data["EXT_SOURCE_3"])) ,(( data["INSTAL_PAYMENT_DIFF_SUM"])))))*1.))) ,(((((data["PREV_NAME_CASH_LOAN_PURPOSE_Medicine_MEAN"])>(((data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"])*(data["REFUSED_AMT_GOODS_PRICE_MAX"])))) *1.))))))))* 2.0))
v["i123"] = 0.024998*np.tanh(np.where(data["LIVE_CITY_NOT_WORK_CITY"]>0, data["ACTIVE_AMT_ANNUITY_MEAN"], np.where(data["ACTIVE_AMT_CREDIT_SUM_DEBT_MAX"]<0, np.where(data["CLOSED_DAYS_CREDIT_VAR"]<0, np.where(data["PREV_NAME_CONTRACT_STATUS_Approved_MEAN"]<0, data["NEW_RATIO_PREV_AMT_GOODS_PRICE_MIN"], data["PREV_NAME_CONTRACT_STATUS_Approved_MEAN"]), data["APPROVED_APP_CREDIT_PERC_VAR"]), data["NEW_CREDIT_TO_INCOME_RATIO"])))
v["i124"] = 0.037322*np.tanh(np.maximum(((np.where(data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"] < -99998, data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"], data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]))),(( np.where(data["APPROVED_AMT_GOODS_PRICE_MIN"]>0, data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"],(((data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"])<(data["APPROVED_AMT_GOODS_PRICE_MIN"])) *1.))))))
v["i125"] = 0.049402*np.tanh(((np.where(data["CC_AMT_DRAWINGS_POS_CURRENT_SUM"]>0, data["PREV_AMT_GOODS_PRICE_MIN"], np.where(data["PREV_PRODUCT_COMBINATION_POS_industry_with_interest_MEAN"]>0,(-1.0*(( data["PREV_NAME_YIELD_GROUP_middle_MEAN"]))),(( data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__middle_MEAN"])*(data["APPROVED_CNT_PAYMENT_MEAN"])))))-(((( data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])>(data["REG_CITY_NOT_LIVE_CITY"])) *1.))))
v["i126"] = 0.049398*np.tanh(np.where(data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"] < -99998, data["DEF_30_CNT_SOCIAL_CIRCLE"], np.where(data["EXT_SOURCE_3"] < -99998, data["EXT_SOURCE_2"],(( np.maximum(((data["DAYS_BIRTH"])) ,(( data["NAME_EDUCATION_TYPE_Higher_education"])))) -(((data["NAME_EDUCATION_TYPE_Higher_education"])*(data["DAYS_BIRTH"])))))))
v["i127"] = 0.043856*np.tanh(((np.where(data["DAYS_ID_PUBLISH"]<0,(( data["DAYS_BIRTH"])-(data["NEW_EMPLOY_TO_BIRTH_RATIO"])) ,(-1.0*(( np.maximum(((data["PREV_NAME_CONTRACT_TYPE_Consumer_loans_MEAN"])) ,(( data["DAYS_BIRTH"])))))))) +(data["DAYS_ID_PUBLISH"])))
v["i128"] = 0.047562*np.tanh(((data["NAME_EDUCATION_TYPE_Lower_secondary"])+(((data["POS_SK_DPD_MEAN"])+(np.maximum(((((np.where(data["ACTIVE_DAYS_CREDIT_ENDDATE_MAX"] < -99998, data["INSTAL_PAYMENT_DIFF_SUM"], data["PREV_AMT_ANNUITY_MIN"])) +(data["ORGANIZATION_TYPE_Transport__type_3"])))) ,(((( data["ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN"])+(data["BURO_CREDIT_TYPE_Microloan_MEAN"]))))))))))
v["i129"] = 0.041886*np.tanh(((data["DAYS_REGISTRATION"])*(((((( data["POS_NAME_CONTRACT_STATUS_Signed_MEAN"])+(data["REGION_RATING_CLIENT_W_CITY"])) /2.0)) +(((((((data["PREV_CODE_REJECT_REASON_SCOFR_MEAN"])+(( -1.0*(( data["ORGANIZATION_TYPE_Military"])))))/2.0)) +(((( data["POS_SK_DPD_DEF_MEAN"])>(data["DAYS_BIRTH"])) *1.))) /2.0))))))
v["i130"] = 0.049340*np.tanh(np.where(data["ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN"]<0, np.where(data["CC_AMT_INST_MIN_REGULARITY_SUM"]<0, np.where(data["CC_AMT_INST_MIN_REGULARITY_SUM"] < -99998, data["NAME_HOUSING_TYPE_Rented_apartment"], data["APPROVED_CNT_PAYMENT_MEAN"]), np.where(data["CC_CNT_INSTALMENT_MATURE_CUM_MEAN"]>0, data["FLOORSMIN_AVG"], data["CC_AMT_CREDIT_LIMIT_ACTUAL_MIN"])) , 3.141593))
v["i131"] = 0.044198*np.tanh(np.where(((data["APPROVED_APP_CREDIT_PERC_VAR"])-(data["EXT_SOURCE_3"])) < -99998,(( data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])* 2.0), np.maximum(((((data["EXT_SOURCE_3"])-(data["APPROVED_APP_CREDIT_PERC_VAR"])))) ,(((((( data["CC_AMT_RECEIVABLE_PRINCIPAL_VAR"])-(data["CC_AMT_CREDIT_LIMIT_ACTUAL_MEAN"])))* 2.0))))))
v["i132"] = 0.049514*np.tanh(( -1.0*(( np.where(data["LIVINGAREA_AVG"]>0, data["NEW_RATIO_PREV_APP_CREDIT_PERC_MEAN"], np.maximum(((data["NAME_HOUSING_TYPE_Office_apartment"])) ,(((( np.maximum(((((( data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"])>(np.maximum(((data["REGION_POPULATION_RELATIVE"])) ,(( data["CC_AMT_RECEIVABLE_PRINCIPAL_MEAN"])))))*1.))) ,(( data["ORGANIZATION_TYPE_Industry__type_9"])))) * 2.0)))))))))
v["i133"] = 0.049862*np.tanh(np.where(data["BURO_AMT_CREDIT_SUM_DEBT_MAX"]>0, data["CC_NAME_CONTRACT_STATUS_Active_SUM"],(( np.where(((( data["ENTRANCES_MEDI"])>(data["INSTAL_DPD_MEAN"])) *1.) >0, data["CC_NAME_CONTRACT_STATUS_Active_SUM"],(((data["NEW_RATIO_PREV_DAYS_DECISION_MAX"])>(data["ORGANIZATION_TYPE_Military"])) *1.))) * 2.0)))
v["i134"] = 0.045502*np.tanh(( -1.0*(( np.where(data["EXT_SOURCE_3"]>0, data["BURO_DAYS_CREDIT_UPDATE_MEAN"], np.where(data["CLOSED_AMT_CREDIT_MAX_OVERDUE_MEAN"]>0, data["INSTAL_PAYMENT_DIFF_MAX"], np.where(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"]<0, data["CLOSED_AMT_CREDIT_MAX_OVERDUE_MEAN"], np.where(data["ACTIVE_AMT_CREDIT_SUM_SUM"]<0, data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"], data["EXT_SOURCE_3"]))))))))
v["i135"] = 0.020008*np.tanh(( -1.0*(( np.where(data["APPROVED_CNT_PAYMENT_SUM"]>0,(( np.tanh(( data["DAYS_EMPLOYED"])))-(np.where(data["APPROVED_AMT_GOODS_PRICE_MIN"]<0,(((data["INSTAL_DPD_MEAN"])>(data["DAYS_EMPLOYED"])) *1.) , data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"]))), data["OCCUPATION_TYPE_Medicine_staff"])))))
v["i136"] = 0.035000*np.tanh(np.where(data["NEW_EXT_SOURCES_MEAN"]>0,(-1.0*(( data["NEW_PHONE_TO_BIRTH_RATIO"]))), np.where(data["EXT_SOURCE_3"] < -99998, data["REFUSED_AMT_CREDIT_MAX"],(( data["PREV_NAME_GOODS_CATEGORY_Photo___Cinema_Equipment_MEAN"])*(np.where(data["PREV_NAME_GOODS_CATEGORY_Photo___Cinema_Equipment_MEAN"] < -99998, data["EXT_SOURCE_3"], data["ACTIVE_DAYS_CREDIT_ENDDATE_MEAN"]))))))
v["i137"] = 0.048501*np.tanh(np.where(data["NEW_RATIO_BURO_MONTHS_BALANCE_SIZE_MEAN"]>0, data["PREV_NAME_YIELD_GROUP_middle_MEAN"], np.maximum(((data["PREV_NAME_GOODS_CATEGORY_Audio_Video_MEAN"])) ,(((( np.where(data["INSTAL_PAYMENT_DIFF_MAX"]>0, data["OCCUPATION_TYPE_Drivers"], data["APPROVED_DAYS_DECISION_MAX"])) *(data["NEW_CREDIT_TO_GOODS_RATIO"])))))))
v["i138"] = 0.048902*np.tanh(np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_VAR"] < -99998,(((np.where(data["BURO_DAYS_CREDIT_MEAN"]>0, data["PREV_CHANNEL_TYPE_Stone_MEAN"], data["PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN"])) +(np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"] < -99998, data["PREV_CHANNEL_TYPE_Stone_MEAN"], data["ACTIVE_DAYS_CREDIT_MEAN"])))/2.0),(( data["NEW_RATIO_PREV_AMT_ANNUITY_MAX"])+(data["BURO_DAYS_CREDIT_MEAN"]))))
v["i139"] = 0.043840*np.tanh(np.where(data["AMT_INCOME_TOTAL"]>0, data["FLAG_DOCUMENT_3"], np.maximum(((data["WEEKDAY_APPR_PROCESS_START_WEDNESDAY"])) ,(( np.maximum(((data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"])) ,(((-1.0*(((((((data["WEEKDAY_APPR_PROCESS_START_WEDNESDAY"])>(data["NEW_EXT_SOURCES_MEAN"])) *1.))+(data["NEW_CREDIT_TO_GOODS_RATIO"])))))))))))))
v["i140"] = 0.049910*np.tanh(np.where(data["PREV_NAME_PAYMENT_TYPE_Cash_through_the_bank_MEAN"]>0, data["PREV_CODE_REJECT_REASON_XAP_MEAN"], np.where(data["CC_AMT_PAYMENT_CURRENT_SUM"]>0, data["OCCUPATION_TYPE_Drivers"],(((data["OCCUPATION_TYPE_Drivers"])>(np.where(data["APPROVED_AMT_ANNUITY_MIN"] < -99998, data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"],(( data["APPROVED_AMT_GOODS_PRICE_MIN"])/ 2.0)))) *1.))))
v["i141"] = 0.049610*np.tanh(((np.where(data["LANDAREA_AVG"]>0, data["CC_NAME_CONTRACT_STATUS_Active_SUM"],(( np.where(data["BURO_STATUS_0_MEAN_MEAN"]>0, data["PREV_PRODUCT_COMBINATION_POS_household_without_interest_MEAN"], np.where(data["BASEMENTAREA_AVG"]>0, data["NEW_CREDIT_TO_ANNUITY_RATIO"],(((data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"])>(data["POS_SK_DPD_DEF_MEAN"])) *1.))))* 2.0)))* 2.0))
v["i142"] = 0.042842*np.tanh(np.where(data["CODE_GENDER"]>0, np.minimum(((data["CODE_GENDER"])) ,(((( data["DAYS_BIRTH"])* 2.0)))) ,(-1.0*(((( data["DAYS_BIRTH"])-(( -1.0*(((( data["NEW_EXT_SOURCES_MEAN"])-(( -1.0*(( data["DAYS_BIRTH"])))))))))))))))
v["i143"] = 0.045501*np.tanh(np.where(data["REGION_RATING_CLIENT"]>0,(( data["PREV_RATE_DOWN_PAYMENT_MIN"])* 2.0), np.where(((data["CC_AMT_PAYMENT_CURRENT_SUM"])/ 2.0)>0, data["PREV_NAME_TYPE_SUITE_Family_MEAN"], np.where(data["NEW_PHONE_TO_EMPLOY_RATIO"]>0, data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"],(((data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])>(data["NEW_PHONE_TO_EMPLOY_RATIO"])) *1.)))))
v["i144"] = 0.000197*np.tanh(((( data["POS_NAME_CONTRACT_STATUS_Completed_MEAN"])<(np.tanh(( np.where(data["PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN"]<0, np.where(data["PREV_AMT_GOODS_PRICE_MAX"]<0, data["FLOORSMIN_MODE"], data["APPROVED_AMT_APPLICATION_MAX"]), np.where(data["BURO_STATUS_1_MEAN_MEAN"]<0, data["CC_AMT_DRAWINGS_ATM_CURRENT_SUM"],(-1.0*(( data["PREV_NAME_SELLER_INDUSTRY_XNA_MEAN"])))))))))*1.))
v["i145"] = 0.049703*np.tanh(np.maximum(((data["POS_NAME_CONTRACT_STATUS_Returned_to_the_store_MEAN"])) ,(( np.maximum(((((((data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"])* 2.0)) * 2.0))),(((((data["DAYS_EMPLOYED"])<(np.where(np.maximum(((data["BURO_DAYS_CREDIT_ENDDATE_MIN"])) ,(( data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])))>0, data["BURO_CREDIT_ACTIVE_Sold_MEAN"], data["CC_CNT_DRAWINGS_POS_CURRENT_MIN"])))*1.))))))))
v["i146"] = 0.049482*np.tanh(np.where(data["NEW_CREDIT_TO_GOODS_RATIO"]<0, data["INSTAL_DBD_MAX"],(( np.where(data["BURO_DAYS_CREDIT_ENDDATE_MEAN"]<0, np.where(data["WEEKDAY_APPR_PROCESS_START_WEDNESDAY"]>0, data["APPROVED_AMT_GOODS_PRICE_MAX"],(( data["PREV_NAME_PORTFOLIO_Cash_MEAN"])*(data["POS_NAME_CONTRACT_STATUS_Completed_MEAN"]))), data["NEW_RATIO_BURO_AMT_ANNUITY_MEAN"])) -(data["PREV_NAME_TYPE_SUITE_Children_MEAN"]))))
v["i147"] = 0.040002*np.tanh(np.where(data["APARTMENTS_AVG"]<0, np.where(data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]<0,(((( data["APPROVED_AMT_ANNUITY_MEAN"])*(data["OCCUPATION_TYPE_Laborers"])))/ 2.0), 1.0),(-1.0*(( np.where(data["NAME_FAMILY_STATUS_Married"]<0, data["BURO_STATUS_0_MEAN_MEAN"], data["OCCUPATION_TYPE_Laborers"]))))))
v["i148"] = 0.048200*np.tanh(np.where(data["PREV_NAME_SELLER_INDUSTRY_Clothing_MEAN"]>0, data["CC_CNT_DRAWINGS_POS_CURRENT_VAR"], np.where(data["INSTAL_DBD_MEAN"]>0, data["POS_COUNT"],(( data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"])*(((( np.where(data["REFUSED_AMT_GOODS_PRICE_MEAN"] < -99998, data["PREV_NAME_SELLER_INDUSTRY_Clothing_MEAN"], data["REFUSED_AMT_GOODS_PRICE_MEAN"])) >(data["POS_COUNT"])) *1.))))))
v["i149"] = 0.047494*np.tanh(( -1.0*(( np.maximum(((((( data["APPROVED_HOUR_APPR_PROCESS_START_MIN"])<(np.where(data["INSTAL_AMT_PAYMENT_MIN"]<0, data["REFUSED_DAYS_DECISION_MIN"], np.where(data["REFUSED_AMT_ANNUITY_MIN"] < -99998, data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"], data["NONLIVINGAPARTMENTS_AVG"])))) *1.))) ,(( np.maximum(((data["ORGANIZATION_TYPE_Industry__type_9"])) ,(( data["PREV_CHANNEL_TYPE_Channel_of_corporate_sales_MEAN"]))))))))))
return Output(v.sum(axis=1)-2.432490 ) | Home Credit Default Risk |
1,462,214 | shapes = []
for i in range(len(X_train)) :
path = '.. /input/ava/dataset/dataset/'+str(X_train[i])+'.jpg'
img = cv2.imread(path)
shapes.append(img.shape)
shapes = np.array(shapes[:])
print(np.mean(shapes[:,0]), np.mean(shapes[:,1]), np.mean(shapes[:,2]))<normalization> | roc_auc_score(train_df.TARGET,GP1(train_df)) | Home Credit Default Risk |
1,462,214 | def get_feature(img):
img = cv2.resize(img,(32, 32), interpolation = cv2.INTER_AREA)
return np.array(img ).flatten()<feature_engineering> | roc_auc_score(train_df.TARGET,GP2(train_df)) | Home Credit Default Risk |
1,462,214 | <train_model><EOS> | x = test_df[['SK_ID_CURR']].copy()
x['TARGET'] =.5*GP1(test_df)+.5*GP2(test_df)
x.to_csv('pure_submission.csv', index = False ) | Home Credit Default Risk |
1,443,616 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<prepare_output> | import gc
import time
import numpy as np
import pandas as pd
from contextlib import contextmanager
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn.preprocessing import StandardScaler | Home Credit Default Risk |
1,443,616 | prediction = pd.DataFrame()
prediction['labels'] = lr.predict(features_test )<save_to_csv> | @contextmanager
def timer(title):
t0 = time.time()
yield
print("{} - done in {:.0f}s".format(title, time.time() - t0))
def one_hot_encoder(df, nan_as_category = True):
original_columns = list(df.columns)
categorical_columns = [col for col in df.columns if df[col].dtype == 'object']
df = pd.get_dummies(df, columns= categorical_columns, dummy_na= nan_as_category)
new_columns = [c for c in df.columns if c not in original_columns]
return df, new_columns
def application_train_test(num_rows = None, nan_as_category = False):
df = pd.read_csv('.. /input/application_train.csv', nrows= num_rows)
test_df = pd.read_csv('.. /input/application_test.csv', nrows= num_rows)
print("Train samples: {}, test samples: {}".format(len(df), len(test_df)))
df = df.append(test_df ).reset_index()
df = df[df['CODE_GENDER'] != 'XNA']
docs = [_f for _f in df.columns if 'FLAG_DOC' in _f]
live = [_f for _f in df.columns if('FLAG_' in _f)&('FLAG_DOC' not in _f)&('_FLAG_' not in _f)]
df['DAYS_EMPLOYED'].replace(365243, np.nan, inplace= True)
inc_by_org = df[['AMT_INCOME_TOTAL', 'ORGANIZATION_TYPE']].groupby('ORGANIZATION_TYPE' ).median() ['AMT_INCOME_TOTAL']
df['NEW_CREDIT_TO_ANNUITY_RATIO'] = df['AMT_CREDIT'] / df['AMT_ANNUITY']
df['NEW_CREDIT_TO_GOODS_RATIO'] = df['AMT_CREDIT'] / df['AMT_GOODS_PRICE']
df['NEW_DOC_IND_AVG'] = df[docs].mean(axis=1)
df['NEW_DOC_IND_STD'] = df[docs].std(axis=1)
df['NEW_DOC_IND_KURT'] = df[docs].kurtosis(axis=1)
df['NEW_LIVE_IND_SUM'] = df[live].sum(axis=1)
df['NEW_LIVE_IND_STD'] = df[live].std(axis=1)
df['NEW_LIVE_IND_KURT'] = df[live].kurtosis(axis=1)
df['NEW_INC_PER_CHLD'] = df['AMT_INCOME_TOTAL'] /(1 + df['CNT_CHILDREN'])
df['NEW_INC_BY_ORG'] = df['ORGANIZATION_TYPE'].map(inc_by_org)
df['NEW_EMPLOY_TO_BIRTH_RATIO'] = df['DAYS_EMPLOYED'] / df['DAYS_BIRTH']
df['NEW_ANNUITY_TO_INCOME_RATIO'] = df['AMT_ANNUITY'] /(1 + df['AMT_INCOME_TOTAL'])
df['NEW_SOURCES_PROD'] = df['EXT_SOURCE_1'] * df['EXT_SOURCE_2'] * df['EXT_SOURCE_3']
df['NEW_EXT_SOURCES_MEAN'] = df[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].mean(axis=1)
df['NEW_SCORES_STD'] = df[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].std(axis=1)
df['NEW_SCORES_STD'] = df['NEW_SCORES_STD'].fillna(df['NEW_SCORES_STD'].mean())
df['NEW_CAR_TO_BIRTH_RATIO'] = df['OWN_CAR_AGE'] / df['DAYS_BIRTH']
df['NEW_CAR_TO_EMPLOY_RATIO'] = df['OWN_CAR_AGE'] / df['DAYS_EMPLOYED']
df['NEW_PHONE_TO_BIRTH_RATIO'] = df['DAYS_LAST_PHONE_CHANGE'] / df['DAYS_BIRTH']
df['NEW_PHONE_TO_EMPLOY_RATIO'] = df['DAYS_LAST_PHONE_CHANGE'] / df['DAYS_EMPLOYED']
df['NEW_CREDIT_TO_INCOME_RATIO'] = df['AMT_CREDIT'] / df['AMT_INCOME_TOTAL']
for bin_feature in ['CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY']:
df[bin_feature], uniques = pd.factorize(df[bin_feature])
df, cat_cols = one_hot_encoder(df, nan_as_category)
del test_df
gc.collect()
return df
def bureau_and_balance(num_rows = None, nan_as_category = True):
bureau = pd.read_csv('.. /input/bureau.csv', nrows = num_rows)
bb = pd.read_csv('.. /input/bureau_balance.csv', nrows = num_rows)
bb, bb_cat = one_hot_encoder(bb, nan_as_category)
bureau, bureau_cat = one_hot_encoder(bureau, nan_as_category)
bb_aggregations = {'MONTHS_BALANCE': ['min', 'max', 'size']}
for col in bb_cat:
bb_aggregations[col] = ['mean']
bb_agg = bb.groupby('SK_ID_BUREAU' ).agg(bb_aggregations)
bb_agg.columns = pd.Index([e[0] + "_" + e[1].upper() for e in bb_agg.columns.tolist() ])
bureau = bureau.join(bb_agg, how='left', on='SK_ID_BUREAU')
bureau.drop(['SK_ID_BUREAU'], axis=1, inplace= True)
del bb, bb_agg
gc.collect()
num_aggregations = {
'DAYS_CREDIT': ['min', 'max', 'mean', 'var'],
'DAYS_CREDIT_ENDDATE': ['min', 'max', 'mean'],
'DAYS_CREDIT_UPDATE': ['mean'],
'CREDIT_DAY_OVERDUE': ['max', 'mean'],
'AMT_CREDIT_MAX_OVERDUE': ['mean'],
'AMT_CREDIT_SUM': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_DEBT': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_OVERDUE': ['mean'],
'AMT_CREDIT_SUM_LIMIT': ['mean', 'sum'],
'AMT_ANNUITY': ['max', 'mean'],
'CNT_CREDIT_PROLONG': ['sum'],
'MONTHS_BALANCE_MIN': ['min'],
'MONTHS_BALANCE_MAX': ['max'],
'MONTHS_BALANCE_SIZE': ['mean', 'sum']
}
cat_aggregations = {}
for cat in bureau_cat: cat_aggregations[cat] = ['mean']
for cat in bb_cat: cat_aggregations[cat + "_MEAN"] = ['mean']
bureau_agg = bureau.groupby('SK_ID_CURR' ).agg({**num_aggregations, **cat_aggregations})
bureau_agg.columns = pd.Index(['BURO_' + e[0] + "_" + e[1].upper() for e in bureau_agg.columns.tolist() ])
active = bureau[bureau['CREDIT_ACTIVE_Active'] == 1]
active_agg = active.groupby('SK_ID_CURR' ).agg(num_aggregations)
cols = active_agg.columns.tolist()
active_agg.columns = pd.Index(['ACTIVE_' + e[0] + "_" + e[1].upper() for e in active_agg.columns.tolist() ])
bureau_agg = bureau_agg.join(active_agg, how='left', on='SK_ID_CURR')
del active, active_agg
gc.collect()
closed = bureau[bureau['CREDIT_ACTIVE_Closed'] == 1]
closed_agg = closed.groupby('SK_ID_CURR' ).agg(num_aggregations)
closed_agg.columns = pd.Index(['CLOSED_' + e[0] + "_" + e[1].upper() for e in closed_agg.columns.tolist() ])
bureau_agg = bureau_agg.join(closed_agg, how='left', on='SK_ID_CURR')
for e in cols:
bureau_agg['NEW_RATIO_BURO_' + e[0] + "_" + e[1].upper() ] = bureau_agg['ACTIVE_' + e[0] + "_" + e[1].upper() ] / bureau_agg['CLOSED_' + e[0] + "_" + e[1].upper() ]
del closed, closed_agg, bureau
gc.collect()
return bureau_agg
def previous_applications(num_rows = None, nan_as_category = True):
prev = pd.read_csv('.. /input/previous_application.csv', nrows = num_rows)
prev, cat_cols = one_hot_encoder(prev, nan_as_category= True)
prev['DAYS_FIRST_DRAWING'].replace(365243, np.nan, inplace= True)
prev['DAYS_FIRST_DUE'].replace(365243, np.nan, inplace= True)
prev['DAYS_LAST_DUE_1ST_VERSION'].replace(365243, np.nan, inplace= True)
prev['DAYS_LAST_DUE'].replace(365243, np.nan, inplace= True)
prev['DAYS_TERMINATION'].replace(365243, np.nan, inplace= True)
prev['APP_CREDIT_PERC'] = prev['AMT_APPLICATION'] / prev['AMT_CREDIT']
num_aggregations = {
'AMT_ANNUITY': ['min', 'max', 'mean'],
'AMT_APPLICATION': ['min', 'max', 'mean'],
'AMT_CREDIT': ['min', 'max', 'mean'],
'APP_CREDIT_PERC': ['min', 'max', 'mean', 'var'],
'AMT_DOWN_PAYMENT': ['min', 'max', 'mean'],
'AMT_GOODS_PRICE': ['min', 'max', 'mean'],
'HOUR_APPR_PROCESS_START': ['min', 'max', 'mean'],
'RATE_DOWN_PAYMENT': ['min', 'max', 'mean'],
'DAYS_DECISION': ['min', 'max', 'mean'],
'CNT_PAYMENT': ['mean', 'sum'],
}
cat_aggregations = {}
for cat in cat_cols:
cat_aggregations[cat] = ['mean']
prev_agg = prev.groupby('SK_ID_CURR' ).agg({**num_aggregations, **cat_aggregations})
prev_agg.columns = pd.Index(['PREV_' + e[0] + "_" + e[1].upper() for e in prev_agg.columns.tolist() ])
approved = prev[prev['NAME_CONTRACT_STATUS_Approved'] == 1]
approved_agg = approved.groupby('SK_ID_CURR' ).agg(num_aggregations)
cols = approved_agg.columns.tolist()
approved_agg.columns = pd.Index(['APPROVED_' + e[0] + "_" + e[1].upper() for e in approved_agg.columns.tolist() ])
prev_agg = prev_agg.join(approved_agg, how='left', on='SK_ID_CURR')
refused = prev[prev['NAME_CONTRACT_STATUS_Refused'] == 1]
refused_agg = refused.groupby('SK_ID_CURR' ).agg(num_aggregations)
refused_agg.columns = pd.Index(['REFUSED_' + e[0] + "_" + e[1].upper() for e in refused_agg.columns.tolist() ])
prev_agg = prev_agg.join(refused_agg, how='left', on='SK_ID_CURR')
del refused, refused_agg, approved, approved_agg, prev
for e in cols:
prev_agg['NEW_RATIO_PREV_' + e[0] + "_" + e[1].upper() ] = prev_agg['APPROVED_' + e[0] + "_" + e[1].upper() ] / prev_agg['REFUSED_' + e[0] + "_" + e[1].upper() ]
gc.collect()
return prev_agg
def pos_cash(num_rows = None, nan_as_category = True):
pos = pd.read_csv('.. /input/POS_CASH_balance.csv', nrows = num_rows)
pos, cat_cols = one_hot_encoder(pos, nan_as_category= True)
aggregations = {
'MONTHS_BALANCE': ['max', 'mean', 'size'],
'SK_DPD': ['max', 'mean'],
'SK_DPD_DEF': ['max', 'mean']
}
for cat in cat_cols:
aggregations[cat] = ['mean']
pos_agg = pos.groupby('SK_ID_CURR' ).agg(aggregations)
pos_agg.columns = pd.Index(['POS_' + e[0] + "_" + e[1].upper() for e in pos_agg.columns.tolist() ])
pos_agg['POS_COUNT'] = pos.groupby('SK_ID_CURR' ).size()
del pos
gc.collect()
return pos_agg
def installments_payments(num_rows = None, nan_as_category = True):
ins = pd.read_csv('.. /input/installments_payments.csv', nrows = num_rows)
ins, cat_cols = one_hot_encoder(ins, nan_as_category= True)
ins['PAYMENT_PERC'] = ins['AMT_PAYMENT'] / ins['AMT_INSTALMENT']
ins['PAYMENT_DIFF'] = ins['AMT_INSTALMENT'] - ins['AMT_PAYMENT']
ins['DPD'] = ins['DAYS_ENTRY_PAYMENT'] - ins['DAYS_INSTALMENT']
ins['DBD'] = ins['DAYS_INSTALMENT'] - ins['DAYS_ENTRY_PAYMENT']
ins['DPD'] = ins['DPD'].apply(lambda x: x if x > 0 else 0)
ins['DBD'] = ins['DBD'].apply(lambda x: x if x > 0 else 0)
aggregations = {
'NUM_INSTALMENT_VERSION': ['nunique'],
'DPD': ['max', 'mean', 'sum'],
'DBD': ['max', 'mean', 'sum'],
'PAYMENT_PERC': ['max', 'mean', 'sum', 'var'],
'PAYMENT_DIFF': ['max', 'mean', 'sum', 'var'],
'AMT_INSTALMENT': ['max', 'mean', 'sum'],
'AMT_PAYMENT': ['min', 'max', 'mean', 'sum'],
'DAYS_ENTRY_PAYMENT': ['max', 'mean', 'sum']
}
for cat in cat_cols:
aggregations[cat] = ['mean']
ins_agg = ins.groupby('SK_ID_CURR' ).agg(aggregations)
ins_agg.columns = pd.Index(['INSTAL_' + e[0] + "_" + e[1].upper() for e in ins_agg.columns.tolist() ])
ins_agg['INSTAL_COUNT'] = ins.groupby('SK_ID_CURR' ).size()
del ins
gc.collect()
return ins_agg
def credit_card_balance(num_rows = None, nan_as_category = True):
cc = pd.read_csv('.. /input/credit_card_balance.csv', nrows = num_rows)
cc, cat_cols = one_hot_encoder(cc, nan_as_category= True)
cc.drop(['SK_ID_PREV'], axis= 1, inplace = True)
cc_agg = cc.groupby('SK_ID_CURR' ).agg(['min', 'max', 'mean', 'sum', 'var'])
cc_agg.columns = pd.Index(['CC_' + e[0] + "_" + e[1].upper() for e in cc_agg.columns.tolist() ])
cc_agg['CC_COUNT'] = cc.groupby('SK_ID_CURR' ).size()
del cc
gc.collect()
return cc_agg | Home Credit Default Risk |
1,443,616 | prediction.to_csv("submittion.csv", index_label='id' )<categorify> | warnings.simplefilter(action='ignore', category=FutureWarning)
debug = None
num_rows = 10000 if debug else None
df = application_train_test(num_rows)
with timer("Process bureau and bureau_balance"):
bureau = bureau_and_balance(num_rows)
print("Bureau df shape:", bureau.shape)
df = df.join(bureau, how='left', on='SK_ID_CURR')
del bureau
gc.collect()
with timer("Process previous_applications"):
prev = previous_applications(num_rows)
print("Previous applications df shape:", prev.shape)
df = df.join(prev, how='left', on='SK_ID_CURR')
del prev
gc.collect()
with timer("Process POS-CASH balance"):
pos = pos_cash(num_rows)
print("Pos-cash balance df shape:", pos.shape)
df = df.join(pos, how='left', on='SK_ID_CURR')
del pos
gc.collect()
with timer("Process installments payments"):
ins = installments_payments(num_rows)
print("Installments payments df shape:", ins.shape)
df = df.join(ins, how='left', on='SK_ID_CURR')
del ins
gc.collect()
with timer("Process credit card balance"):
cc = credit_card_balance(num_rows)
print("Credit card balance df shape:", cc.shape)
df = df.join(cc, how='left', on='SK_ID_CURR')
del cc
gc.collect() | Home Credit Default Risk |
1,443,616 | def createSubmission(filename,coords,classes,test_directory = '.. /input/test/test/'):
if coords.shape !=(225,2):
raise ValueError('coords must have shape(225,2)')
if classes.shape !=(225,31):
raise ValueError('classes must have shape(225,31)')
files = os.listdir(test_directory)
with open(filename + '.csv','w')as f:
f.write('Nr,X,Y,' + ",".join(['C' + str(i)for i in range(0,31)])+'
')
for n,coords,id_ in zip(classes,coords,files):
d = np.zeros(shape=(31,))
d[np.argmax(n[0])] = 1.0
class_ = str(d.tolist() ).replace('[','' ).replace(']','' ).replace(' ','')
x_coords = coords[0]
y_coords = coords[1]
string = str(id_)+ ',' + str(x_coords)+ ',' + str(y_coords)+ ',' + str(class_)+ '
'
f.write(string)
<prepare_output> | feats = [f for f in df.columns if f not in ['TARGET','SK_ID_CURR','SK_ID_BUREAU','SK_ID_PREV','index']]
for c in feats:
ss = StandardScaler()
df.loc[~np.isfinite(df[c]),c] = np.nan
df.loc[~df[c].isnull() ,c] = ss.fit_transform(df.loc[~df[c].isnull() ,c].values.reshape(-1,1))
df[c].fillna(-99999.,inplace=True ) | Home Credit Default Risk |
1,443,616 | classes = np.random.rand(225,31)
classes[classes >= 0.5] = 1.0
classes[classes != 1.0] = 0
createSubmission("submission.csv",np.random.rand(225,2),classes )<compute_test_metric> | def Output(p):
return 1./(1.+np.exp(-p))
def GP1(data):
v = pd.DataFrame()
v["i0"] = 0.010000*np.tanh(((((((( -1.0*(((((((((( data["CODE_GENDER"])>(data["NEW_EXT_SOURCES_MEAN"])) *1.))>(( -1.0*(( data["CLOSED_AMT_CREDIT_SUM_SUM"])))))*1.))+(((data["NEW_EXT_SOURCES_MEAN"])*(3.141593)))))))) * 2.0)) * 2.0)) * 2.0))
v["i1"] = 0.034000*np.tanh(((((((((((( -1.0*(( data["EXT_SOURCE_2"])))) -(np.tanh(((( data["EXT_SOURCE_3"])* 2.0)))))) * 2.0)) * 2.0)) -(np.where(data["EXT_SOURCE_2"]<0, 1.570796, data["EXT_SOURCE_2"])))) * 2.0))
v["i2"] = 0.015000*np.tanh(((((((((np.where(data["NEW_SOURCES_PROD"]>0, data["NEW_EXT_SOURCES_MEAN"], data["NEW_CREDIT_TO_GOODS_RATIO"])) -(((((data["NEW_EXT_SOURCES_MEAN"])* 2.0)) -(np.tanh(( data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"])))))))-(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) * 2.0))
v["i3"] = 0.025000*np.tanh(((((np.maximum(((np.maximum(((-3.0)) ,(( data["NEW_SOURCES_PROD"]))))),(( data["EXT_SOURCE_3"])))) *(((-3.0)* 2.0)))) +(((data["EXT_SOURCE_2"])*(np.minimum(((-3.0)) ,(( data["EXT_SOURCE_3"]))))))))
v["i4"] = 0.047000*np.tanh(((((((((data["NEW_EXT_SOURCES_MEAN"])*(np.where(data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"]>0, -2.0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"])))) * 2.0)) * 2.0)) +(np.where(data["CC_CNT_DRAWINGS_ATM_CURRENT_VAR"]>0,(-1.0*(( data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"]))), data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"]))))
v["i5"] = 0.046000*np.tanh(((( -1.0*(((( data["NEW_EXT_SOURCES_MEAN"])+(((data["NEW_EXT_SOURCES_MEAN"])+(((((data["NEW_EXT_SOURCES_MEAN"])+(((( data["EXT_SOURCE_3"])>(data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])) *1.))))+(( -1.0*(( data["NEW_CREDIT_TO_GOODS_RATIO"])))))))))))))* 2.0))
v["i6"] = 0.010024*np.tanh(((((((((((((data["NEW_CREDIT_TO_GOODS_RATIO"])-(np.tanh(( data["APPROVED_APP_CREDIT_PERC_MAX"])))))-(((data["NEW_EXT_SOURCES_MEAN"])* 2.0)))) -(((( data["NEW_EXT_SOURCES_MEAN"])>(data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])) *1.))))* 2.0)) * 2.0)) * 2.0))
v["i7"] = 0.029998*np.tanh(((((((((((((( -1.0*(( data["NEW_EXT_SOURCES_MEAN"])))) * 2.0)) -(((((( data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"])* 2.0)) <(data["INSTAL_AMT_PAYMENT_MEAN"])) *1.))))* 2.0)) * 2.0)) -(( -1.0*(( data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])))))) * 2.0))
v["i8"] = 0.040000*np.tanh(((((((((((np.tanh(((((( data["DAYS_EMPLOYED"])+(data["NEW_CREDIT_TO_GOODS_RATIO"])))-(data["BURO_CREDIT_ACTIVE_Closed_MEAN"])))))+(np.tanh(( data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))))-(data["EXT_SOURCE_2"])))* 2.0)) * 2.0)) * 2.0))
v["i9"] = 0.041000*np.tanh(((((((((((data["NEW_CREDIT_TO_GOODS_RATIO"])+(( -1.0*(((((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)) +(data["CODE_GENDER"])))))))) -(np.maximum(((data["EXT_SOURCE_3"])) ,(( data["NAME_EDUCATION_TYPE_Higher_education"])))))) * 2.0)) * 2.0)) * 2.0))
v["i10"] = 0.035006*np.tanh(((((((np.maximum(((data["CC_CNT_DRAWINGS_ATM_CURRENT_VAR"])) ,(((((((-1.0*(((( data["NEW_EXT_SOURCES_MEAN"])+(((( data["NEW_EXT_SOURCES_MEAN"])>(data["DAYS_EMPLOYED"])) *1.))))))) * 2.0)) +(data["NEW_CREDIT_TO_GOODS_RATIO"])))))) * 2.0)) * 2.0)) * 2.0))
v["i11"] = 0.043600*np.tanh(((((((((((((data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])+(np.where(data["NEW_EXT_SOURCES_MEAN"] > -1, data["DAYS_EMPLOYED"], 3.0)))) -(((data["NEW_EXT_SOURCES_MEAN"])* 2.0)))) * 2.0)) -(data["CODE_GENDER"])))* 2.0)) * 2.0))
v["i12"] = 0.012760*np.tanh(((((((((((np.tanh(((( data["DAYS_EMPLOYED"])-(np.maximum(((data["PREV_APP_CREDIT_PERC_MEAN"])) ,(((( data["NEW_EXT_SOURCES_MEAN"])-(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])))))))))) -(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i13"] = 0.041000*np.tanh(((((( -1.0*(((((((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)) +(((((( data["INSTAL_AMT_PAYMENT_MIN"])* 2.0)) >(data["DAYS_EMPLOYED"])) *1.))))+(((( data["ORGANIZATION_TYPE_Bank"])>(data["APPROVED_DAYS_DECISION_MIN"])) *1.))))))) * 2.0)) * 2.0))
v["i14"] = 0.046700*np.tanh(((((data["NEW_CREDIT_TO_GOODS_RATIO"])+(((((np.tanh(( data["PREV_NAME_PRODUCT_TYPE_walk_in_MEAN"])))+(((np.maximum(((( -1.0*(( data["NEW_EXT_SOURCES_MEAN"]))))),(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])))) +(np.tanh(( data["DAYS_EMPLOYED"])))))))* 2.0)))) * 2.0))
v["i15"] = 0.000015*np.tanh(((((np.where(data["EXT_SOURCE_1"]<0,(( data["NEW_CREDIT_TO_GOODS_RATIO"])+(((data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])+(((( -1.0*(( data["EXT_SOURCE_3"])))) * 2.0))))),(( data["CC_AMT_TOTAL_RECEIVABLE_MEAN"])-(data["EXT_SOURCE_1"])))) * 2.0)) * 2.0))
v["i16"] = 0.005000*np.tanh(((data["NEW_EXT_SOURCES_MEAN"])*(((np.minimum(((np.minimum(((((data["NEW_EXT_SOURCES_MEAN"])*(((data["NEW_RATIO_PREV_AMT_DOWN_PAYMENT_MAX"])+(data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"])))))) ,(( data["EXT_SOURCE_1"]))))),(( data["EXT_SOURCE_3"])))) +(data["EXT_SOURCE_3"])))))
v["i17"] = 0.049700*np.tanh(((((((((np.maximum(((data["CC_AMT_RECIVABLE_MAX"])) ,(((-1.0*(((((data["DAYS_EMPLOYED"])<(((data["APPROVED_AMT_DOWN_PAYMENT_MAX"])-(data["APPROVED_CNT_PAYMENT_MEAN"])))) *1.))))))))-(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) -(data["NAME_EDUCATION_TYPE_Higher_education"])))* 2.0))
v["i18"] = 0.045088*np.tanh(((((((np.tanh(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])))+(((((np.maximum(((data["DAYS_EMPLOYED"])) ,(( data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))) -(data["NEW_EXT_SOURCES_MEAN"])))+(((( data["DEF_30_CNT_SOCIAL_CIRCLE"])+(data["NEW_CREDIT_TO_GOODS_RATIO"])) /2.0)))))) * 2.0)) * 2.0))
v["i19"] = 0.047500*np.tanh(((((np.where(data["NEW_SOURCES_PROD"] > -1, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"],(((( data["NEW_DOC_IND_KURT"])-(((data["EXT_SOURCE_3"])-(((data["DAYS_BIRTH"])-(data["EXT_SOURCE_2"])))))))* 2.0)))-(data["EXT_SOURCE_3"])))* 2.0))
v["i20"] = 0.042704*np.tanh(((((data["NEW_CREDIT_TO_GOODS_RATIO"])+(((((data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])+(((((((data["PREV_CNT_PAYMENT_MEAN"])-(data["NEW_EXT_SOURCES_MEAN"])))-(data["CODE_GENDER"])))-(data["APPROVED_APP_CREDIT_PERC_MAX"])))))-(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])))))* 2.0))
v["i21"] = 0.048960*np.tanh(((((((((((((((data["INSTAL_DAYS_ENTRY_PAYMENT_SUM"])-(data["NEW_EXT_SOURCES_MEAN"])))+(data["INSTAL_PAYMENT_DIFF_MAX"])))+(data["INSTAL_PAYMENT_DIFF_MAX"])))* 2.0)) +(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])))+(data["NEW_CREDIT_TO_GOODS_RATIO"])))+(data["NAME_INCOME_TYPE_Working"])))
v["i22"] = 0.049970*np.tanh(((((((((np.tanh(((( np.where(data["EXT_SOURCE_1"] > -1, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"],(( data["PREV_NAME_PRODUCT_TYPE_walk_in_MEAN"])-(data["BURO_CREDIT_ACTIVE_Closed_MEAN"])))) * 2.0)))) -(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) * 2.0)) * 2.0))
v["i23"] = 0.049950*np.tanh(((((data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])+(((data["REGION_RATING_CLIENT_W_CITY"])+(((np.where(np.maximum(((data["PREV_AMT_DOWN_PAYMENT_MAX"])) ,(( data["EXT_SOURCE_3"])))>0,(( data["CC_CNT_DRAWINGS_ATM_CURRENT_VAR"])* 2.0),(-1.0*(( data["EXT_SOURCE_3"])))))* 2.0)))))) * 2.0))
v["i24"] = 0.049985*np.tanh(((((np.where(data["EXT_SOURCE_1"] > -1, data["CODE_GENDER"], data["DAYS_BIRTH"])) +(((data["REGION_RATING_CLIENT_W_CITY"])+(data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))))+(((data["PREV_NAME_YIELD_GROUP_high_MEAN"])+(((data["PREV_CNT_PAYMENT_MEAN"])-(data["CODE_GENDER"])))))))
v["i25"] = 0.049800*np.tanh(((((((((((((((data["PREV_CNT_PAYMENT_MEAN"])-(data["POS_MONTHS_BALANCE_SIZE"])))-(np.maximum(((data["NEW_CAR_TO_BIRTH_RATIO"])) ,(( data["CODE_GENDER"])))))) -(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) +(data["NEW_DOC_IND_KURT"])))* 2.0)) * 2.0))
v["i26"] = 0.049993*np.tanh(((((data["DAYS_EMPLOYED"])+(data["NEW_ANNUITY_TO_INCOME_RATIO"])))-(((data["EXT_SOURCE_2"])-(((((np.tanh(( data["REFUSED_DAYS_DECISION_MAX"])))+(((((data["INSTAL_PAYMENT_DIFF_MEAN"])* 2.0)) * 2.0)))) -(data["NAME_EDUCATION_TYPE_Higher_education"])))))))
v["i27"] = 0.049841*np.tanh(((np.tanh(((-1.0*(( data["BURO_CREDIT_ACTIVE_Closed_MEAN"])))))) -(((((((data["CODE_GENDER"])-(data["NEW_ANNUITY_TO_INCOME_RATIO"])))+(data["NEW_EXT_SOURCES_MEAN"])))-(np.where(data["POS_MONTHS_BALANCE_SIZE"]<0, data["FLAG_DOCUMENT_3"], data["REFUSED_AMT_GOODS_PRICE_MAX"]))))))
v["i28"] = 0.050000*np.tanh(((data["APPROVED_CNT_PAYMENT_MEAN"])-(((data["NEW_EXT_SOURCES_MEAN"])+(((data["PREV_NAME_YIELD_GROUP_low_action_MEAN"])+(((np.maximum(((data["NEW_CAR_TO_BIRTH_RATIO"])) ,(((((( data["INSTAL_AMT_PAYMENT_MIN"])* 2.0)) +(data["INSTAL_DBD_SUM"])))))) * 2.0))))))))
v["i29"] = 0.049911*np.tanh(((((data["DEF_30_CNT_SOCIAL_CIRCLE"])+(((((((((data["INSTAL_PAYMENT_DIFF_MAX"])* 2.0)) -(data["PREV_APP_CREDIT_PERC_MAX"])))+(( -1.0*(( data["APPROVED_AMT_ANNUITY_MEAN"])))))) * 2.0)))) -(((data["NEW_EXT_SOURCES_MEAN"])-(data["AMT_ANNUITY"])))))
v["i30"] = 0.046032*np.tanh(((((((data["DAYS_LAST_PHONE_CHANGE"])+(((data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])+(data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))))-(data["APPROVED_AMT_DOWN_PAYMENT_MAX"])))+(np.where(data["EXT_SOURCE_1"]>0, data["REFUSED_DAYS_DECISION_MEAN"],(( data["NEW_CREDIT_TO_GOODS_RATIO"])+(data["DAYS_EMPLOYED"]))))))
v["i31"] = 0.049970*np.tanh(((data["NEW_CREDIT_TO_GOODS_RATIO"])+(((((((data["PREV_NAME_CLIENT_TYPE_New_MEAN"])-(data["INSTAL_AMT_PAYMENT_MIN"])))+(((data["PREV_CNT_PAYMENT_MEAN"])+(((((data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])-(data["INSTAL_AMT_PAYMENT_MIN"])))-(data["POS_MONTHS_BALANCE_SIZE"])))))))* 2.0))))
v["i32"] = 0.049504*np.tanh(((((np.where(data["EXT_SOURCE_3"] > -1, data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"], np.maximum(((data["REFUSED_DAYS_DECISION_MAX"])) ,(( np.maximum(((data["BURO_DAYS_CREDIT_MAX"])) ,(((((( data["EXT_SOURCE_3"])-(data["EXT_SOURCE_1"])))-(data["CODE_GENDER"])))))))))) * 2.0)) * 2.0))
v["i33"] = 0.049920*np.tanh(np.where(data["NAME_EDUCATION_TYPE_Higher_education"]>0, data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"],(((( np.where(data["PREV_NAME_YIELD_GROUP_low_action_MEAN"]>0, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"], np.where(data["PREV_PRODUCT_COMBINATION_POS_industry_with_interest_MEAN"]>0, data["CC_CNT_DRAWINGS_CURRENT_MAX"],(( data["NEW_CAR_TO_BIRTH_RATIO"])*(data["CC_CNT_INSTALMENT_MATURE_CUM_VAR"])))))* 2.0)) * 2.0)))
v["i34"] = 0.048501*np.tanh(np.where(data["INSTAL_DPD_MEAN"]<0,(((( np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0,(( data["NEW_CREDIT_TO_ANNUITY_RATIO"])+(((data["FLAG_DOCUMENT_3"])+(data["FLAG_DOCUMENT_3"])))) , data["CC_CNT_DRAWINGS_ATM_CURRENT_VAR"])) * 2.0)) * 2.0),(-1.0*(( data["CC_AMT_CREDIT_LIMIT_ACTUAL_MEAN"])))))
v["i35"] = 0.048340*np.tanh(np.where(data["INSTAL_DPD_MEAN"]>0, 3.0,(((((((((( data["APPROVED_CNT_PAYMENT_MEAN"])-(data["APPROVED_AMT_ANNUITY_MEAN"])))+(data["REG_CITY_NOT_LIVE_CITY"])))* 2.0)) +(((data["REGION_RATING_CLIENT_W_CITY"])-(data["CODE_GENDER"])))))* 2.0)))
v["i36"] = 0.048800*np.tanh(((((((((((np.where(((data["AMT_ANNUITY"])-(data["APPROVED_AMT_ANNUITY_MEAN"])) <0, data["CC_AMT_RECEIVABLE_PRINCIPAL_MEAN"],(-1.0*(( np.tanh(( data["NEW_CAR_TO_EMPLOY_RATIO"])))))))* 2.0)) -(data["NAME_INCOME_TYPE_State_servant"])))* 2.0)) * 2.0)) * 2.0))
v["i37"] = 0.049972*np.tanh(((np.where(data["POS_SK_DPD_DEF_MAX"]>0, 3.141593,(( data["DEF_30_CNT_SOCIAL_CIRCLE"])+(np.where(data["NEW_SOURCES_PROD"] > -1, data["DEF_30_CNT_SOCIAL_CIRCLE"],(((((( data["PREV_CNT_PAYMENT_SUM"])-(data["POS_MONTHS_BALANCE_SIZE"])))* 2.0)) * 2.0)))))) * 2.0))
v["i38"] = 0.046442*np.tanh(((((((data["INSTAL_PAYMENT_DIFF_MAX"])-(data["APPROVED_AMT_ANNUITY_MEAN"])))+(((np.maximum(((data["CC_AMT_RECEIVABLE_PRINCIPAL_MEAN"])) ,(( np.maximum(((data["APPROVED_CNT_PAYMENT_MEAN"])) ,(( data["DEF_60_CNT_SOCIAL_CIRCLE"])))))))-(np.maximum(((data["APPROVED_APP_CREDIT_PERC_VAR"])) ,(( data["NAME_FAMILY_STATUS_Married"])))))))) * 2.0))
v["i39"] = 0.049950*np.tanh(((((((np.where(np.maximum(((data["BURO_CREDIT_ACTIVE_Closed_MEAN"])) ,(( data["APPROVED_HOUR_APPR_PROCESS_START_MAX"])))<0,(( data["DAYS_ID_PUBLISH"])-(data["FLOORSMAX_AVG"])) , np.where(data["AMT_ANNUITY"]<0, data["CC_CNT_DRAWINGS_CURRENT_MEAN"], data["NEW_CREDIT_TO_GOODS_RATIO"])))* 2.0)) * 2.0)) * 2.0))
v["i40"] = 0.049998*np.tanh(((((data["INSTAL_AMT_INSTALMENT_MAX"])+(((((data["INSTAL_DPD_MEAN"])-(((data["PREV_NAME_YIELD_GROUP_low_action_MEAN"])+(data["PREV_NAME_YIELD_GROUP_low_normal_MEAN"])))))-(np.maximum(((data["NEW_SOURCES_PROD"])) ,(((( data["CODE_GENDER"])+(data["APPROVED_AMT_ANNUITY_MEAN"])))))))))) * 2.0))
v["i41"] = 0.047701*np.tanh(((((((((((((data["INSTAL_PAYMENT_DIFF_MEAN"])+(np.minimum(((data["AMT_ANNUITY"])) ,(( data["REGION_RATING_CLIENT_W_CITY"])))))) -(((data["POS_MONTHS_BALANCE_SIZE"])* 2.0)))) +(data["APPROVED_CNT_PAYMENT_SUM"])))* 2.0)) +(data["APPROVED_CNT_PAYMENT_SUM"])))* 2.0))
v["i42"] = 0.049932*np.tanh(((data["NEW_ANNUITY_TO_INCOME_RATIO"])+(((((np.where(data["CC_AMT_CREDIT_LIMIT_ACTUAL_MEAN"] > -1, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"], np.tanh(( data["DAYS_EMPLOYED"])))) +(((np.maximum(((data["INSTAL_PAYMENT_DIFF_MAX"])) ,(( data["ACTIVE_DAYS_CREDIT_MAX"])))) * 2.0)))) -(data["OCCUPATION_TYPE_Core_staff"])))))
v["i43"] = 0.049520*np.tanh(((((np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]>0, data["CC_AMT_INST_MIN_REGULARITY_SUM"],(( np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_ENDDATE_MIN"]>0, data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"],(( data["FLAG_WORK_PHONE"])+(((data["DAYS_LAST_PHONE_CHANGE"])-(data["INSTAL_AMT_PAYMENT_MIN"])))))) * 2.0)))* 2.0)) * 2.0))
v["i44"] = 0.049798*np.tanh(((data["PREV_CNT_PAYMENT_MEAN"])+(((np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]>0,(( data["PREV_CNT_PAYMENT_MEAN"])-(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])) , data["PREV_DAYS_DECISION_MIN"])) -(((data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])+(((data["APPROVED_AMT_APPLICATION_MIN"])+(data["PREV_NAME_YIELD_GROUP_low_action_MEAN"])))))))))
v["i45"] = 0.049566*np.tanh(np.where(data["INSTAL_DPD_MEAN"]<0,(( data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])-(((data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"])*(data["PREV_DAYS_DECISION_MEAN"])))) , np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"]>0, data["DAYS_LAST_PHONE_CHANGE"],(((-1.0*(( data["CC_AMT_CREDIT_LIMIT_ACTUAL_SUM"])))) -(data["CC_AMT_CREDIT_LIMIT_ACTUAL_SUM"])))))
v["i46"] = 0.049300*np.tanh(np.where(data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"]<0, np.where(((data["ACTIVE_DAYS_CREDIT_ENDDATE_MEAN"])-(data["DEF_30_CNT_SOCIAL_CIRCLE"])) <0,(( data["DEF_30_CNT_SOCIAL_CIRCLE"])-(np.maximum(((data["ACTIVE_DAYS_CREDIT_UPDATE_MEAN"])) ,(((( data["NAME_FAMILY_STATUS_Married"])* 2.0)))))) , data["ACTIVE_DAYS_CREDIT_MAX"]), data["ACTIVE_DAYS_CREDIT_MAX"]))
v["i47"] = 0.049902*np.tanh(( -1.0*(( np.where(data["PREV_APP_CREDIT_PERC_MEAN"]<0, np.where(data["CC_AMT_CREDIT_LIMIT_ACTUAL_SUM"]<0, data["LIVINGAREA_AVG"], data["CC_AMT_CREDIT_LIMIT_ACTUAL_SUM"]),(((data["PREV_NAME_TYPE_SUITE_nan_MEAN"])<(((( data["CC_AMT_BALANCE_MEAN"])<(data["PREV_APP_CREDIT_PERC_MEAN"])) *1.))) *1.))))))
v["i48"] = 0.047562*np.tanh(((data["REGION_RATING_CLIENT_W_CITY"])+(np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]<0, np.where(data["AMT_GOODS_PRICE"] > -1, np.where(data["OCCUPATION_TYPE_Core_staff"]>0, -3.0,(( data["PREV_NAME_TYPE_SUITE_nan_MEAN"])-(data["NEW_CAR_TO_EMPLOY_RATIO"]))), data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"]), -3.0))))
v["i49"] = 0.049943*np.tanh(np.where(np.maximum(((data["ORGANIZATION_TYPE_Construction"])) ,(( np.maximum(((data["CC_CNT_DRAWINGS_CURRENT_VAR"])) ,(( data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])))))) >0, 3.141593,(((((( data["ORGANIZATION_TYPE_Self_employed"])* 2.0)) * 2.0)) +(np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN"])))))))
v["i50"] = 0.049280*np.tanh(((((((np.where(((data["PREV_AMT_ANNUITY_MEAN"])+(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])) >0,(( data["APPROVED_CNT_PAYMENT_MEAN"])-(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])) ,(( data["AMT_ANNUITY"])+(((data["NEW_DOC_IND_KURT"])* 2.0)))))* 2.0)) * 2.0)) * 2.0))
v["i51"] = 0.044800*np.tanh(((((((np.maximum(((data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])) ,(( np.where(data["NEW_SOURCES_PROD"]>0, data["REG_CITY_NOT_LIVE_CITY"],(( np.where(data["NEW_EXT_SOURCES_MEAN"] > -1, data["NEW_EXT_SOURCES_MEAN"], data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])) -(data["APPROVED_AMT_DOWN_PAYMENT_MEAN"])))))))* 2.0)) * 2.0)) * 2.0))
v["i52"] = 0.049551*np.tanh(((data["FLAG_WORK_PHONE"])+(((data["INSTAL_PAYMENT_DIFF_MEAN"])+(((((data["DAYS_ID_PUBLISH"])-(data["CODE_GENDER"])))+(((np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0, data["NEW_CREDIT_TO_ANNUITY_RATIO"], data["CC_CNT_DRAWINGS_ATM_CURRENT_MAX"])) -(data["INSTAL_AMT_PAYMENT_SUM"])))))))))
v["i53"] = 0.050000*np.tanh(np.where(data["AMT_REQ_CREDIT_BUREAU_QRT"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_LIMIT_MEAN"], np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_LIMIT_MEAN"],(((( data["EXT_SOURCE_3"])* 2.0)) -(np.where(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"]<0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_LIMIT_MEAN"],(-1.0*(( data["INSTAL_PAYMENT_DIFF_MEAN"])))))))))
v["i54"] = 0.049550*np.tanh(((((((np.where(data["OWN_CAR_AGE"] > -1, data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__high_MEAN"], np.where(data["CLOSED_DAYS_CREDIT_VAR"] > -1, data["ACTIVE_AMT_CREDIT_SUM_SUM"],(( data["NAME_INCOME_TYPE_Working"])-(((( data["ACTIVE_AMT_CREDIT_SUM_SUM"])+(data["NEW_EMPLOY_TO_BIRTH_RATIO"])) /2.0)))))) * 2.0)) * 2.0)) * 2.0))
v["i55"] = 0.049100*np.tanh(((np.where(data["CC_CNT_INSTALMENT_MATURE_CUM_SUM"]>0, data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"],(( data["NEW_DOC_IND_KURT"])+(((np.where(data["CC_AMT_RECEIVABLE_PRINCIPAL_MEAN"] > -1, data["CC_AMT_RECIVABLE_MEAN"],(( data["DAYS_REGISTRATION"])-(data["NEW_EMPLOY_TO_BIRTH_RATIO"])))) * 2.0)))))* 2.0))
v["i56"] = 0.048334*np.tanh(((((((data["ORGANIZATION_TYPE_Business_Entity_Type_3"])* 2.0)) +(data["REG_CITY_NOT_LIVE_CITY"])))+(np.maximum(((data["APPROVED_CNT_PAYMENT_SUM"])) ,(( np.maximum(((data["OCCUPATION_TYPE_Drivers"])) ,(( np.where(data["POS_SK_DPD_DEF_MEAN"]<0, data["BURO_CREDIT_TYPE_Microloan_MEAN"],(-1.0*(( data["NEW_RATIO_PREV_AMT_APPLICATION_MIN"])))))))))))))
v["i57"] = 0.050000*np.tanh(((((((data["INSTAL_PAYMENT_DIFF_SUM"])-(((np.where(data["NEW_DOC_IND_STD"] > -1, np.where(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"]>0, data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"], data["PREV_NAME_PAYMENT_TYPE_Cash_through_the_bank_MEAN"]), data["NEW_DOC_IND_STD"])) * 2.0)))) -(data["CODE_GENDER"])))-(data["NAME_FAMILY_STATUS_Married"])))
v["i58"] = 0.049904*np.tanh(((np.where(data["AMT_GOODS_PRICE"] > -1,(((((((((((( data["NEW_DOC_IND_KURT"])-(data["NEW_CREDIT_TO_ANNUITY_RATIO"])))-(data["NEW_CREDIT_TO_ANNUITY_RATIO"])))+(data["REGION_RATING_CLIENT_W_CITY"])))* 2.0)) * 2.0)) * 2.0), data["NEW_CREDIT_TO_ANNUITY_RATIO"])) * 2.0))
v["i59"] = 0.048700*np.tanh(np.where(data["NEW_DOC_IND_AVG"]>0,(( np.where(data["NEW_CREDIT_TO_GOODS_RATIO"]>0,(( data["AMT_CREDIT"])+(((((( data["AMT_CREDIT"])>(data["INSTAL_DBD_SUM"])) *1.))* 2.0))),(( data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])* 2.0)))* 2.0), data["INSTAL_PAYMENT_DIFF_MEAN"]))
v["i60"] = 0.045160*np.tanh(((((((np.where(((( data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])>(data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])) *1.) >0, data["CC_CNT_DRAWINGS_POS_CURRENT_MIN"], data["BURO_CREDIT_TYPE_Microloan_MEAN"])) -(np.maximum(((data["NEW_CAR_TO_BIRTH_RATIO"])) ,(( data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])))))) -(data["BURO_CREDIT_TYPE_Mortgage_MEAN"])))-(data["NAME_INCOME_TYPE_State_servant"])))
v["i61"] = 0.049550*np.tanh(np.where(data["NEW_EXT_SOURCES_MEAN"]<0, np.where(data["ACTIVE_DAYS_CREDIT_MEAN"]<0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"],(-1.0*(( data["NEW_EXT_SOURCES_MEAN"])))) , np.where(data["NAME_EDUCATION_TYPE_Higher_education"]>0,(-1.0*(( data["NEW_EXT_SOURCES_MEAN"]))),(-1.0*(( data["NEW_SOURCES_PROD"]))))))
v["i62"] = 0.047401*np.tanh(((((((data["POS_SK_DPD_DEF_MAX"])-(data["APPROVED_AMT_APPLICATION_MIN"])))* 2.0)) -(np.maximum(((np.maximum(((data["BURO_CREDIT_TYPE_Car_loan_MEAN"])) ,(( np.maximum(((((data["NAME_INCOME_TYPE_Commercial_associate"])+(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])))) ,(( data["BURO_CREDIT_TYPE_Mortgage_MEAN"])))))))) ,(( data["FLAG_DOCUMENT_8"]))))))
v["i63"] = 0.048088*np.tanh(np.where(data["INSTAL_AMT_INSTALMENT_MAX"]>0, data["APPROVED_CNT_PAYMENT_MEAN"],(((((((( np.maximum(((data["DEF_60_CNT_SOCIAL_CIRCLE"])) ,(( data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])))) * 2.0)) +(data["NEW_SCORES_STD"])))+(np.maximum(((data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"])) ,(( data["BURO_CREDIT_TYPE_Microloan_MEAN"])))))) * 2.0)))
v["i64"] = 0.049282*np.tanh(((data["POS_SK_DPD_DEF_MAX"])-(np.where(((( data["NEW_EXT_SOURCES_MEAN"])+(data["INSTAL_DBD_SUM"])) /2.0)> -1,(( data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"])-(((((data["NEW_EXT_SOURCES_MEAN"])* 2.0)) * 2.0))),(( data["NEW_EXT_SOURCES_MEAN"])+(data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"]))))))
v["i65"] = 0.049900*np.tanh(np.where(((data["ORGANIZATION_TYPE_Military"])-(data["DAYS_BIRTH"])) >0,(((( data["EXT_SOURCE_1"])/ 2.0)) -(data["DAYS_BIRTH"])) ,(-1.0*(((( data["DAYS_BIRTH"])+(((data["DAYS_BIRTH"])+(data["EXT_SOURCE_1"])))))))))
v["i66"] = 0.049906*np.tanh(np.where(data["LANDAREA_AVG"]>0, data["CC_MONTHS_BALANCE_VAR"],(( np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"]<0, data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"], data["CC_AMT_CREDIT_LIMIT_ACTUAL_SUM"])) *(np.where(((( data["INSTAL_PAYMENT_DIFF_MEAN"])+(data["INSTAL_DPD_MEAN"])) /2.0)<0, data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"], data["LANDAREA_AVG"])))))
v["i67"] = 0.050000*np.tanh(((data["PREV_NAME_CLIENT_TYPE_New_MEAN"])+(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])+(np.maximum(((((((data["DAYS_ID_PUBLISH"])-(( -1.0*(( data["REGION_RATING_CLIENT_W_CITY"])))))) +(((data["PREV_NAME_PORTFOLIO_XNA_MEAN"])+(data["APPROVED_CNT_PAYMENT_MEAN"])))))) ,(( data["BURO_CREDIT_TYPE_Microloan_MEAN"]))))))))
v["i68"] = 0.048988*np.tanh(((((data["WALLSMATERIAL_MODE_Stone__brick"])+(((((np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( np.where(data["APPROVED_AMT_ANNUITY_MEAN"] > -1, data["POS_SK_DPD_DEF_MAX"],(((( -1.0*(( data["NEW_PHONE_TO_BIRTH_RATIO"])))) >(data["NEW_EXT_SOURCES_MEAN"])) *1.))))))* 2.0)) * 2.0)))) * 2.0))
v["i69"] = 0.050000*np.tanh(( -1.0*(( np.maximum(((np.where(((( data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"])+(data["INSTAL_AMT_INSTALMENT_MAX"])) /2.0)>0, data["YEARS_BUILD_MEDI"], data["INSTAL_COUNT"]))),(((( np.maximum(((data["BURO_CREDIT_TYPE_Mortgage_MEAN"])) ,(( data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])))) +(data["BURO_CREDIT_TYPE_Car_loan_MEAN"])))))))))
v["i70"] = 0.044000*np.tanh(((((((np.where(((data["APPROVED_AMT_GOODS_PRICE_MAX"])-(np.tanh(( data["POS_MONTHS_BALANCE_SIZE"])))) >0, data["PREV_DAYS_DECISION_MAX"],(((data["POS_NAME_CONTRACT_STATUS_Returned_to_the_store_MEAN"])+(data["AMT_ANNUITY"])) /2.0)))* 2.0)) * 2.0)) -(data["NEW_DOC_IND_AVG"])))
v["i71"] = 0.037587*np.tanh(((np.where(data["BURO_CREDIT_ACTIVE_Closed_MEAN"]>0, np.minimum(((data["REGION_POPULATION_RELATIVE"])) ,(((-1.0*(( data["PREV_AMT_ANNUITY_MEAN"])))))) , np.maximum(((data["FLAG_WORK_PHONE"])) ,(( np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(((( data["BASEMENTAREA_AVG"])*(data["PREV_CODE_REJECT_REASON_XAP_MEAN"])))))))))) * 2.0))
v["i72"] = 0.026552*np.tanh(((((np.where(data["ACTIVE_AMT_CREDIT_SUM_SUM"]<0,(( data["APPROVED_CNT_PAYMENT_SUM"])-(((np.tanh(( np.maximum(((np.maximum(((data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"])) ,(( data["POS_COUNT"]))))),(( data["INSTAL_AMT_PAYMENT_SUM"])))))) * 2.0))), data["BURO_CREDIT_TYPE_Credit_card_MEAN"])) * 2.0)) * 2.0))
v["i73"] = 0.047540*np.tanh(((np.where(data["BURO_CREDIT_TYPE_Microloan_MEAN"]>0, data["BURO_CREDIT_TYPE_Microloan_MEAN"], np.where(data["PREV_AMT_DOWN_PAYMENT_MIN"]>0, data["BURO_CREDIT_TYPE_Microloan_MEAN"], np.where(data["NAME_INCOME_TYPE_Commercial_associate"]>0, data["INSTAL_AMT_PAYMENT_MIN"],(( data["INSTAL_AMT_PAYMENT_MIN"])*(data["NEW_RATIO_PREV_HOUR_APPR_PROCESS_START_MAX"])))))) -(data["PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN"])))
v["i74"] = 0.049300*np.tanh(((((np.maximum(((data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])) ,(( data["NAME_FAMILY_STATUS_Separated"])))) +(data["NAME_HOUSING_TYPE_Municipal_apartment"])))+(((np.where(data["APPROVED_APP_CREDIT_PERC_MIN"]>0, data["INSTAL_DPD_MEAN"],(((data["CC_AMT_PAYMENT_CURRENT_SUM"])<(data["POS_SK_DPD_DEF_MAX"])) *1.))) * 2.0))))
v["i75"] = 0.042080*np.tanh(((np.tanh(( data["NEW_EXT_SOURCES_MEAN"])))-(((((data["NEW_EXT_SOURCES_MEAN"])+(((( data["NEW_EXT_SOURCES_MEAN"])<(data["OCCUPATION_TYPE_Accountants"])) *1.))))+(((( data["FLAG_PHONE"])+(np.maximum(((data["NEW_EMPLOY_TO_BIRTH_RATIO"])) ,(( data["BURO_STATUS_0_MEAN_MEAN"])))))/2.0))))))
v["i76"] = 0.039200*np.tanh(((np.maximum(((data["BURO_STATUS_1_MEAN_MEAN"])) ,(( np.maximum(((data["ORGANIZATION_TYPE_Self_employed"])) ,(((( data["BURO_CREDIT_TYPE_Microloan_MEAN"])+(data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])))))))))+(np.where(data["PREV_CODE_REJECT_REASON_XAP_MEAN"] > -1, data["BURO_AMT_CREDIT_SUM_DEBT_SUM"],(( data["BURO_DAYS_CREDIT_MEAN"])*(data["BURO_AMT_CREDIT_SUM_DEBT_SUM"]))))))
v["i77"] = 0.045016*np.tanh(((np.where(((data["WEEKDAY_APPR_PROCESS_START_MONDAY"])*(data["FLAG_DOCUMENT_18"])) <0, data["NEW_RATIO_BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"], np.where(((data["INSTAL_AMT_INSTALMENT_SUM"])*(data["CC_CNT_INSTALMENT_MATURE_CUM_VAR"])) <0, data["OCCUPATION_TYPE_Laborers"],(( data["ORGANIZATION_TYPE_Industry__type_9"])*(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])))))* 2.0))
v["i78"] = 0.044600*np.tanh(np.where(data["FLOORSMIN_MODE"]<0,(( np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"]<0,(((( data["APPROVED_AMT_CREDIT_MAX"])-(data["INSTAL_AMT_PAYMENT_SUM"])))* 2.0),(( data["PREV_NAME_CONTRACT_STATUS_Canceled_MEAN"])*(data["FLOORSMIN_MODE"])))) * 2.0), data["NEW_DOC_IND_KURT"]))
v["i79"] = 0.047706*np.tanh(((((((np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"] > -1,(( data["FLOORSMAX_AVG"])*(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])+(data["PREV_CHANNEL_TYPE_Channel_of_corporate_sales_MEAN"])))) , data["NEW_CREDIT_TO_ANNUITY_RATIO"])) -(data["NAME_INCOME_TYPE_State_servant"])))-(data["NAME_INCOME_TYPE_State_servant"])))-(data["WEEKDAY_APPR_PROCESS_START_SATURDAY"])))
v["i80"] = 0.019998*np.tanh(np.where(data["CC_CNT_DRAWINGS_ATM_CURRENT_MAX"]<0, np.minimum(((data["NEW_DOC_IND_KURT"])) ,(( np.where(data["CC_AMT_CREDIT_LIMIT_ACTUAL_MIN"]>0,(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])* 2.0),(( data["AMT_CREDIT"])+(data["NEW_DOC_IND_KURT"])))))) ,(( data["APPROVED_CNT_PAYMENT_SUM"])-(data["REFUSED_CNT_PAYMENT_SUM"]))))
v["i81"] = 0.049499*np.tanh(((((data["FLAG_WORK_PHONE"])+(np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(((((np.maximum(((data["APPROVED_CNT_PAYMENT_SUM"])) ,(( data["ORGANIZATION_TYPE_Construction"])))) +(np.maximum(((np.maximum(((data["NAME_HOUSING_TYPE_Rented_apartment"])) ,(( data["NEW_SCORES_STD"]))))),(( data["CC_CNT_DRAWINGS_CURRENT_MAX"])))))/2.0)))))))* 2.0))
v["i82"] = 0.047499*np.tanh(((((( data["INSTAL_DBD_MAX"])+(( -1.0*(( np.maximum(((data["OCCUPATION_TYPE_Accountants"])) ,(( data["ORGANIZATION_TYPE_School"])))))))) /2.0)) -(((((((( data["EXT_SOURCE_2"])+(data["NEW_PHONE_TO_EMPLOY_RATIO"])))+(data["APPROVED_HOUR_APPR_PROCESS_START_MAX"])) /2.0)) +(data["NEW_LIVE_IND_SUM"])))))
v["i83"] = 0.048937*np.tanh(((np.where(data["BURO_STATUS_1_MEAN_MEAN"] > -1, data["AMT_ANNUITY"], np.where(data["AMT_ANNUITY"]<0, data["DAYS_ID_PUBLISH"], data["PREV_CODE_REJECT_REASON_LIMIT_MEAN"])))-(((((data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])-(data["OCCUPATION_TYPE_Drivers"])))-(data["POS_SK_DPD_MEAN"])))))
v["i84"] = 0.047002*np.tanh(np.where(data["BURO_CREDIT_ACTIVE_Active_MEAN"] > -1, np.minimum(((data["NEW_DOC_IND_KURT"])) ,(( data["REGION_POPULATION_RELATIVE"]))), np.where(data["CLOSED_DAYS_CREDIT_MAX"]>0, data["CC_CNT_DRAWINGS_CURRENT_MEAN"], np.where(data["ACTIVE_DAYS_CREDIT_ENDDATE_MEAN"]>0, data["BURO_CREDIT_ACTIVE_Active_MEAN"],(( data["EXT_SOURCE_3"])-(data["BURO_CREDIT_ACTIVE_Active_MEAN"]))))))
v["i85"] = 0.049968*np.tanh(np.where(((( data["INSTAL_DPD_MEAN"])>(data["NAME_EDUCATION_TYPE_Lower_secondary"])) *1.) >0, data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"], np.where(data["NEW_PHONE_TO_BIRTH_RATIO"] > -1, data["REFUSED_AMT_CREDIT_MAX"],(( data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"])*(data["REFUSED_AMT_CREDIT_MAX"])))))
v["i86"] = 0.048000*np.tanh(np.where(((( data["PREV_NAME_CONTRACT_STATUS_Canceled_MEAN"])>(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])/ 2.0)))*1.) >0, np.where(data["PREV_NAME_CONTRACT_STATUS_Canceled_MEAN"]>0, data["PREV_NAME_YIELD_GROUP_high_MEAN"], data["NEW_CREDIT_TO_ANNUITY_RATIO"]),(((( data["REFUSED_AMT_CREDIT_MAX"])*(data["WEEKDAY_APPR_PROCESS_START_SUNDAY"])))-(data["NEW_CREDIT_TO_ANNUITY_RATIO"]))))
v["i87"] = 0.047521*np.tanh(((data["NAME_FAMILY_STATUS_Married"])*(np.where(((( data["NAME_FAMILY_STATUS_Married"])<(np.minimum(((data["PREV_AMT_GOODS_PRICE_MEAN"])) ,(((-1.0*(( data["DAYS_BIRTH"])))))))) *1.) >0, data["REFUSED_AMT_CREDIT_MIN"],(((( data["DAYS_BIRTH"])* 2.0)) * 2.0)))))
v["i88"] = 0.035104*np.tanh(((data["INSTAL_AMT_PAYMENT_MIN"])-(( -1.0*(((( np.where(data["BURO_CREDIT_TYPE_Consumer_credit_MEAN"]<0, data["PREV_NAME_PORTFOLIO_XNA_MEAN"],(((( data["CLOSED_AMT_CREDIT_SUM_DEBT_SUM"])+(data["PREV_CHANNEL_TYPE_Contact_center_MEAN"])))+(data["NAME_HOUSING_TYPE_Rented_apartment"])))) -(data["BURO_AMT_CREDIT_SUM_MEAN"]))))))))
v["i89"] = 0.049998*np.tanh(np.where(((data["NEW_DOC_IND_KURT"])-(data["DAYS_BIRTH"])) > -1, np.where(data["NEW_INC_PER_CHLD"]<0, data["CLOSED_DAYS_CREDIT_MIN"],(((( data["CLOSED_MONTHS_BALANCE_MIN_MIN"])*(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"])))* 2.0)) ,(-1.0*(( data["DAYS_BIRTH"])))))
v["i90"] = 0.049998*np.tanh(np.where(data["CLOSED_AMT_CREDIT_SUM_SUM"]>0,(( data["ACTIVE_AMT_CREDIT_SUM_SUM"])+(np.where(data["ACTIVE_AMT_CREDIT_SUM_SUM"]<0, data["NEW_RATIO_PREV_APP_CREDIT_PERC_MEAN"], data["REGION_RATING_CLIENT_W_CITY"]))),(((((((( data["APPROVED_CNT_PAYMENT_SUM"])-(data["POS_COUNT"])))* 2.0)) * 2.0)) * 2.0)))
v["i91"] = 0.049698*np.tanh(np.where(data["APPROVED_AMT_GOODS_PRICE_MEAN"]>0, data["CC_AMT_BALANCE_MIN"], np.where(data["POS_MONTHS_BALANCE_MEAN"]>0,(((((( data["INSTAL_DPD_MEAN"])* 2.0)) * 2.0)) * 2.0),(( data["BURO_STATUS_X_MEAN_MEAN"])-(((data["NEW_CAR_TO_BIRTH_RATIO"])* 2.0))))))
v["i92"] = 0.049598*np.tanh(np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_MEAN"]<0,(((((data["EXT_SOURCE_3"])>(-3.0)) *1.))-(((( data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])>(data["NEW_EXT_SOURCES_MEAN"])) *1.))) , np.where(data["EXT_SOURCE_3"] > -1, data["EXT_SOURCE_3"], data["PREV_NAME_SELLER_INDUSTRY_XNA_MEAN"])))
v["i93"] = 0.049600*np.tanh(((((((np.minimum(((data["PREV_NAME_GOODS_CATEGORY_Consumer_Electronics_MEAN"])) ,(( data["NAME_EDUCATION_TYPE_Lower_secondary"])))) +(((( np.minimum(((data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__middle_MEAN"])) ,(( data["DAYS_ID_PUBLISH"])))) >(data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"])) *1.))))+(np.maximum(((data["NEW_ANNUITY_TO_INCOME_RATIO"])) ,(( data["POS_NAME_CONTRACT_STATUS_Returned_to_the_store_MEAN"])))))) * 2.0))
v["i94"] = 0.049793*np.tanh(((np.where(( -1.0*(( data["EXT_SOURCE_2"])))> -1, np.maximum(((data["CC_AMT_BALANCE_MIN"])) ,(((( np.minimum(((data["NAME_CONTRACT_TYPE_Cash_loans"])) ,(( data["OBS_30_CNT_SOCIAL_CIRCLE"])))) *(data["PREV_AMT_GOODS_PRICE_MEAN"]))))),(((data["NEW_RATIO_PREV_HOUR_APPR_PROCESS_START_MIN"])+(data["REGION_RATING_CLIENT_W_CITY"])) /2.0)))* 2.0))
v["i95"] = 0.047992*np.tanh(np.where(data["EXT_SOURCE_3"] > -1, np.where(data["CODE_GENDER"] > -1, data["PREV_PRODUCT_COMBINATION_POS_household_without_interest_MEAN"],(-1.0*(( data["CLOSED_DAYS_CREDIT_MAX"])))) , np.maximum(((data["CC_AMT_RECIVABLE_MEAN"])) ,(( np.maximum(((data["REFUSED_CNT_PAYMENT_SUM"])) ,(( np.maximum(((data["REFUSED_DAYS_DECISION_MEAN"])) ,(( data["ACTIVE_DAYS_CREDIT_MEAN"])))))))))))
v["i96"] = 0.048061*np.tanh(np.where(data["PREV_NAME_GOODS_CATEGORY_Photo___Cinema_Equipment_MEAN"]>0, data["REFUSED_AMT_DOWN_PAYMENT_MIN"], np.where(data["REFUSED_AMT_DOWN_PAYMENT_MIN"] > -1, data["PREV_AMT_GOODS_PRICE_MAX"], np.where(data["ACTIVE_MONTHS_BALANCE_SIZE_MEAN"]>0, data["REFUSED_AMT_DOWN_PAYMENT_MIN"],(((data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])>(((data["PREV_NAME_GOODS_CATEGORY_Photo___Cinema_Equipment_MEAN"])*(data["BURO_DAYS_CREDIT_MAX"])))) *1.)))))
v["i97"] = 0.022584*np.tanh(((np.where(np.where(data["BURO_AMT_CREDIT_SUM_DEBT_SUM"] > -1, data["CODE_GENDER"], data["BURO_AMT_CREDIT_SUM_MEAN"])> -1, data["BURO_AMT_CREDIT_SUM_DEBT_SUM"],(( data["EXT_SOURCE_2"])*(np.where(data["BURO_AMT_CREDIT_SUM_DEBT_SUM"] > -1, data["BURO_MONTHS_BALANCE_SIZE_SUM"], data["BURO_AMT_CREDIT_SUM_MEAN"])))))-(data["BURO_AMT_CREDIT_SUM_MEAN"])))
v["i98"] = 0.046920*np.tanh(( -1.0*(( np.where(data["DAYS_BIRTH"]>0, np.where(data["DAYS_EMPLOYED"]>0, np.maximum(((data["BURO_DAYS_CREDIT_MIN"])) ,(( data["CC_AMT_CREDIT_LIMIT_ACTUAL_MAX"]))), np.where(data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"] > -1, data["CC_AMT_PAYMENT_TOTAL_CURRENT_MAX"],(-1.0*(( data["PREV_NAME_PRODUCT_TYPE_x_sell_MEAN"]))))), data["CODE_GENDER"])))))
v["i99"] = 0.049880*np.tanh(((((((((( data["ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN"])>(np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_MEAN"])) ,(( data["DEF_30_CNT_SOCIAL_CIRCLE"])))))*1.))+(np.tanh(( np.tanh(( np.where(data["ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN"] > -1, data["NEW_RATIO_BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"], data["POS_SK_DPD_DEF_MAX"])))))))) * 2.0)) * 2.0))
v["i100"] = 0.049848*np.tanh(np.where(np.where(( -1.0*(( data["APPROVED_DAYS_DECISION_MAX"])))> -1, data["NEW_EXT_SOURCES_MEAN"], data["BURO_AMT_CREDIT_SUM_MEAN"])>0,(-1.0*(( data["EXT_SOURCE_1"]))),(( data["BURO_CREDIT_TYPE_Another_type_of_loan_MEAN"])*(np.where(data["NEW_RATIO_PREV_APP_CREDIT_PERC_MEAN"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_MAX"], data["APPROVED_DAYS_DECISION_MAX"])))))
v["i101"] = 0.047541*np.tanh(np.where(data["ACTIVE_MONTHS_BALANCE_SIZE_SUM"]>0, data["EXT_SOURCE_2"],(-1.0*(((( data["CC_CNT_DRAWINGS_POS_CURRENT_MIN"])*(( -1.0*(( np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"]>0, data["INSTAL_PAYMENT_DIFF_MAX"],(( data["DEF_60_CNT_SOCIAL_CIRCLE"])*(data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"])))))))))))))
v["i102"] = 0.047686*np.tanh(np.where(data["CC_AMT_RECIVABLE_VAR"] > -1,(((( data["CC_AMT_RECIVABLE_VAR"])+(data["CC_CNT_DRAWINGS_CURRENT_VAR"])))-(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"])) , np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]>0, data["CC_CNT_DRAWINGS_CURRENT_VAR"],(((data["NEW_SOURCES_PROD"])>(data["CC_CNT_DRAWINGS_CURRENT_VAR"])) *1.))))
v["i103"] = 0.046496*np.tanh(((data["POS_NAME_CONTRACT_STATUS_Completed_MEAN"])*(np.maximum(((((np.where(data["POS_NAME_CONTRACT_STATUS_Completed_MEAN"] > -1,(( data["APPROVED_AMT_APPLICATION_MIN"])* 2.0), data["INSTAL_COUNT"])) * 2.0))),(((((((((data["AMT_CREDIT"])+(data["WEEKDAY_APPR_PROCESS_START_SATURDAY"])) /2.0)) * 2.0)) * 2.0)))))))
v["i104"] = 0.049850*np.tanh(np.where(data["BURO_DAYS_CREDIT_MAX"] > -1, np.where(data["BURO_CREDIT_TYPE_Car_loan_MEAN"]<0, np.where(data["PREV_CHANNEL_TYPE_Contact_center_MEAN"]>0, data["PREV_CHANNEL_TYPE_Contact_center_MEAN"], data["INSTAL_AMT_PAYMENT_MIN"]), data["CC_AMT_CREDIT_LIMIT_ACTUAL_SUM"]), np.maximum(((data["WEEKDAY_APPR_PROCESS_START_WEDNESDAY"])) ,(((-1.0*(( data["PREV_NAME_SELLER_INDUSTRY_XNA_MEAN"]))))))))
v["i105"] = 0.049100*np.tanh(((np.where(data["BURO_STATUS_0_MEAN_MEAN"] > -1, data["PREV_CNT_PAYMENT_SUM"], np.maximum(((data["PREV_NAME_CONTRACT_TYPE_Revolving_loans_MEAN"])) ,(( data["BURO_CREDIT_TYPE_Microloan_MEAN"])))))-(np.maximum(((data["BURO_STATUS_0_MEAN_MEAN"])) ,(( np.maximum(((np.maximum(((data["NAME_FAMILY_STATUS_Married"])) ,(( data["EXT_SOURCE_1"]))))),(( data["EXT_SOURCE_1"])))))))))
v["i106"] = 0.049003*np.tanh(np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0, np.where(((( data["NEW_CREDIT_TO_ANNUITY_RATIO"])<(((data["PREV_CODE_REJECT_REASON_SCO_MEAN"])+(((data["PREV_NAME_CLIENT_TYPE_Refreshed_MEAN"])* 2.0)))))*1.) >0, data["BURO_STATUS_0_MEAN_MEAN"],(((data["PREV_CNT_PAYMENT_SUM"])>(data["NEW_CREDIT_TO_ANNUITY_RATIO"])) *1.)), data["NAME_EDUCATION_TYPE_Higher_education"]))
v["i107"] = 0.041200*np.tanh(((np.where(data["BURO_CREDIT_TYPE_Microloan_MEAN"] > -1, data["REGION_RATING_CLIENT_W_CITY"], data["AMT_ANNUITY"])) +(((data["PREV_CHANNEL_TYPE_Credit_and_cash_offices_MEAN"])*(np.where(data["PREV_NAME_PRODUCT_TYPE_XNA_MEAN"]<0, data["REGION_RATING_CLIENT_W_CITY"], np.where(data["REGION_RATING_CLIENT_W_CITY"]<0, data["PREV_WEEKDAY_APPR_PROCESS_START_SATURDAY_MEAN"], data["BURO_CREDIT_TYPE_Mortgage_MEAN"])))))))
v["i108"] = 0.048457*np.tanh(((((((((((((data["DAYS_BIRTH"])*(((data["PREV_NAME_PORTFOLIO_POS_MEAN"])* 2.0)))) -(data["ORGANIZATION_TYPE_Military"])))-(data["ORGANIZATION_TYPE_Industry__type_9"])))-(data["OCCUPATION_TYPE_High_skill_tech_staff"])))-(data["ORGANIZATION_TYPE_Bank"])))-(data["OCCUPATION_TYPE_Medicine_staff"])))
v["i109"] = 0.049920*np.tanh(np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]>0, data["NEW_RATIO_BURO_AMT_ANNUITY_MEAN"], np.where(data["CLOSED_AMT_CREDIT_MAX_OVERDUE_MEAN"]>0, data["BURO_DAYS_CREDIT_MEAN"], np.where(data["CLOSED_AMT_CREDIT_MAX_OVERDUE_MEAN"] > -1, data["ACTIVE_AMT_CREDIT_SUM_SUM"], np.where(data["BURO_DAYS_CREDIT_VAR"] > -1, data["ACTIVE_AMT_CREDIT_SUM_SUM"],(-1.0*(( data["INSTAL_DBD_SUM"]))))))))
v["i110"] = 0.048617*np.tanh(((data["ACTIVE_AMT_CREDIT_SUM_SUM"])*(((((( data["INSTAL_AMT_PAYMENT_MEAN"])+(data["INSTAL_AMT_INSTALMENT_SUM"])) /2.0)) -(np.where(data["REGION_RATING_CLIENT"] > -1, data["EXT_SOURCE_3"],(((((((data["APPROVED_CNT_PAYMENT_MEAN"])>(data["NEW_ANNUITY_TO_INCOME_RATIO"])) *1.))* 2.0)) * 2.0)))))))
v["i111"] = 0.031776*np.tanh(((((((( np.where(data["BURO_AMT_CREDIT_SUM_DEBT_MAX"]<0, np.where(data["INSTAL_PAYMENT_DIFF_MAX"]<0, data["POS_NAME_CONTRACT_STATUS_Signed_MEAN"], data["INSTAL_DBD_MAX"]),(-1.0*(( data["INSTAL_DAYS_ENTRY_PAYMENT_SUM"])))))+(((data["BURO_DAYS_CREDIT_ENDDATE_MAX"])*(data["OBS_60_CNT_SOCIAL_CIRCLE"])))) /2.0)) * 2.0)) * 2.0))
v["i112"] = 0.049079*np.tanh(np.where(data["NEW_CREDIT_TO_GOODS_RATIO"] > -1,(((((( np.minimum(((np.minimum(((( -1.0*(( data["REFUSED_AMT_ANNUITY_MIN"]))))),(( data["NEW_CREDIT_TO_GOODS_RATIO"]))))),(( data["AMT_ANNUITY"])))) * 2.0)) * 2.0)) * 2.0),(( data["INSTAL_DBD_MAX"])-(data["REFUSED_AMT_ANNUITY_MIN"]))))
v["i113"] = 0.048979*np.tanh(np.where(np.minimum(((data["ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN"])) ,(( data["NEW_EXT_SOURCES_MEAN"])))> -1, data["BURO_CREDIT_TYPE_Microloan_MEAN"], np.where(data["AMT_REQ_CREDIT_BUREAU_QRT"]<0, np.maximum(((data["NEW_CREDIT_TO_GOODS_RATIO"])) ,(( np.where(data["ORGANIZATION_TYPE_Business_Entity_Type_3"]<0, data["INSTAL_PAYMENT_DIFF_MEAN"], data["ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN"])))) , data["ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN"])))
v["i114"] = 0.047000*np.tanh(np.where(data["CC_AMT_RECEIVABLE_PRINCIPAL_MAX"] > -1, data["REGION_POPULATION_RELATIVE"],(( data["REFUSED_DAYS_DECISION_MEAN"])*(((((((data["APPROVED_AMT_CREDIT_MIN"])+(data["NEW_PHONE_TO_EMPLOY_RATIO"])) /2.0)) +(((( data["INSTAL_PAYMENT_DIFF_VAR"])<(((( data["APPROVED_AMT_CREDIT_MIN"])+(data["NEW_CREDIT_TO_INCOME_RATIO"])) /2.0)))*1.))) /2.0)))))
v["i115"] = 0.048568*np.tanh(((np.where(data["ACTIVE_AMT_CREDIT_SUM_SUM"]<0,(( np.where(data["CLOSED_AMT_CREDIT_SUM_SUM"]>0, data["PREV_PRODUCT_COMBINATION_POS_other_with_interest_MEAN"],(((-2.0)>(np.where(data["INSTAL_PAYMENT_DIFF_VAR"]>0, data["LIVINGAPARTMENTS_AVG"], data["EXT_SOURCE_2"])))*1.))) * 2.0), data["ACTIVE_DAYS_CREDIT_MEAN"])) * 2.0))
v["i116"] = 0.049750*np.tanh(np.where(data["NEW_ANNUITY_TO_INCOME_RATIO"]>0, data["INSTAL_PAYMENT_DIFF_SUM"], np.where(np.where(data["APPROVED_APP_CREDIT_PERC_MEAN"]<0, data["DAYS_BIRTH"], data["NAME_EDUCATION_TYPE_Higher_education"])<0, data["APPROVED_AMT_APPLICATION_MAX"], np.where(data["DAYS_BIRTH"]<0, data["NAME_EDUCATION_TYPE_Higher_education"],(-1.0*(( data["NAME_EDUCATION_TYPE_Higher_education"])))))))
v["i117"] = 0.046000*np.tanh(((np.where(data["BASEMENTAREA_MEDI"]>0, data["NEW_CREDIT_TO_ANNUITY_RATIO"],(-1.0*(( np.where(data["CC_AMT_DRAWINGS_ATM_CURRENT_MAX"]>0, data["NEW_CREDIT_TO_ANNUITY_RATIO"], np.where(data["AMT_REQ_CREDIT_BUREAU_QRT"]>0, data["NEW_CREDIT_TO_ANNUITY_RATIO"],(( data["DEF_60_CNT_SOCIAL_CIRCLE"])*(data["NEW_ANNUITY_TO_INCOME_RATIO"])))))))))* 2.0))
v["i118"] = 0.049042*np.tanh(np.where(data["BURO_AMT_CREDIT_SUM_MEAN"]>0, data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"],(( np.where(data["PREV_CHANNEL_TYPE_Channel_of_corporate_sales_MEAN"]>0, data["CC_AMT_PAYMENT_CURRENT_MIN"],(( data["PREV_WEEKDAY_APPR_PROCESS_START_SUNDAY_MEAN"])*(np.minimum(((data["INSTAL_DBD_MEAN"])) ,(((( data["NEW_ANNUITY_TO_INCOME_RATIO"])*(data["APPROVED_DAYS_DECISION_MAX"])))))))))* 2.0)))
v["i119"] = 0.046682*np.tanh(np.where(data["CC_AMT_PAYMENT_CURRENT_MEAN"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"], np.where(data["PREV_NAME_YIELD_GROUP_middle_MEAN"]>0, data["PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN"], np.maximum(((data["CC_CNT_DRAWINGS_CURRENT_MAX"])) ,(( np.maximum(((np.where(data["PREV_DAYS_DECISION_MIN"] > -1, data["ORGANIZATION_TYPE_Construction"], data["BURO_AMT_CREDIT_SUM_DEBT_SUM"]))),(( data["PREV_PRODUCT_COMBINATION_Cash_Street__middle_MEAN"])))))))))
v["i120"] = 0.037398*np.tanh(((((np.where(data["CC_AMT_CREDIT_LIMIT_ACTUAL_MEAN"]>0, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"], np.maximum(((np.maximum(((((( data["NAME_EDUCATION_TYPE_Lower_secondary"])+(((( data["NEW_RATIO_PREV_DAYS_DECISION_MAX"])>(data["NAME_EDUCATION_TYPE_Lower_secondary"])) *1.))) /2.0))),(( data["BURO_CREDIT_TYPE_Microloan_MEAN"]))))),(( data["CLOSED_AMT_CREDIT_SUM_DEBT_MAX"])))))* 2.0)) * 2.0))
v["i121"] = 0.044000*np.tanh(np.where(data["INSTAL_DPD_MEAN"]<0,(( np.maximum(((((( data["INSTAL_DPD_MEAN"])>(((((data["POS_MONTHS_BALANCE_MEAN"])/ 2.0)) / 2.0)))*1.))) ,(((((data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])>(data["ORGANIZATION_TYPE_Construction"])) *1.))))) * 2.0), data["POS_MONTHS_BALANCE_MEAN"]))
v["i122"] = 0.005400*np.tanh(np.where(data["DAYS_ID_PUBLISH"] > -1, np.where(data["POS_MONTHS_BALANCE_MEAN"]<0, np.where(data["DAYS_ID_PUBLISH"]<0, data["PREV_NAME_TYPE_SUITE_Family_MEAN"], -3.0),(((( np.maximum(((data["CC_AMT_RECIVABLE_VAR"])) ,(( data["INSTAL_DPD_MEAN"])))) * 2.0)) * 2.0)) , data["FLOORSMIN_MEDI"]))
v["i123"] = 0.040397*np.tanh(np.where(data["EXT_SOURCE_2"]>0, data["NEW_ANNUITY_TO_INCOME_RATIO"], np.where(data["ACTIVE_MONTHS_BALANCE_MAX_MAX"]<0,(( np.where(data["DAYS_ID_PUBLISH"] > -1,(( data["NEW_DOC_IND_KURT"])+(data["APPROVED_APP_CREDIT_PERC_MEAN"])) , data["REFUSED_AMT_DOWN_PAYMENT_MIN"])) -(data["NEW_ANNUITY_TO_INCOME_RATIO"])) , data["EXT_SOURCE_1"])))
v["i124"] = 0.049016*np.tanh(((np.where(data["NEW_EXT_SOURCES_MEAN"] > -1, data["NAME_HOUSING_TYPE_Municipal_apartment"], data["NEW_CREDIT_TO_GOODS_RATIO"])) +(((np.maximum(((data["BURO_CREDIT_ACTIVE_Sold_MEAN"])) ,(( np.maximum(((np.where(data["NEW_SCORES_STD"] > -1, data["BURO_CREDIT_TYPE_Microloan_MEAN"], data["NEW_CREDIT_TO_GOODS_RATIO"]))),(( data["ORGANIZATION_TYPE_Construction"])))))))* 2.0))))
v["i125"] = 0.023880*np.tanh(np.maximum(((data["ORGANIZATION_TYPE_Transport__type_3"])) ,(((( data["NEW_RATIO_BURO_DAYS_CREDIT_UPDATE_MEAN"])+(((data["ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN"])*(((data["NEW_ANNUITY_TO_INCOME_RATIO"])*(((( data["ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN"])<(np.tanh(( data["NEW_ANNUITY_TO_INCOME_RATIO"])))) *1.)))))))))))
v["i126"] = 0.031000*np.tanh(np.where(data["PREV_CHANNEL_TYPE_Channel_of_corporate_sales_MEAN"] > -1, np.where(data["PREV_CHANNEL_TYPE_Channel_of_corporate_sales_MEAN"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"],(((np.where(data["NEW_DOC_IND_STD"] > -1, data["CLOSED_AMT_CREDIT_SUM_DEBT_SUM"], data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"])) >(data["BURO_DAYS_CREDIT_MAX"])) *1.)), data["NEW_DOC_IND_STD"]))
v["i127"] = 0.015002*np.tanh(((( -1.0*(((( data["LIVE_CITY_NOT_WORK_CITY"])*(np.where(data["BURO_DAYS_CREDIT_MIN"]>0, data["PREV_PRODUCT_COMBINATION_POS_other_with_interest_MEAN"],(-1.0*(((((( data["PREV_PRODUCT_COMBINATION_POS_other_with_interest_MEAN"])* 2.0)) * 2.0)))))))))))-(np.maximum(((data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])) ,(( data["ORGANIZATION_TYPE_Military"]))))))
v["i128"] = 0.048040*np.tanh(((((np.where(data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"]>0, data["ORGANIZATION_TYPE_Business_Entity_Type_3"], np.maximum(((np.maximum(((np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_MAX"])) ,(( data["BURO_STATUS_1_MEAN_MEAN"]))))),(( data["PREV_PRODUCT_COMBINATION_POS_other_with_interest_MEAN"]))))),(( np.maximum(((data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])) ,(( data["ACTIVE_AMT_CREDIT_SUM_SUM"])))))))) * 2.0)) * 2.0))
v["i129"] = 0.031766*np.tanh(np.where(data["CC_AMT_CREDIT_LIMIT_ACTUAL_MAX"] > -1, data["PREV_CNT_PAYMENT_MEAN"], np.maximum(((((((data["NEW_SOURCES_PROD"])-(data["BURO_STATUS_X_MEAN_MEAN"])))* 2.0))),(((((data["BURO_AMT_CREDIT_SUM_SUM"])<(np.where(data["NAME_INCOME_TYPE_Commercial_associate"]>0, data["BURO_AMT_CREDIT_SUM_SUM"], data["PREV_PRODUCT_COMBINATION_Card_Street_MEAN"])))*1.))))))
v["i130"] = 0.042002*np.tanh(((data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"])*(np.where(data["PREV_CHANNEL_TYPE_Regional___Local_MEAN"]<0, np.where(data["FLOORSMIN_MODE"]<0, data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"],(( data["PREV_CHANNEL_TYPE_Regional___Local_MEAN"])+(data["NEW_RATIO_BURO_DAYS_CREDIT_MEAN"]))),(( data["POS_MONTHS_BALANCE_MEAN"])-(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"]))))))
v["i131"] = 0.010397*np.tanh(np.where(data["NEW_RATIO_PREV_AMT_ANNUITY_MAX"]<0, np.where(data["EXT_SOURCE_3"] > -1,(( np.where(data["LANDAREA_MODE"]<0,(-1.0*(((( data["CLOSED_DAYS_CREDIT_ENDDATE_MIN"])*(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_SUM"]))))), data["PREV_NAME_CONTRACT_TYPE_Revolving_loans_MEAN"])) * 2.0), data["FLAG_WORK_PHONE"]), data["NEW_RATIO_BURO_DAYS_CREDIT_VAR"]))
v["i132"] = 0.049340*np.tanh(np.where(data["BURO_DAYS_CREDIT_MAX"]<0, data["INSTAL_PAYMENT_DIFF_MEAN"],(-1.0*(((((data["BURO_DAYS_CREDIT_MAX"])<(np.tanh(((((((( data["BURO_DAYS_CREDIT_MAX"])>(((( data["BURO_DAYS_CREDIT_MAX"])>(data["BURO_AMT_CREDIT_SUM_MEAN"])) *1.))) *1.))>(data["BURO_AMT_CREDIT_SUM_MAX"])) *1.))))) *1.))))))
v["i133"] = 0.049700*np.tanh(np.where(data["AMT_INCOME_TOTAL"]>0, np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_ENDDATE_MIN"]>0, data["NAME_CONTRACT_TYPE_Cash_loans"], data["PREV_NAME_CLIENT_TYPE_Repeater_MEAN"]),(((( data["BURO_AMT_CREDIT_SUM_DEBT_MEAN"])-(np.where(data["NEW_RATIO_PREV_AMT_CREDIT_MIN"]<0, data["PREV_NAME_TYPE_SUITE_Children_MEAN"], 2.0)))) -(data["INSTAL_AMT_INSTALMENT_SUM"]))))
v["i134"] = 0.049820*np.tanh(np.where(data["FLAG_DOCUMENT_8"]<0,(((np.where(((data["NAME_INCOME_TYPE_State_servant"])+(data["PREV_NAME_YIELD_GROUP_low_normal_MEAN"])) <0, data["PREV_NAME_TYPE_SUITE_Spouse__partner_MEAN"],(( data["INSTAL_DPD_MAX"])* 2.0)))<(((data["INSTAL_DPD_MAX"])* 2.0)))*1.) ,(( data["PREV_NAME_YIELD_GROUP_low_normal_MEAN"])* 2.0)))
v["i135"] = 0.034598*np.tanh(((data["PREV_AMT_DOWN_PAYMENT_MEAN"])*(np.tanh(((( data["EXT_SOURCE_3"])-(((((data["NAME_FAMILY_STATUS_Separated"])-(((((((((-1.0*(( data["NAME_CONTRACT_TYPE_Cash_loans"])))) <(data["ACTIVE_DAYS_CREDIT_MIN"])) *1.))* 2.0)) * 2.0)))) / 2.0))))))))
v["i136"] = 0.049281*np.tanh(np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"] > -1, np.where(data["BURO_STATUS_0_MEAN_MEAN"] > -1, data["CLOSED_AMT_ANNUITY_MEAN"], np.where(data["NEW_INC_BY_ORG"] > -1, data["INSTAL_AMT_PAYMENT_MIN"], data["NEW_CREDIT_TO_ANNUITY_RATIO"])) ,(( data["NEW_CREDIT_TO_ANNUITY_RATIO"])-(((data["REFUSED_AMT_APPLICATION_MIN"])+(data["BURO_STATUS_0_MEAN_MEAN"]))))))
v["i137"] = 0.018362*np.tanh(np.where(data["POS_MONTHS_BALANCE_SIZE"]>0, data["NEW_CREDIT_TO_ANNUITY_RATIO"],(-1.0*(( np.maximum(((data["NAME_CONTRACT_TYPE_Cash_loans"])) ,(( np.where(np.where(data["APPROVED_AMT_APPLICATION_MIN"]<0, data["NEW_DOC_IND_AVG"], data["COMMONAREA_MEDI"])<0,(( data["FLOORSMAX_AVG"])* 2.0), data["PREV_NAME_YIELD_GROUP_high_MEAN"])))))))))
v["i138"] = 0.000340*np.tanh(np.where(data["YEARS_BUILD_AVG"] > -1, data["NAME_HOUSING_TYPE_Municipal_apartment"], np.where(data["ORGANIZATION_TYPE_Police"]<0,(( data["WEEKDAY_APPR_PROCESS_START_SATURDAY"])*(np.where(data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"]>0,(((data["REFUSED_APP_CREDIT_PERC_MAX"])+(data["PREV_NAME_CONTRACT_STATUS_Canceled_MEAN"])) /2.0), data["NAME_HOUSING_TYPE_Municipal_apartment"]))), data["YEARS_BUILD_AVG"])))
v["i139"] = 0.047401*np.tanh(( -1.0*(((((((data["ORGANIZATION_TYPE_Industry__type_9"])+(((data["PREV_CHANNEL_TYPE_Channel_of_corporate_sales_MEAN"])+(data["ORGANIZATION_TYPE_Military"])))))+(np.maximum(((data["NEW_DOC_IND_AVG"])) ,(((( data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])+(((data["PREV_NAME_GOODS_CATEGORY_Furniture_MEAN"])+(data["AMT_REQ_CREDIT_BUREAU_YEAR"])))))))))/2.0)))))
v["i140"] = 0.045253*np.tanh(((np.maximum(((np.where(data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"]>0, data["POS_COUNT"],(( data["APPROVED_AMT_CREDIT_MAX"])-(data["INSTAL_AMT_INSTALMENT_SUM"]))))),(( np.where(data["PREV_NAME_PRODUCT_TYPE_x_sell_MEAN"]<0, data["PREV_PRODUCT_COMBINATION_Cash_Street__middle_MEAN"],(( data["OCCUPATION_TYPE_Drivers"])-(data["INSTAL_AMT_INSTALMENT_SUM"])))))))* 2.0))
v["i141"] = 0.047007*np.tanh(((np.where(data["NEW_EXT_SOURCES_MEAN"]<0,(( np.where(data["NEW_EXT_SOURCES_MEAN"] > -1, data["NEW_EXT_SOURCES_MEAN"], data["CC_AMT_BALANCE_MAX"])) -(((data["NEW_EXT_SOURCES_MEAN"])+(((( data["WEEKDAY_APPR_PROCESS_START_MONDAY"])>(data["NEW_EXT_SOURCES_MEAN"])) *1.))))) , data["REGION_RATING_CLIENT_W_CITY"])) * 2.0))
v["i142"] = 0.048014*np.tanh(np.where(data["CC_AMT_PAYMENT_CURRENT_MEAN"]>0,(( data["ORGANIZATION_TYPE_Transport__type_3"])+(data["NEW_RATIO_BURO_AMT_CREDIT_SUM_LIMIT_MEAN"])) ,(((data["ORGANIZATION_TYPE_Transport__type_3"])<(((((( data["ACTIVE_DAYS_CREDIT_MAX"])+(np.tanh(((( data["NAME_HOUSING_TYPE_Rented_apartment"])+(-3.0)))))) /2.0)) / 2.0)))*1.)))
v["i143"] = 0.010000*np.tanh(((((( data["INSTAL_AMT_PAYMENT_MIN"])>(np.where(data["INSTAL_DBD_SUM"]<0,(((data["NEW_RATIO_BURO_DAYS_CREDIT_ENDDATE_MAX"])<(np.where(data["CC_AMT_PAYMENT_TOTAL_CURRENT_SUM"] > -1, data["PREV_APP_CREDIT_PERC_MIN"], data["BURO_CREDIT_TYPE_Car_loan_MEAN"])))*1.) ,(( data["APPROVED_AMT_ANNUITY_MIN"])-(data["REG_CITY_NOT_LIVE_CITY"])))))*1.))* 2.0))
v["i144"] = 0.048499*np.tanh(((((((data["CODE_GENDER"])*(((data["DAYS_BIRTH"])+(np.where(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"] > -1, np.where(data["CODE_GENDER"] > -1, data["DAYS_BIRTH"], data["EXT_SOURCE_2"]), data["CODE_GENDER"])))))) * 2.0)) * 2.0))
v["i145"] = 0.020720*np.tanh(np.where(data["PREV_NAME_GOODS_CATEGORY_Sport_and_Leisure_MEAN"]<0, np.where(data["PREV_NAME_PORTFOLIO_Cards_MEAN"]<0,(-1.0*(((( data["NEW_PHONE_TO_EMPLOY_RATIO"])+(((( data["PREV_CODE_REJECT_REASON_LIMIT_MEAN"])<(data["APPROVED_CNT_PAYMENT_MEAN"])) *1.)))))),(((data["PREV_NAME_TYPE_SUITE_Other_B_MEAN"])<(data["PREV_PRODUCT_COMBINATION_Cash_MEAN"])) *1.)), data["NEW_PHONE_TO_EMPLOY_RATIO"]))
v["i146"] = 0.049683*np.tanh(((( np.where(data["CC_AMT_DRAWINGS_POS_CURRENT_MEAN"] > -1,(( data["WEEKDAY_APPR_PROCESS_START_WEDNESDAY"])* 2.0), np.where(data["WEEKDAY_APPR_PROCESS_START_WEDNESDAY"]<0, data["ORGANIZATION_TYPE_Transport__type_3"],(( data["REFUSED_AMT_CREDIT_MAX"])*(data["NEW_INC_BY_ORG"])))))+(((data["CC_AMT_TOTAL_RECEIVABLE_MEAN"])-(data["CC_AMT_PAYMENT_TOTAL_CURRENT_SUM"])))) /2.0))
v["i147"] = 0.048699*np.tanh(((data["PREV_WEEKDAY_APPR_PROCESS_START_THURSDAY_MEAN"])*(np.where(((( data["PREV_WEEKDAY_APPR_PROCESS_START_THURSDAY_MEAN"])+(data["BURO_CREDIT_TYPE_Credit_card_MEAN"])) /2.0)> -1, data["PREV_NAME_SELLER_INDUSTRY_XNA_MEAN"],(( data["PREV_AMT_APPLICATION_MAX"])*(np.where(data["PREV_WEEKDAY_APPR_PROCESS_START_THURSDAY_MEAN"]>0, data["PREV_AMT_APPLICATION_MAX"], data["REGION_POPULATION_RELATIVE"])))))))
v["i148"] = 0.041840*np.tanh(np.where(data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"]>0,(((((( data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"])-(data["ACTIVE_AMT_CREDIT_SUM_MAX"])))-(data["ACTIVE_AMT_CREDIT_SUM_MAX"])))* 2.0), np.maximum(((np.maximum(((data["ORGANIZATION_TYPE_Transport__type_3"])) ,(( data["ACTIVE_AMT_CREDIT_SUM_MEAN"]))))),(( data["CC_AMT_RECEIVABLE_PRINCIPAL_VAR"])))))
v["i149"] = 0.049911*np.tanh(np.minimum(((((((( data["NEW_EXT_SOURCES_MEAN"])>(data["PREV_CODE_REJECT_REASON_HC_MEAN"])) *1.))-(data["YEARS_BUILD_AVG"])))) ,(((((((-1.0*(((((data["NEW_EXT_SOURCES_MEAN"])>(((1.0)-(data["ORGANIZATION_TYPE_Industry__type_9"])))) *1.))))) * 2.0)) * 2.0)))))
return Output(v.sum(axis=1))
def GP2(data):
v = pd.DataFrame()
v["i0"] = 0.010000*np.tanh(((((((((np.tanh(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])))-(((np.where(((( data["NEW_EXT_SOURCES_MEAN"])<(data["NEW_DOC_IND_AVG"])) *1.) >0,(( data["NEW_EXT_SOURCES_MEAN"])* 2.0), data["NEW_EXT_SOURCES_MEAN"])) * 2.0)))) * 2.0)) * 2.0)) * 2.0))
v["i1"] = 0.034000*np.tanh(( -1.0*(((((((((((((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)) -(np.where(np.maximum(((data["CC_CNT_DRAWINGS_ATM_CURRENT_VAR"])) ,(( data["NEW_EXT_SOURCES_MEAN"])))>0, data["CC_CNT_DRAWINGS_ATM_CURRENT_VAR"], data["NAME_INCOME_TYPE_Working"])))) * 2.0)) * 2.0)) * 2.0)) * 2.0)))))
v["i2"] = 0.048500*np.tanh(((data["OCCUPATION_TYPE_Drivers"])+(((data["FLAG_DOCUMENT_3"])+(((((data["NEW_EXT_SOURCES_MEAN"])+(((np.tanh(( data["CC_AMT_TOTAL_RECEIVABLE_MEAN"])))+(((((( -1.0*(( data["NEW_EXT_SOURCES_MEAN"])))) * 2.0)) * 2.0)))))) * 2.0))))))
v["i3"] = 0.030000*np.tanh(((((((((((data["NEW_CREDIT_TO_GOODS_RATIO"])-(( -1.0*(( np.tanh(( data["CC_AMT_DRAWINGS_ATM_CURRENT_VAR"])))))))) -(((((data["NEW_EXT_SOURCES_MEAN"])* 2.0)) * 2.0)))) -(data["CODE_GENDER"])))* 2.0)) * 2.0))
v["i4"] = 0.049000*np.tanh(((((((((((np.tanh(((( np.tanh(( data["DAYS_EMPLOYED"])))-(data["EXT_SOURCE_3"])))))-(((( data["EXT_SOURCE_2"])<(data["NAME_EDUCATION_TYPE_Higher_education"])) *1.))))-(data["EXT_SOURCE_2"])))* 2.0)) * 2.0)) * 2.0))
v["i5"] = 0.017800*np.tanh(((((((( -1.0*(((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)))))+(((data["NEW_CREDIT_TO_GOODS_RATIO"])+(((( -1.0*(( np.maximum(((data["CODE_GENDER"])) ,(( data["APPROVED_APP_CREDIT_PERC_MAX"])))))))-(data["NEW_EXT_SOURCES_MEAN"])))))))* 2.0)) * 2.0))
v["i6"] = 0.029394*np.tanh(((((((((((np.tanh(( data["NEW_CREDIT_TO_GOODS_RATIO"])))-(data["NEW_EXT_SOURCES_MEAN"])))-(((((((data["NEW_EXT_SOURCES_MEAN"])>(data["DAYS_EMPLOYED"])) *1.))>(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])) *1.))))-(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) * 2.0))
v["i7"] = 0.035000*np.tanh(((((((((np.tanh(((((data["DAYS_EMPLOYED"])+(((np.tanh(((( data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])+(data["PREV_NAME_PRODUCT_TYPE_walk_in_MEAN"])))))+(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])))) /2.0)))) -(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) * 2.0)) * 2.0))
v["i8"] = 0.034200*np.tanh(((((np.minimum(((((((((( -1.0*(( data["NEW_EXT_SOURCES_MEAN"])))) * 2.0)) +(data["NEW_CREDIT_TO_GOODS_RATIO"])))* 2.0))),(((((((-1.0*(( data["NEW_EXT_SOURCES_MEAN"])))) -(data["APPROVED_AMT_DOWN_PAYMENT_MAX"])))* 2.0)))))* 2.0)) * 2.0))
v["i9"] = 0.030000*np.tanh(((((((np.maximum(((np.minimum(((data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])) ,(( np.tanh(( data["DAYS_EMPLOYED"]))))))),(((( np.tanh(( data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])))* 2.0)))))+(data["NEW_CREDIT_TO_GOODS_RATIO"])))-(((data["NEW_EXT_SOURCES_MEAN"])* 2.0)))) * 2.0))
v["i10"] = 0.041000*np.tanh(((((((((((np.tanh(((( data["NEW_CREDIT_TO_GOODS_RATIO"])+(data["DAYS_EMPLOYED"])))))-(data["EXT_SOURCE_2"])))-(np.tanh(( data["EXT_SOURCE_3"])))))* 2.0)) +(np.tanh(( data["REFUSED_DAYS_DECISION_MAX"])))))* 2.0))
v["i11"] = 0.049000*np.tanh(((((((((np.tanh(( data["CC_AMT_INST_MIN_REGULARITY_MEAN"])))-(((data["APPROVED_APP_CREDIT_PERC_MAX"])-(data["PREV_NAME_CLIENT_TYPE_New_MEAN"])))))-(((data["APPROVED_AMT_CREDIT_MIN"])-(data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))))-(data["NEW_EXT_SOURCES_MEAN"])))-(data["NEW_EXT_SOURCES_MEAN"])))
v["i12"] = 0.049901*np.tanh(((((((((np.maximum(((data["NEW_CREDIT_TO_GOODS_RATIO"])) ,(( data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))) +(data["DEF_30_CNT_SOCIAL_CIRCLE"])))+(np.tanh(( data["CC_CNT_DRAWINGS_CURRENT_MAX"])))))-(((data["CODE_GENDER"])+(((data["NEW_EXT_SOURCES_MEAN"])* 2.0)))))) * 2.0))
v["i13"] = 0.049040*np.tanh(((((((np.maximum(((np.tanh(( data["PREV_CNT_PAYMENT_MEAN"])))) ,(( data["CC_CNT_DRAWINGS_CURRENT_MAX"])))) +(((np.minimum(((data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])) ,(( np.tanh(( data["DAYS_EMPLOYED"])))))) -(((data["NEW_EXT_SOURCES_MEAN"])* 2.0)))))) * 2.0)) * 2.0))
v["i14"] = 0.044680*np.tanh(((((data["NEW_DOC_IND_KURT"])+(data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))+(((((((((((data["DAYS_EMPLOYED"])+(data["INSTAL_PAYMENT_DIFF_MAX"])))-(data["EXT_SOURCE_2"])))* 2.0)) -(data["PREV_RATE_DOWN_PAYMENT_MAX"])))-(data["PREV_AMT_ANNUITY_MEAN"])))))
v["i15"] = 0.047043*np.tanh(((((((((np.tanh(( np.where(data["PREV_APP_CREDIT_PERC_MEAN"]>0, data["CC_CNT_DRAWINGS_ATM_CURRENT_VAR"],(( data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])+(( -1.0*(( data["EXT_SOURCE_3"])))))))))+(( -1.0*(( data["NEW_EXT_SOURCES_MEAN"])))))) * 2.0)) * 2.0)) * 2.0))
v["i16"] = 0.049100*np.tanh(((((((( -1.0*(((( data["NEW_EXT_SOURCES_MEAN"])+(np.maximum(((data["NEW_CAR_TO_BIRTH_RATIO"])) ,(((( data["CODE_GENDER"])-(np.maximum(((data["PREV_NAME_PRODUCT_TYPE_walk_in_MEAN"])) ,(( data["NEW_CREDIT_TO_GOODS_RATIO"])))))))))))))) * 2.0)) * 2.0)) * 2.0))
v["i17"] = 0.048520*np.tanh(((((( data["NEW_CREDIT_TO_GOODS_RATIO"])+(((((np.maximum(((data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])) ,(((((-1.0*(((((( 1.28758943080902100)) +(data["NEW_EXT_SOURCES_MEAN"])) /2.0)))))-(np.tanh(( data["EXT_SOURCE_3"])))))))) * 2.0)) * 2.0)))/2.0)) * 2.0))
v["i18"] = 0.049320*np.tanh(((data["DEF_60_CNT_SOCIAL_CIRCLE"])+(((data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])+(((((((((np.tanh(( data["NEW_ANNUITY_TO_INCOME_RATIO"])))-(data["NEW_EXT_SOURCES_MEAN"])))-(np.tanh(( np.tanh(( data["BURO_CREDIT_ACTIVE_Closed_MEAN"])))))))* 2.0)) * 2.0))))))
v["i19"] = 0.049995*np.tanh(((((((data["PREV_CNT_PAYMENT_MEAN"])-(((data["NEW_EXT_SOURCES_MEAN"])-(((data["NEW_CREDIT_TO_GOODS_RATIO"])+(((data["PREV_CNT_PAYMENT_SUM"])+(((((data["INSTAL_PAYMENT_DIFF_MAX"])-(data["POS_MONTHS_BALANCE_SIZE"])))* 2.0)))))))))) * 2.0)) * 2.0))
v["i20"] = 0.049870*np.tanh(((((((np.maximum(((data["DAYS_EMPLOYED"])) ,(( data["NEW_RATIO_PREV_HOUR_APPR_PROCESS_START_MIN"])))) -(data["NEW_EXT_SOURCES_MEAN"])))-(data["NAME_EDUCATION_TYPE_Higher_education"])))+(((((data["INSTAL_PAYMENT_DIFF_MAX"])+(((data["INSTAL_PAYMENT_DIFF_MAX"])-(data["POS_MONTHS_BALANCE_SIZE"])))))* 2.0))))
v["i21"] = 0.049513*np.tanh(((((((np.where(data["APPROVED_AMT_DOWN_PAYMENT_MAX"]>0, -2.0,(((-1.0*(( data["EXT_SOURCE_3"])))) -(np.where(data["CODE_GENDER"]>0, data["CODE_GENDER"], data["NEW_SOURCES_PROD"])))))-(data["NEW_EXT_SOURCES_MEAN"])))* 2.0)) * 2.0))
v["i22"] = 0.049435*np.tanh(((((((data["PREV_DAYS_DECISION_MIN"])+(data["PREV_CNT_PAYMENT_MEAN"])))+(((data["NEW_DOC_IND_KURT"])+(((((data["PREV_NAME_YIELD_GROUP_high_MEAN"])-(data["CODE_GENDER"])))+(data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))))))-(data["NEW_EXT_SOURCES_MEAN"])))
v["i23"] = 0.049304*np.tanh(((((np.where(data["NEW_SOURCES_PROD"] > -1, np.minimum(((data["REGION_RATING_CLIENT_W_CITY"])) ,(( data["CC_AMT_TOTAL_RECEIVABLE_MEAN"]))),(( data["DAYS_BIRTH"])+(np.maximum(((data["PREV_NAME_PRODUCT_TYPE_walk_in_MEAN"])) ,(( np.maximum(((data["CC_AMT_TOTAL_RECEIVABLE_MEAN"])) ,(( data["NEW_CREDIT_TO_GOODS_RATIO"])))))))))) * 2.0)) * 2.0))
v["i24"] = 0.049200*np.tanh(((((((data["NEW_DOC_IND_KURT"])+(((np.where(data["INSTAL_AMT_PAYMENT_MIN"]>0, data["REFUSED_CNT_PAYMENT_SUM"], np.maximum(((data["REFUSED_CNT_PAYMENT_SUM"])) ,(( data["PREV_DAYS_DECISION_MIN"])))))-(((data["APPROVED_HOUR_APPR_PROCESS_START_MAX"])+(data["NEW_EXT_SOURCES_MEAN"])))))))* 2.0)) * 2.0))
v["i25"] = 0.049792*np.tanh(((data["REGION_RATING_CLIENT_W_CITY"])+(((np.where(data["INSTAL_DPD_MEAN"]<0, np.where(data["EXT_SOURCE_1"] > -1, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"], np.where(data["NEW_CAR_TO_BIRTH_RATIO"] > -1, data["REFUSED_DAYS_DECISION_MAX"], data["DAYS_EMPLOYED"])) ,(-1.0*(( data["NEW_CAR_TO_BIRTH_RATIO"])))))* 2.0))))
v["i26"] = 0.049621*np.tanh(((((((np.where(data["PREV_NAME_YIELD_GROUP_low_action_MEAN"]<0,(((( data["NEW_ANNUITY_TO_INCOME_RATIO"])-(data["CODE_GENDER"])))-(data["NEW_EXT_SOURCES_MEAN"])) , data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"])) -(data["INSTAL_DBD_SUM"])))-(data["APPROVED_AMT_ANNUITY_MEAN"])))+(data["PREV_CNT_PAYMENT_MEAN"])))
v["i27"] = 0.049640*np.tanh(((((((((((data["APPROVED_CNT_PAYMENT_MEAN"])+(data["PREV_NAME_CLIENT_TYPE_New_MEAN"])))+(data["PREV_NAME_CONTRACT_STATUS_Refused_MEAN"])))+(np.where(data["CC_CNT_INSTALMENT_MATURE_CUM_MEAN"] > -1, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"], np.tanh(( data["DAYS_EMPLOYED"])))))) -(data["NAME_EDUCATION_TYPE_Higher_education"])))* 2.0))
v["i28"] = 0.049849*np.tanh(((np.where(data["POS_SK_DPD_DEF_MAX"]>0,(5.0),(((((( np.where(data["AMT_CREDIT"] > -1, data["PREV_NAME_YIELD_GROUP_high_MEAN"], data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])) +(data["DEF_30_CNT_SOCIAL_CIRCLE"])))+(data["NEW_CREDIT_TO_GOODS_RATIO"])))-(data["POS_MONTHS_BALANCE_SIZE"])))) * 2.0))
v["i29"] = 0.049352*np.tanh(((((((np.where(data["NEW_CAR_TO_EMPLOY_RATIO"]>0, data["CC_AMT_PAYMENT_CURRENT_MIN"],(( data["INSTAL_PAYMENT_DIFF_MAX"])-(data["APPROVED_AMT_ANNUITY_MEAN"])))) -(np.where(data["BURO_CREDIT_ACTIVE_Closed_MEAN"]<0, data["YEARS_BUILD_AVG"], data["CODE_GENDER"])))) * 2.0)) +(data["NEW_DOC_IND_KURT"])))
v["i30"] = 0.049816*np.tanh(((((((( np.where(data["INSTAL_AMT_PAYMENT_MIN"]>0, data["REFUSED_DAYS_DECISION_MAX"], data["APPROVED_DAYS_DECISION_MIN"])) +(data["INSTAL_PAYMENT_DIFF_MEAN"])) /2.0)) +(((data["APPROVED_CNT_PAYMENT_MEAN"])+(np.where(data["ACTIVE_MONTHS_BALANCE_SIZE_SUM"] > -1, data["BURO_DAYS_CREDIT_MEAN"], data["DAYS_LAST_PHONE_CHANGE"])))))) * 2.0))
v["i31"] = 0.049908*np.tanh(((np.where(data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"]>0, 2.0, np.where(np.minimum(((((data["INSTAL_AMT_INSTALMENT_MAX"])* 2.0))),(((( data["NEW_EXT_SOURCES_MEAN"])/ 2.0)))) > -1, data["NEW_RATIO_BURO_AMT_ANNUITY_MEAN"],(( data["NEW_ANNUITY_TO_INCOME_RATIO"])-(data["EXT_SOURCE_3"])))))* 2.0))
v["i32"] = 0.049859*np.tanh(((data["DAYS_ID_PUBLISH"])+(((((((data["PREV_CNT_PAYMENT_MEAN"])-(data["POS_MONTHS_BALANCE_SIZE"])))+(((((((data["INSTAL_PAYMENT_DIFF_MEAN"])-(data["INSTAL_AMT_PAYMENT_MIN"])))-(data["INSTAL_AMT_PAYMENT_MIN"])))-(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])))))* 2.0))))
v["i33"] = 0.049192*np.tanh(((((np.where(data["APPROVED_AMT_ANNUITY_MEAN"]<0,(( data["DAYS_LAST_PHONE_CHANGE"])+(((data["AMT_ANNUITY"])+(data["NAME_INCOME_TYPE_Working"])))) , data["PREV_CNT_PAYMENT_MEAN"])) +(data["REGION_RATING_CLIENT_W_CITY"])))+(((data["REG_CITY_NOT_LIVE_CITY"])-(data["CODE_GENDER"])))))
v["i34"] = 0.049730*np.tanh(((((np.where(data["NEW_SOURCES_PROD"] > -1, data["BURO_STATUS_1_MEAN_MEAN"], np.where(data["DAYS_BIRTH"] > -1,(( np.where(data["AMT_GOODS_PRICE"] > -1,(-1.0*(( data["NEW_CAR_TO_BIRTH_RATIO"]))), data["CC_CNT_DRAWINGS_CURRENT_MEAN"])) * 2.0), data["BURO_STATUS_1_MEAN_MEAN"])))* 2.0)) * 2.0))
v["i35"] = 0.050000*np.tanh(((((np.maximum(((data["APPROVED_CNT_PAYMENT_MEAN"])) ,(( data["INSTAL_DAYS_ENTRY_PAYMENT_SUM"])))) +(((((np.maximum(((data["ACTIVE_DAYS_CREDIT_MAX"])) ,(( data["DEF_60_CNT_SOCIAL_CIRCLE"])))) +(((data["INSTAL_DPD_MEAN"])*(( 10.86021709442138672)))))) -(data["OCCUPATION_TYPE_Core_staff"])))))* 2.0))
v["i36"] = 0.048505*np.tanh(((( 7.0)) *(np.where(data["INSTAL_DPD_MEAN"]>0,(1.50285637378692627),(( np.where(data["NAME_FAMILY_STATUS_Married"]>0, np.where(data["ACTIVE_AMT_CREDIT_SUM_DEBT_SUM"]>0, data["NAME_EDUCATION_TYPE_Secondary___secondary_special"], data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"]),(-1.0*(( data["FLOORSMAX_MEDI"])))))* 2.0)))))
v["i37"] = 0.049500*np.tanh(((((np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]>0, data["CC_AMT_RECIVABLE_MIN"], np.where(data["CC_AMT_INST_MIN_REGULARITY_VAR"] > -1, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"], np.where(data["PREV_NAME_YIELD_GROUP_low_action_MEAN"]>0, data["CC_AMT_INST_MIN_REGULARITY_VAR"],(( data["AMT_ANNUITY"])-(data["APPROVED_AMT_ANNUITY_MEAN"])))))) * 2.0)) * 2.0))
v["i38"] = 0.048958*np.tanh(((((data["REG_CITY_NOT_LIVE_CITY"])+(data["PREV_CNT_PAYMENT_MEAN"])))+(((((((data["INSTAL_AMT_INSTALMENT_MAX"])-(data["PREV_NAME_PAYMENT_TYPE_Cash_through_the_bank_MEAN"])))-(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])))-(np.where(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"] > -1, data["PREV_AMT_ANNUITY_MEAN"], data["NEW_CAR_TO_BIRTH_RATIO"]))))))
v["i39"] = 0.049650*np.tanh(((((((np.maximum(((data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])) ,(( np.maximum(((np.where(data["POS_SK_DPD_DEF_MAX"]<0, data["CC_AMT_BALANCE_MIN"], data["INSTAL_PAYMENT_DIFF_MEAN"]))),(((((( data["BURO_CREDIT_TYPE_Microloan_MEAN"])* 2.0)) * 2.0)))))))) * 2.0)) * 2.0)) * 2.0))
v["i40"] = 0.049700*np.tanh(((data["REGION_RATING_CLIENT_W_CITY"])+(((((np.where(((data["POS_MONTHS_BALANCE_SIZE"])* 2.0)> -1, data["PREV_CNT_PAYMENT_MEAN"], np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"]>0, -3.0,(( data["CLOSED_DAYS_CREDIT_VAR"])*(data["INSTAL_COUNT"])))))* 2.0)) * 2.0))))
v["i41"] = 0.049099*np.tanh(np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0,(((((( data["AMT_ANNUITY"])+(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])+(np.maximum(((data["FLAG_WORK_PHONE"])) ,(((((data["REGION_RATING_CLIENT_W_CITY"])>(data["FLAG_WORK_PHONE"])) *1.))))))))) * 2.0)) * 2.0), data["REFUSED_AMT_DOWN_PAYMENT_MIN"]))
v["i42"] = 0.049706*np.tanh(((((np.where(data["NEW_EXT_SOURCES_MEAN"] > -1, np.where(data["NEW_EXT_SOURCES_MEAN"]<0, data["BURO_AMT_CREDIT_SUM_DEBT_MEAN"],(( data["REGION_RATING_CLIENT_W_CITY"])-(data["EXT_SOURCE_1"]))), np.maximum(((data["ACTIVE_DAYS_CREDIT_MEAN"])) ,(((-1.0*(( data["CODE_GENDER"])))))))) * 2.0)) * 2.0))
v["i43"] = 0.048092*np.tanh(((((((data["INSTAL_PAYMENT_DIFF_MEAN"])-(((data["APPROVED_AMT_ANNUITY_MAX"])+(((data["PREV_NAME_YIELD_GROUP_low_action_MEAN"])-(data["INSTAL_AMT_PAYMENT_MAX"])))))))* 2.0)) -(np.maximum(((np.maximum(((data["PREV_PRODUCT_COMBINATION_POS_industry_with_interest_MEAN"])) ,(( data["NEW_EMPLOY_TO_BIRTH_RATIO"]))))),(( data["PREV_NAME_YIELD_GROUP_low_normal_MEAN"]))))))
v["i44"] = 0.049950*np.tanh(((((((((((data["AMT_ANNUITY"])* 2.0)) -(data["PREV_AMT_ANNUITY_MEAN"])))-(((((data["PREV_NAME_CONTRACT_TYPE_Consumer_loans_MEAN"])+(data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])))-(data["DEF_60_CNT_SOCIAL_CIRCLE"])))))-(data["INSTAL_DBD_SUM"])))* 2.0))
v["i45"] = 0.049975*np.tanh(((((((((data["PREV_CODE_REJECT_REASON_SCOFR_MEAN"])-(data["INSTAL_DBD_SUM"])))* 2.0)) +(((data["APPROVED_CNT_PAYMENT_SUM"])-(np.where(data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"]<0, data["NAME_FAMILY_STATUS_Married"],(( data["BURO_MONTHS_BALANCE_SIZE_MEAN"])* 2.0)))))))* 2.0))
v["i46"] = 0.047492*np.tanh(((np.where(data["AMT_ANNUITY"]<0, np.where(data["DAYS_BIRTH"]>0,(-1.0*(((( data["DAYS_BIRTH"])+(data["EXT_SOURCE_1"]))))), data["CC_CNT_DRAWINGS_CURRENT_MAX"]),(((((( data["NEW_CREDIT_TO_GOODS_RATIO"])* 2.0)) * 2.0)) * 2.0)))* 2.0))
v["i47"] = 0.049920*np.tanh(((((((((((data["REGION_RATING_CLIENT_W_CITY"])-(data["NEW_EMPLOY_TO_BIRTH_RATIO"])))-(np.maximum(((np.maximum(((data["PREV_RATE_DOWN_PAYMENT_MEAN"])) ,(( np.maximum(((data["NAME_INCOME_TYPE_State_servant"])) ,(( data["FLOORSMAX_MEDI"])))))))) ,(( data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])))))) * 2.0)) * 2.0)) * 2.0))
v["i48"] = 0.048000*np.tanh(((((((np.where(data["ACTIVE_DAYS_CREDIT_MAX"]>0, data["BURO_AMT_CREDIT_SUM_DEBT_SUM"], data["INSTAL_PAYMENT_DIFF_MAX"])) * 2.0)) * 2.0)) +(((np.where(data["BURO_DAYS_CREDIT_VAR"] > -1, data["ACTIVE_DAYS_CREDIT_MAX"], data["PREV_PRODUCT_COMBINATION_POS_mobile_with_interest_MEAN"])) +(data["ORGANIZATION_TYPE_Self_employed"])))))
v["i49"] = 0.048000*np.tanh(((((np.where(data["NEW_CAR_TO_BIRTH_RATIO"]>0, -3.0,(((((((((data["NEW_EXT_SOURCES_MEAN"])>(data["PREV_NAME_GOODS_CATEGORY_Furniture_MEAN"])) *1.))+(data["FLAG_WORK_PHONE"])))* 2.0)) * 2.0)))-(data["OCCUPATION_TYPE_Core_staff"])))-(data["NEW_EXT_SOURCES_MEAN"])))
v["i50"] = 0.049213*np.tanh(((np.maximum(((data["APPROVED_CNT_PAYMENT_MEAN"])) ,(( np.maximum(((data["NEW_SCORES_STD"])) ,(( np.maximum(((data["REG_CITY_NOT_LIVE_CITY"])) ,(( np.maximum(((data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"])) ,(( data["CC_CNT_DRAWINGS_CURRENT_MAX"])))))))))))))-(((np.maximum(((data["BURO_CREDIT_TYPE_Mortgage_MEAN"])) ,(( data["CODE_GENDER"])))) * 2.0))))
v["i51"] = 0.047502*np.tanh(((np.maximum(((data["INSTAL_PAYMENT_DIFF_MEAN"])) ,(( np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])))))))-(np.maximum(((data["BURO_CREDIT_TYPE_Mortgage_MEAN"])) ,(((((-1.0*(((( data["AMT_ANNUITY"])-(data["NEW_DOC_IND_AVG"])))))) +(data["NAME_INCOME_TYPE_State_servant"]))))))))
v["i52"] = 0.042032*np.tanh(np.where(data["EXT_SOURCE_1"] > -1,(-1.0*(( data["DAYS_BIRTH"]))),(((( data["DAYS_BIRTH"])-(data["FLAG_DOCUMENT_8"])))-(np.where(( -1.0*(( data["DAYS_BIRTH"])))<0, data["OCCUPATION_TYPE_High_skill_tech_staff"], data["CODE_GENDER"])))))
v["i53"] = 0.049812*np.tanh(((np.where(((data["INSTAL_COUNT"])* 2.0)> -1, data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"],(( data["LIVINGAREA_MEDI"])*(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_SUM"])))) -(np.where(data["BURO_CREDIT_TYPE_Microloan_MEAN"]>0, data["ACTIVE_MONTHS_BALANCE_SIZE_MEAN"],(( data["PREV_AMT_ANNUITY_MEAN"])-(data["PREV_AMT_APPLICATION_MAX"]))))))
v["i54"] = 0.049694*np.tanh(((np.where(data["PREV_AMT_ANNUITY_MAX"]<0,(( data["NEW_CREDIT_TO_ANNUITY_RATIO"])*(((data["NONLIVINGAPARTMENTS_MODE"])-(data["PREV_NAME_TYPE_SUITE_nan_MEAN"])))) , np.where(data["PREV_NAME_TYPE_SUITE_nan_MEAN"] > -1,(( data["APPROVED_CNT_PAYMENT_MEAN"])+(data["OCCUPATION_TYPE_Drivers"])) , data["PREV_NAME_TYPE_SUITE_nan_MEAN"])))* 2.0))
v["i55"] = 0.049701*np.tanh(((((np.where(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"] > -1,(((((((( data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])-(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])))+(data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])))* 2.0)) -(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"])) , data["INSTAL_PAYMENT_DIFF_MEAN"])) * 2.0)) * 2.0))
v["i56"] = 0.049080*np.tanh(np.where(data["ACTIVE_DAYS_CREDIT_MEAN"] > -1,(((( np.where(data["BURO_CREDIT_ACTIVE_Active_MEAN"]>0, data["ACTIVE_DAYS_CREDIT_MAX"], np.where(data["ACTIVE_AMT_CREDIT_SUM_MEAN"]>0, data["ACTIVE_DAYS_CREDIT_MAX"], data["BURO_CREDIT_ACTIVE_Active_MEAN"])))* 2.0)) * 2.0), np.maximum(((data["EXT_SOURCE_3"])) ,(( data["REFUSED_DAYS_DECISION_MAX"])))))
v["i57"] = 0.049955*np.tanh(((np.where(data["PREV_AMT_APPLICATION_MAX"]<0, np.minimum(((data["AMT_ANNUITY"])) ,(( data["NEW_DOC_IND_KURT"]))), data["APPROVED_CNT_PAYMENT_SUM"])) +(((((np.minimum(((data["REGION_POPULATION_RELATIVE"])) ,(( data["AMT_ANNUITY"])))) -(data["APPROVED_AMT_ANNUITY_MEAN"])))-(data["APPROVED_AMT_ANNUITY_MEAN"])))))
v["i58"] = 0.048588*np.tanh(((np.where(((data["NEW_DOC_IND_KURT"])+(data["NEW_CREDIT_TO_ANNUITY_RATIO"])) > -1,(((((-1.0*(((( data["NEW_CREDIT_TO_ANNUITY_RATIO"])* 2.0)))))-(data["WEEKDAY_APPR_PROCESS_START_SATURDAY"])))* 2.0), np.minimum(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])) ,(( data["INSTAL_PAYMENT_DIFF_SUM"])))))* 2.0))
v["i59"] = 0.039981*np.tanh(((np.where(data["NEW_CAR_TO_EMPLOY_RATIO"] > -1, data["REFUSED_DAYS_DECISION_MAX"],(( np.maximum(((np.maximum(((data["DEF_30_CNT_SOCIAL_CIRCLE"])) ,(((( data["APPROVED_CNT_PAYMENT_MEAN"])+(data["BURO_CREDIT_TYPE_Microloan_MEAN"]))))))),(( data["DAYS_ID_PUBLISH"])))) +(data["NEW_CREDIT_TO_ANNUITY_RATIO"])))) +(data["NEW_CREDIT_TO_INCOME_RATIO"])))
v["i60"] = 0.035776*np.tanh(((((np.where(data["AMT_REQ_CREDIT_BUREAU_QRT"]<0, data["NEW_CREDIT_TO_ANNUITY_RATIO"], -2.0)) +(np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( data["INSTAL_DBD_MAX"])))))) -(np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"]<0, data["PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN"],(-1.0*(( -2.0)))))))
v["i61"] = 0.049702*np.tanh(((((((data["ORGANIZATION_TYPE_Construction"])+(np.maximum(((data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"])) ,(( np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( np.where(np.maximum(((data["INSTAL_DPD_MEAN"])) ,(( data["CC_AMT_BALANCE_MAX"])))<0, data["PREV_CNT_PAYMENT_SUM"], data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"])))))))))) * 2.0)) * 2.0))
v["i62"] = 0.049299*np.tanh(((((((np.where(( -1.0*(( data["NEW_EXT_SOURCES_MEAN"])))> -1,(( np.where(data["CC_AMT_TOTAL_RECEIVABLE_MEAN"] > -1, data["NEW_EXT_SOURCES_MEAN"],(((data["NEW_EXT_SOURCES_MEAN"])>(data["PREV_AMT_DOWN_PAYMENT_MAX"])) *1.))) * 2.0), data["CC_CNT_DRAWINGS_POS_CURRENT_VAR"])) * 2.0)) * 2.0)) * 2.0))
v["i63"] = 0.033400*np.tanh(((data["WALLSMATERIAL_MODE_Stone__brick"])+(((((data["POS_SK_DPD_DEF_MAX"])-(data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])))+(((((( data["APPROVED_APP_CREDIT_PERC_MIN"])<(data["INSTAL_PAYMENT_DIFF_MEAN"])) *1.))-(np.maximum(((data["INSTAL_AMT_PAYMENT_SUM"])) ,(( data["BURO_CREDIT_TYPE_Mortgage_MEAN"]))))))))))
v["i64"] = 0.049672*np.tanh(((((((np.maximum(((data["INSTAL_DPD_MEAN"])) ,(( np.maximum(((data["ORGANIZATION_TYPE_Construction"])) ,(( data["BURO_CREDIT_TYPE_Microloan_MEAN"])))))))* 2.0)) * 2.0)) -(((((data["NEW_PHONE_TO_BIRTH_RATIO"])-(np.tanh(( data["PREV_CHANNEL_TYPE_Contact_center_MEAN"])))))-(data["NAME_FAMILY_STATUS_Separated"])))))
v["i65"] = 0.048401*np.tanh(((data["ORGANIZATION_TYPE_Business_Entity_Type_3"])-(((((data["POS_MONTHS_BALANCE_SIZE"])-(np.maximum(((data["APPROVED_CNT_PAYMENT_SUM"])) ,(( data["POS_SK_DPD_MAX"])))))) -(np.maximum(((data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"])) ,(( np.maximum(((data["INSTAL_PAYMENT_DIFF_MEAN"])) ,(( data["NAME_HOUSING_TYPE_Municipal_apartment"])))))))))))
v["i66"] = 0.036240*np.tanh(((((( data["REGION_RATING_CLIENT_W_CITY"])+(((np.where(data["NEW_EXT_SOURCES_MEAN"]>0, data["NAME_EDUCATION_TYPE_Secondary___secondary_special"],(((((( np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_SUM"])) ,(( data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])))) * 2.0)) -(data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"])))* 2.0)))* 2.0)))/2.0)) * 2.0))
v["i67"] = 0.049448*np.tanh(np.where(data["NEW_CREDIT_TO_GOODS_RATIO"] > -1, np.minimum(((data["NEW_CREDIT_TO_GOODS_RATIO"])) ,(((( data["ACTIVE_AMT_CREDIT_SUM_MAX"])*(np.where(data["PREV_AMT_ANNUITY_MIN"]>0, data["CODE_GENDER"], data["INSTAL_AMT_INSTALMENT_SUM"])))))) , np.where(data["PREV_AMT_ANNUITY_MIN"]>0, data["PREV_NAME_CONTRACT_STATUS_Approved_MEAN"], data["CODE_GENDER"])))
v["i68"] = 0.045202*np.tanh(((((data["ORGANIZATION_TYPE_Self_employed"])+(((data["INSTAL_DBD_MAX"])-(((data["APPROVED_AMT_CREDIT_MIN"])+(np.maximum(((data["NEW_PHONE_TO_EMPLOY_RATIO"])) ,(( data["BURO_STATUS_0_MEAN_MEAN"])))))))))) -(np.maximum(((data["APPROVED_AMT_CREDIT_MIN"])) ,(( data["BURO_AMT_CREDIT_SUM_MEAN"]))))))
v["i69"] = 0.050000*np.tanh(np.where(data["AMT_REQ_CREDIT_BUREAU_QRT"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"], np.where(data["BURO_CREDIT_TYPE_Credit_card_MEAN"] > -1, np.where(data["PREV_NAME_CONTRACT_TYPE_Consumer_loans_MEAN"] > -1,(( data["EXT_SOURCE_3"])-(data["NEW_EXT_SOURCES_MEAN"])) , data["BURO_AMT_CREDIT_SUM_DEBT_SUM"]),(((( data["NEW_EXT_SOURCES_MEAN"])* 2.0)) * 2.0))))
v["i70"] = 0.047500*np.tanh(( -1.0*(((((( np.maximum(((data["BURO_CREDIT_TYPE_Mortgage_MEAN"])) ,(((( np.maximum(((((data["FLAG_DOCUMENT_18"])* 2.0))),(( np.where(data["AMT_GOODS_PRICE"] > -1, data["CC_AMT_PAYMENT_CURRENT_SUM"],(-1.0*(( data["CC_AMT_BALANCE_MEAN"])))))))) * 2.0)))))* 2.0)) * 2.0)))))
v["i71"] = 0.049392*np.tanh(((((((((((((np.maximum(((data["ACTIVE_AMT_CREDIT_SUM_SUM"])) ,(( data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])))) -(np.where(data["DAYS_ID_PUBLISH"] > -1, data["BURO_AMT_CREDIT_SUM_MEAN"], 0.318310)))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i72"] = 0.049200*np.tanh(np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"] > -1, np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"]<0, data["CC_AMT_BALANCE_MEAN"],(-1.0*(( data["INSTAL_PAYMENT_DIFF_MAX"])))) , np.maximum(((data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"])) ,(((( np.maximum(((data["FLAG_WORK_PHONE"])) ,(( data["INSTAL_PAYMENT_DIFF_MAX"])))) +(data["DAYS_REGISTRATION"])))))))
v["i73"] = 0.050000*np.tanh(((data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__high_MEAN"])+(np.where(data["CLOSED_DAYS_CREDIT_MAX"]>0, data["BURO_CREDIT_TYPE_Microloan_MEAN"],(( data["REFUSED_AMT_ANNUITY_MAX"])*(((((( data["PREV_NAME_GOODS_CATEGORY_Computers_MEAN"])+(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"])) /2.0)) -(data["EXT_SOURCE_3"]))))))))
v["i74"] = 0.049580*np.tanh(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])+(((((((data["REGION_RATING_CLIENT_W_CITY"])+(np.where(data["CODE_GENDER"] > -1, data["REGION_POPULATION_RELATIVE"],(((-1.0*(((( data["NEW_CREDIT_TO_ANNUITY_RATIO"])+(data["BURO_CREDIT_TYPE_Mortgage_MEAN"])))))) * 2.0)))))* 2.0)) * 2.0))))
v["i75"] = 0.047500*np.tanh(np.where(data["NEW_DOC_IND_STD"]<0, data["PREV_NAME_TYPE_SUITE_nan_MEAN"],(((( data["CC_AMT_RECIVABLE_MAX"])-(data["CC_AMT_PAYMENT_CURRENT_SUM"])))+(np.where(data["ACTIVE_MONTHS_BALANCE_SIZE_MEAN"] > -1, data["EXT_SOURCE_2"],(( data["INSTAL_COUNT"])*(data["EXT_SOURCE_2"])))))))
v["i76"] = 0.049748*np.tanh(np.where(np.tanh(( data["PREV_APP_CREDIT_PERC_MIN"])) > -1, np.where(data["POS_NAME_CONTRACT_STATUS_Completed_MEAN"] > -1,(( data["NAME_EDUCATION_TYPE_Lower_secondary"])-(((((data["PREV_APP_CREDIT_PERC_MIN"])* 2.0)) * 2.0))),(( data["CC_CNT_INSTALMENT_MATURE_CUM_VAR"])*(data["BURO_AMT_CREDIT_SUM_MAX"]))), data["NEW_DOC_IND_STD"]))
v["i77"] = 0.048004*np.tanh(np.where(data["CC_AMT_BALANCE_VAR"]>0, data["CC_CNT_DRAWINGS_POS_CURRENT_MAX"],(( np.where(data["CC_AMT_CREDIT_LIMIT_ACTUAL_SUM"] > -1, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_MEAN"],(( data["DAYS_BIRTH"])*(data["NAME_FAMILY_STATUS_Married"])))) -(np.where(data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"]<0, data["NAME_FAMILY_STATUS_Married"], data["REFUSED_AMT_CREDIT_MIN"])))))
v["i78"] = 0.049471*np.tanh(np.where(data["INSTAL_COUNT"]>0, np.where(data["EXT_SOURCE_2"]<0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_MAX"], data["APPROVED_CNT_PAYMENT_SUM"]),(( -1.0)-(np.where(data["EXT_SOURCE_3"] > -1, data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"],(( data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_MAX"])-(data["EXT_SOURCE_3"])))))))
v["i79"] = 0.047294*np.tanh(( -1.0*(((( data["NEW_RATIO_PREV_AMT_CREDIT_MIN"])*(np.where(((((data["FLAG_DOCUMENT_8"])+(data["WEEKDAY_APPR_PROCESS_START_MONDAY"])))+(data["NEW_DOC_IND_AVG"])) >0, data["BASEMENTAREA_MODE"],(( data["POS_SK_DPD_DEF_MEAN"])-(data["ORGANIZATION_TYPE_Military"])))))))))
v["i80"] = 0.049797*np.tanh(((data["AMT_ANNUITY"])-(np.where(data["POS_SK_DPD_DEF_MAX"]<0, np.maximum(((data["ACTIVE_MONTHS_BALANCE_SIZE_MEAN"])) ,(((( np.where(data["APPROVED_APP_CREDIT_PERC_MAX"]<0, data["INSTAL_AMT_PAYMENT_SUM"],(( np.tanh(( data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])))* 2.0)))* 2.0)))) , data["ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN"]))))
v["i81"] = 0.049970*np.tanh(np.where(data["NEW_PHONE_TO_BIRTH_RATIO"] > -1,(( data["PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN"])+(np.where(data["BURO_DAYS_CREDIT_ENDDATE_MAX"]>0, data["ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN"], data["APPROVED_AMT_CREDIT_MAX"]))),(( data["WALLSMATERIAL_MODE_Stone__brick"])-(np.where(data["PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN"]<0, data["POS_MONTHS_BALANCE_MEAN"],(9.0))))))
v["i82"] = 0.047933*np.tanh(np.where(data["CLOSED_DAYS_CREDIT_MAX"]<0, np.maximum(((data["CC_CNT_DRAWINGS_CURRENT_MAX"])) ,(( np.where(data["CC_AMT_BALANCE_VAR"] > -1, data["CC_CNT_DRAWINGS_CURRENT_MAX"], np.maximum(((np.maximum(((((data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"])* 2.0))),(( data["DEF_60_CNT_SOCIAL_CIRCLE"]))))),(( data["PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN"]))))))), data["ACTIVE_AMT_CREDIT_SUM_SUM"]))
v["i83"] = 0.049578*np.tanh(np.where(data["LANDAREA_AVG"]>0, data["PREV_NAME_YIELD_GROUP_XNA_MEAN"], np.where(data["BURO_AMT_CREDIT_SUM_MEAN"]>0, data["NAME_INCOME_TYPE_Commercial_associate"],(-1.0*(( np.where(((data["NEW_PHONE_TO_EMPLOY_RATIO"])+(data["BURO_DAYS_CREDIT_MAX"])) > -1, data["NAME_INCOME_TYPE_Commercial_associate"], data["CC_AMT_CREDIT_LIMIT_ACTUAL_MAX"])))))))
v["i84"] = 0.049998*np.tanh(((np.where(((data["AMT_GOODS_PRICE"])*(data["CC_AMT_RECIVABLE_VAR"])) >0,(((( data["APPROVED_CNT_PAYMENT_SUM"])+(data["AMT_ANNUITY"])))-(np.where(data["NEW_DOC_IND_KURT"] > -1, data["INSTAL_AMT_INSTALMENT_SUM"], data["NEW_DOC_IND_AVG"]))), data["PREV_NAME_CONTRACT_TYPE_Revolving_loans_MEAN"])) * 2.0))
v["i85"] = 0.000199*np.tanh(np.minimum(((np.minimum(((data["NEW_DOC_IND_KURT"])) ,(((-1.0*(((((data["WEEKDAY_APPR_PROCESS_START_SUNDAY"])+(((data["PREV_NAME_YIELD_GROUP_low_action_MEAN"])+(np.where(data["POS_SK_DPD_DEF_MAX"]<0, data["OCCUPATION_TYPE_Medicine_staff"], data["PREV_NAME_PORTFOLIO_Cash_MEAN"])))))/2.0))))))))),(((-1.0*(( data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"])))))))
v["i86"] = 0.044998*np.tanh(((np.where(((( data["POS_COUNT"])<(np.tanh(( data["APPROVED_CNT_PAYMENT_SUM"])))) *1.) >0, np.where(data["PREV_NAME_YIELD_GROUP_low_action_MEAN"]>0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"],(((data["CLOSED_MONTHS_BALANCE_SIZE_MEAN"])<(data["POS_COUNT"])) *1.)),(( data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"])* 2.0)))* 2.0))
v["i87"] = 0.049818*np.tanh(((((data["CC_CNT_DRAWINGS_ATM_CURRENT_MEAN"])-(((np.where(data["ACTIVE_AMT_CREDIT_SUM_LIMIT_SUM"]<0, data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"], data["NEW_CAR_TO_BIRTH_RATIO"])) -(np.where(data["PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN"]>0, data["PREV_PRODUCT_COMBINATION_POS_household_without_interest_MEAN"],(((data["INSTAL_PAYMENT_DIFF_MAX"])>(data["PREV_PRODUCT_COMBINATION_POS_industry_with_interest_MEAN"])) *1.))))))) * 2.0))
v["i88"] = 0.048104*np.tanh(((data["CC_CNT_DRAWINGS_POS_CURRENT_VAR"])-(((data["CC_AMT_PAYMENT_CURRENT_MEAN"])-(np.where(data["INSTAL_DPD_MEAN"]<0,(((( data["APPROVED_CNT_PAYMENT_MEAN"])*(data["APPROVED_DAYS_DECISION_MAX"])))-(data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"])) ,(((( data["INSTAL_DAYS_ENTRY_PAYMENT_MEAN"])* 2.0)) * 2.0)))))))
v["i89"] = 0.043416*np.tanh(((((np.where(data["CC_AMT_PAYMENT_CURRENT_SUM"]<0, np.where(data["ORGANIZATION_TYPE_School"]<0, np.where(data["NAME_INCOME_TYPE_State_servant"]<0, data["NEW_DOC_IND_KURT"], data["REFUSED_CNT_PAYMENT_MEAN"]),(( data["REFUSED_CNT_PAYMENT_SUM"])* 2.0)) , data["WALLSMATERIAL_MODE_Stone__brick"])) * 2.0)) * 2.0))
v["i90"] = 0.049199*np.tanh(((((data["POS_SK_DPD_DEF_MAX"])-(np.where(data["NAME_CONTRACT_TYPE_Cash_loans"] > -1, data["NAME_CONTRACT_TYPE_Cash_loans"], data["LIVINGAPARTMENTS_MEDI"])))) +(np.maximum(((np.maximum(((data["BURO_CREDIT_TYPE_Microloan_MEAN"])) ,(( np.maximum(((data["OCCUPATION_TYPE_Drivers"])) ,(( data["POS_NAME_CONTRACT_STATUS_Returned_to_the_store_MEAN"])))))))) ,(( data["NAME_HOUSING_TYPE_Municipal_apartment"]))))))
v["i91"] = 0.041880*np.tanh(np.where(data["CC_CNT_DRAWINGS_ATM_CURRENT_MAX"] > -1,(( data["REFUSED_RATE_DOWN_PAYMENT_MEAN"])*(((data["APPROVED_AMT_DOWN_PAYMENT_MIN"])* 2.0))),(( data["PREV_NAME_YIELD_GROUP_high_MEAN"])*(np.where(data["FLOORSMIN_MEDI"]<0, data["AMT_GOODS_PRICE"],(( data["REFUSED_RATE_DOWN_PAYMENT_MEAN"])*(data["APPROVED_AMT_DOWN_PAYMENT_MIN"])))))))
v["i92"] = 0.049800*np.tanh(np.where(data["APPROVED_HOUR_APPR_PROCESS_START_MAX"]>0,(((data["EXT_SOURCE_3"])+(data["APPROVED_CNT_PAYMENT_SUM"])) /2.0), np.where(data["EXT_SOURCE_3"] > -1, data["OCCUPATION_TYPE_Laborers"], np.maximum(((data["NEW_EXT_SOURCES_MEAN"])) ,(((( np.maximum(((data["ACTIVE_DAYS_CREDIT_MAX"])) ,(( data["DEF_30_CNT_SOCIAL_CIRCLE"])))) * 2.0)))))))
v["i93"] = 0.049748*np.tanh(np.where(data["BURO_AMT_CREDIT_SUM_DEBT_MAX"]<0, np.where(data["AMT_GOODS_PRICE"]<0, np.where(((data["AMT_GOODS_PRICE"])+(data["NEW_DOC_IND_AVG"])) >0, data["NEW_RATIO_BURO_AMT_CREDIT_SUM_LIMIT_MEAN"],(( data["NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM"])*(data["PREV_PRODUCT_COMBINATION_Cash_Street__low_MEAN"]))), data["APPROVED_AMT_DOWN_PAYMENT_MAX"]), data["AMT_GOODS_PRICE"]))
v["i94"] = 0.049770*np.tanh(((np.where(data["INSTAL_PAYMENT_DIFF_VAR"] > -1, np.where(data["AMT_INCOME_TOTAL"]<0, data["AMT_ANNUITY"], np.minimum(((data["NEW_DOC_IND_KURT"])) ,(( np.where(data["REFUSED_AMT_GOODS_PRICE_MEAN"]>0,(-1.0*(( data["DAYS_BIRTH"]))), data["NEW_CREDIT_TO_ANNUITY_RATIO"]))))), data["NEW_DOC_IND_STD"])) * 2.0))
v["i95"] = 0.048000*np.tanh(np.where(data["BURO_DAYS_CREDIT_UPDATE_MEAN"] > -1, np.maximum(((data["NAME_EDUCATION_TYPE_Lower_secondary"])) ,(( np.where(data["BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"]<0, data["BURO_CREDIT_TYPE_Microloan_MEAN"],(-1.0*(( data["ACTIVE_MONTHS_BALANCE_MAX_MAX"]))))))),(( data["YEARS_BUILD_AVG"])*(((data["NAME_EDUCATION_TYPE_Lower_secondary"])-(data["REFUSED_APP_CREDIT_PERC_VAR"]))))))
v["i96"] = 0.049784*np.tanh(((((((np.where(data["CODE_GENDER"]>0, np.where(data["PREV_CNT_PAYMENT_SUM"] > -1, data["DAYS_BIRTH"], data["BURO_DAYS_CREDIT_VAR"]),(((((data["AMT_CREDIT"])<(data["DAYS_BIRTH"])) *1.))-(data["DAYS_BIRTH"])))) * 2.0)) * 2.0)) * 2.0))
v["i97"] = 0.035002*np.tanh(np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_MIN"]<0, np.where(data["NAME_HOUSING_TYPE_Rented_apartment"]<0, np.where(data["LIVINGAPARTMENTS_AVG"]<0, data["WEEKDAY_APPR_PROCESS_START_WEDNESDAY"],(( data["INSTAL_AMT_PAYMENT_MIN"])-(data["NEW_RATIO_PREV_AMT_ANNUITY_MAX"]))), data["NAME_HOUSING_TYPE_Rented_apartment"]),(( data["INSTAL_AMT_PAYMENT_MIN"])-(data["NEW_LIVE_IND_SUM"]))))
v["i98"] = 0.035000*np.tanh(((((data["CC_AMT_DRAWINGS_ATM_CURRENT_MEAN"])*(((( data["APPROVED_CNT_PAYMENT_SUM"])<(np.where(data["EXT_SOURCE_3"]<0, data["POS_MONTHS_BALANCE_SIZE"], data["BURO_AMT_CREDIT_SUM_MAX"])))*1.))))-(np.where(data["DAYS_EMPLOYED"]<0, data["NEW_CAR_TO_BIRTH_RATIO"], data["APPROVED_CNT_PAYMENT_SUM"]))))
v["i99"] = 0.049903*np.tanh(((np.where(data["ORGANIZATION_TYPE_Industry__type_9"]>0, data["CC_AMT_CREDIT_LIMIT_ACTUAL_MEAN"],(( data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"])*(data["INSTAL_PAYMENT_DIFF_MAX"])))) -(np.where(data["OBS_30_CNT_SOCIAL_CIRCLE"]<0, data["INSTAL_AMT_PAYMENT_SUM"],(( data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"])-(data["APPROVED_AMT_CREDIT_MEAN"]))))))
v["i100"] = 0.048280*np.tanh(((((np.where(data["CLOSED_DAYS_CREDIT_MAX"]>0, data["ACTIVE_AMT_CREDIT_SUM_SUM"], np.where(data["CLOSED_AMT_CREDIT_MAX_OVERDUE_MEAN"]<0, np.where(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"] > -1, data["INSTAL_PAYMENT_DIFF_MAX"],(-1.0*(( data["ACTIVE_AMT_CREDIT_SUM_SUM"])))) , data["CLOSED_MONTHS_BALANCE_MIN_MIN"])))* 2.0)) +(data["POS_SK_DPD_DEF_MAX"])))
v["i101"] = 0.048002*np.tanh(np.where(data["BURO_AMT_CREDIT_SUM_MEAN"]>0, data["REGION_RATING_CLIENT_W_CITY"], np.where(data["REFUSED_APP_CREDIT_PERC_MAX"]>0, data["PREV_WEEKDAY_APPR_PROCESS_START_THURSDAY_MEAN"], np.maximum(((data["CC_AMT_BALANCE_VAR"])) ,(( np.maximum(((((( data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])>(((data["OCCUPATION_TYPE_Core_staff"])/ 2.0)))*1.))) ,(( data["ORGANIZATION_TYPE_Transport__type_3"])))))))))
v["i102"] = 0.046002*np.tanh(np.where(((( data["NEW_EXT_SOURCES_MEAN"])+(data["OCCUPATION_TYPE_Core_staff"])) /2.0)> -1,(( data["NEW_EXT_SOURCES_MEAN"])-(((data["NEW_EXT_SOURCES_MEAN"])*(data["NEW_EXT_SOURCES_MEAN"])))) ,(( data["NEW_EXT_SOURCES_MEAN"])*(data["NEW_EXT_SOURCES_MEAN"]))))
v["i103"] = 0.049800*np.tanh(((np.maximum(((data["NEW_RATIO_PREV_APP_CREDIT_PERC_MEAN"])) ,(( np.where(data["NEW_CREDIT_TO_GOODS_RATIO"]>0, data["PREV_DAYS_DECISION_MAX"], np.where(data["NEW_CREDIT_TO_GOODS_RATIO"] > -1, data["ORGANIZATION_TYPE_Business_Entity_Type_3"],(((data["INSTAL_DBD_MAX"])>(data["PREV_NAME_CONTRACT_STATUS_Canceled_MEAN"])) *1.))))))) * 2.0))
v["i104"] = 0.049408*np.tanh(np.where(data["DAYS_ID_PUBLISH"] > -1,(( data["DAYS_BIRTH"])*(((data["NAME_EDUCATION_TYPE_Secondary___secondary_special"])+(np.where(data["DAYS_BIRTH"] > -1, np.where(data["PREV_NAME_PRODUCT_TYPE_walk_in_MEAN"]<0, data["NAME_EDUCATION_TYPE_Secondary___secondary_special"], data["CLOSED_AMT_ANNUITY_MEAN"]), data["NAME_INCOME_TYPE_Working"]))))), data["CLOSED_AMT_ANNUITY_MEAN"]))
v["i105"] = 0.046500*np.tanh(np.where(data["ACTIVE_MONTHS_BALANCE_SIZE_MEAN"] > -1, data["AMT_GOODS_PRICE"],(-1.0*(( np.where(data["PREV_NAME_PORTFOLIO_Cards_MEAN"]>0, data["CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN"], np.where(data["NEW_SCORES_STD"]>0,(-1.0*(( data["NEW_SCORES_STD"]))),(((data["PREV_NAME_PORTFOLIO_Cards_MEAN"])>(data["NEW_EXT_SOURCES_MEAN"])) *1.))))))))
v["i106"] = 0.047502*np.tanh(np.where(data["CODE_GENDER"]<0, data["NAME_FAMILY_STATUS_Separated"], np.where(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"]<0, np.where(data["DAYS_BIRTH"]<0, data["NEW_DOC_IND_AVG"], data["PREV_NAME_GOODS_CATEGORY_Consumer_Electronics_MEAN"]), np.where(data["AMT_REQ_CREDIT_BUREAU_MON"] > -1, data["CLOSED_DAYS_CREDIT_MEAN"], data["NEW_INC_PER_CHLD"]))))
v["i107"] = 0.048562*np.tanh(((np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_ENDDATE_MIN"]>0, data["BURO_CREDIT_TYPE_Credit_card_MEAN"],(( data["POS_SK_DPD_DEF_MAX"])-(data["APPROVED_AMT_GOODS_PRICE_MIN"])))) -(np.maximum(((data["BURO_CREDIT_TYPE_Car_loan_MEAN"])) ,(( np.maximum(((data["PREV_CHANNEL_TYPE_Channel_of_corporate_sales_MEAN"])) ,(( np.maximum(((data["WEEKDAY_APPR_PROCESS_START_SUNDAY"])) ,(( data["ACTIVE_AMT_CREDIT_SUM_LIMIT_SUM"]))))))))))))
v["i108"] = 0.049957*np.tanh(np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_MEAN"]>0,(( data["INSTAL_AMT_PAYMENT_MIN"])-(np.tanh(( 2.0)))) ,(( data["CC_AMT_INST_MIN_REGULARITY_MAX"])-(((data["CC_AMT_CREDIT_LIMIT_ACTUAL_MIN"])-(((( data["ACTIVE_AMT_CREDIT_SUM_DEBT_MAX"])<(((data["BURO_AMT_CREDIT_SUM_DEBT_MEAN"])* 2.0)))*1.)))))))
v["i109"] = 0.037995*np.tanh(np.where(data["POS_NAME_CONTRACT_STATUS_Completed_MEAN"]>0, data["AMT_ANNUITY"], np.where(data["REFUSED_HOUR_APPR_PROCESS_START_MIN"]>0, data["PREV_RATE_DOWN_PAYMENT_MAX"], np.maximum(((np.maximum(((np.where(data["ACTIVE_DAYS_CREDIT_MIN"]>0, data["CLOSED_MONTHS_BALANCE_SIZE_SUM"], data["NAME_EDUCATION_TYPE_Higher_education"]))),(( data["FLAG_WORK_PHONE"]))))),(( data["FLAG_WORK_PHONE"]))))))
v["i110"] = 0.045640*np.tanh(((np.where(data["CC_AMT_CREDIT_LIMIT_ACTUAL_MIN"]<0, np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]>0, data["CC_AMT_CREDIT_LIMIT_ACTUAL_MIN"],(-1.0*(((( data["PREV_DAYS_DECISION_MIN"])*(np.where(data["REFUSED_AMT_APPLICATION_MAX"] > -1, data["INSTAL_DBD_MAX"], data["NEW_DOC_IND_AVG"]))))))), data["CC_AMT_INST_MIN_REGULARITY_VAR"])) * 2.0))
v["i111"] = 0.050000*np.tanh(((data["CC_NAME_CONTRACT_STATUS_Active_MEAN"])*(((( data["POS_NAME_CONTRACT_STATUS_Signed_MEAN"])<(np.where(data["ACTIVE_AMT_CREDIT_SUM_DEBT_MAX"]>0, data["PREV_NAME_CONTRACT_STATUS_Canceled_MEAN"],(( np.where(data["INSTAL_DPD_MEAN"]>0, np.minimum(((data["ACTIVE_AMT_CREDIT_SUM_DEBT_MAX"])) ,(( data["PREV_CNT_PAYMENT_MEAN"]))), data["LANDAREA_MEDI"])) / 2.0)))) *1.))))
v["i112"] = 0.046700*np.tanh(((data["WALLSMATERIAL_MODE_Stone__brick"])+(((( data["ORGANIZATION_TYPE_Construction"])+(( -1.0*(( np.where(data["DEF_30_CNT_SOCIAL_CIRCLE"]>0, data["CC_AMT_INST_MIN_REGULARITY_SUM"], np.where(data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"]>0,(((data["INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE"])+(data["REFUSED_AMT_CREDIT_MIN"])) /2.0), data["NEW_PHONE_TO_EMPLOY_RATIO"])))))))/2.0))))
v["i113"] = 0.049002*np.tanh(np.where(data["NEW_EMPLOY_TO_BIRTH_RATIO"]>0,(((((data["NEW_CREDIT_TO_INCOME_RATIO"])*(data["BURO_DAYS_CREDIT_MAX"])))+(data["BURO_DAYS_CREDIT_MEAN"])) /2.0), np.where(data["BURO_DAYS_CREDIT_MAX"]>0, data["ACTIVE_AMT_CREDIT_SUM_SUM"],(((data["ORGANIZATION_TYPE_Transport__type_3"])>(data["INSTAL_DAYS_ENTRY_PAYMENT_MAX"])) *1.))))
v["i114"] = 0.049400*np.tanh(( -1.0*(((( data["OCCUPATION_TYPE_Accountants"])+(((data["ORGANIZATION_TYPE_Military"])+(np.where(data["PREV_NAME_GOODS_CATEGORY_Computers_MEAN"]>0, data["EXT_SOURCE_2"],(( data["OCCUPATION_TYPE_Core_staff"])+(((((( data["ENTRANCES_AVG"])>(data["PREV_NAME_GOODS_CATEGORY_Computers_MEAN"])) *1.))* 2.0))))))))))))
v["i115"] = 0.045008*np.tanh(np.where(data["FLOORSMIN_AVG"] > -1, data["PREV_CHANNEL_TYPE_Country_wide_MEAN"], np.where(data["PREV_NAME_CLIENT_TYPE_New_MEAN"]>0, data["DAYS_REGISTRATION"], np.maximum(((((( data["ORGANIZATION_TYPE_Construction"])+(data["PREV_NAME_CLIENT_TYPE_New_MEAN"])) /2.0))),(( np.where(data["NEW_EMPLOY_TO_BIRTH_RATIO"]>0, data["BURO_STATUS_1_MEAN_MEAN"], data["AMT_INCOME_TOTAL"])))))))
v["i116"] = 0.049852*np.tanh(((data["POS_COUNT"])*(np.where(data["CLOSED_DAYS_CREDIT_MEAN"]<0, np.where(data["PREV_NAME_SELLER_INDUSTRY_Consumer_electronics_MEAN"]>0, data["PREV_RATE_DOWN_PAYMENT_MAX"], data["INSTAL_AMT_PAYMENT_MAX"]), np.maximum(((np.maximum(((data["PREV_WEEKDAY_APPR_PROCESS_START_SUNDAY_MEAN"])) ,(( data["INSTAL_DBD_MEAN"]))))),(( data["DAYS_REGISTRATION"])))))))
v["i117"] = 0.048914*np.tanh(((data["ACTIVE_AMT_CREDIT_SUM_LIMIT_SUM"])*(np.where(data["AMT_INCOME_TOTAL"]>0, data["PREV_WEEKDAY_APPR_PROCESS_START_SUNDAY_MEAN"],(-1.0*(( np.maximum(((data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"])) ,(( np.where(data["LIVINGAREA_AVG"]>0,(-1.0*(( data["EXT_SOURCE_1"]))), data["ACTIVE_AMT_CREDIT_SUM_LIMIT_SUM"])))))))))))
v["i118"] = 0.049651*np.tanh(((((((data["NEW_CREDIT_TO_GOODS_RATIO"])*(((((data["AMT_ANNUITY"])-(data["INSTAL_DBD_SUM"])))-(((data["OCCUPATION_TYPE_Core_staff"])*(np.where(data["INSTAL_DBD_SUM"]>0, data["OCCUPATION_TYPE_Core_staff"], data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])))))))) * 2.0)) * 2.0))
v["i119"] = 0.037400*np.tanh(np.where(data["CC_NAME_CONTRACT_STATUS_Active_VAR"] > -1, data["PREV_PRODUCT_COMBINATION_Cash_MEAN"],(((((-1.0*(( np.where(data["NEW_RATIO_PREV_AMT_GOODS_PRICE_MEAN"] > -1, data["BURO_CREDIT_ACTIVE_Closed_MEAN"], np.where(data["PREV_PRODUCT_COMBINATION_Cash_MEAN"]>0,(-1.0*(( data["NEW_RATIO_BURO_AMT_CREDIT_SUM_SUM"]))), data["ORGANIZATION_TYPE_Industry__type_9"])))))) * 2.0)) * 2.0)))
v["i120"] = 0.049499*np.tanh(((((((( data["POS_SK_DPD_MEAN"])-(data["PREV_WEEKDAY_APPR_PROCESS_START_SATURDAY_MEAN"])))+(np.where(data["NEW_CREDIT_TO_ANNUITY_RATIO"]<0, data["FLAG_DOCUMENT_3"], np.tanh(( data["APARTMENTS_MEDI"])))))/2.0)) -(((( data["NEW_RATIO_PREV_AMT_APPLICATION_MEAN"])>(data["PREV_NAME_PAYMENT_TYPE_XNA_MEAN"])) *1.))))
v["i121"] = 0.049600*np.tanh(((data["REGION_RATING_CLIENT_W_CITY"])*(((data["NEW_EXT_SOURCES_MEAN"])+(np.where(data["TOTALAREA_MODE"]<0, np.where(data["ACTIVE_MONTHS_BALANCE_MIN_MIN"]<0, data["FLAG_DOCUMENT_8"], data["NEW_EXT_SOURCES_MEAN"]), np.where(data["REGION_RATING_CLIENT_W_CITY"]<0, data["ACTIVE_MONTHS_BALANCE_MIN_MIN"], data["FLAG_DOCUMENT_8"])))))))
v["i122"] = 0.050000*np.tanh(np.where(data["POS_COUNT"]<0, np.where(data["NAME_EDUCATION_TYPE_Lower_secondary"]<0,(( data["FLAG_WORK_PHONE"])*(data["EXT_SOURCE_2"])) , data["NAME_EDUCATION_TYPE_Lower_secondary"]),(((((( data["PREV_NAME_CONTRACT_TYPE_Consumer_loans_MEAN"])-(data["FLAG_PHONE"])))* 2.0)) -(data["FLAG_WORK_PHONE"]))))
v["i123"] = 0.049800*np.tanh(np.where(data["CLOSED_MONTHS_BALANCE_MIN_MIN"]>0, data["EXT_SOURCE_2"], np.where(np.where(data["ACTIVE_DAYS_CREDIT_MIN"] > -1, data["INSTAL_DBD_SUM"], data["CLOSED_MONTHS_BALANCE_MIN_MIN"])<0, data["ORGANIZATION_TYPE_Transport__type_3"],(-1.0*(((((((( data["CC_AMT_BALANCE_MEAN"])* 2.0)) * 2.0)) * 2.0)))))))
v["i124"] = 0.049001*np.tanh(np.where(data["BURO_CREDIT_TYPE_Microloan_MEAN"]>0, data["BURO_CREDIT_TYPE_Microloan_MEAN"], np.minimum(((((( data["NEW_SOURCES_PROD"])>(data["NEW_RATIO_BURO_DAYS_CREDIT_VAR"])) *1.))) ,(((((((data["DAYS_BIRTH"])+(data["NEW_SOURCES_PROD"])) /2.0)) *(((data["NEW_RATIO_BURO_DAYS_CREDIT_VAR"])-(data["DAYS_BIRTH"])))))))))
v["i125"] = 0.048804*np.tanh(((data["NEW_RATIO_BURO_AMT_CREDIT_SUM_MAX"])-(np.where(data["DAYS_LAST_PHONE_CHANGE"] > -1, np.where(data["PREV_DAYS_DECISION_MIN"] > -1, data["ACTIVE_AMT_CREDIT_SUM_DEBT_MAX"], data["BURO_MONTHS_BALANCE_MIN_MIN"]), np.where(data["NAME_EDUCATION_TYPE_Secondary___secondary_special"]>0,(( data["BURO_MONTHS_BALANCE_MIN_MIN"])-(data["DAYS_LAST_PHONE_CHANGE"])) , data["CC_AMT_DRAWINGS_CURRENT_VAR"])))))
v["i126"] = 0.047502*np.tanh(((( -1.0*(( np.where(data["EXT_SOURCE_3"] > -1, data["BURO_DAYS_CREDIT_MAX"],(-1.0*(( np.where(data["AMT_REQ_CREDIT_BUREAU_QRT"] > -1, data["BURO_DAYS_CREDIT_MAX"],(-1.0*(( np.where(data["NEW_RATIO_PREV_AMT_ANNUITY_MIN"] > -1, data["PREV_NAME_CLIENT_TYPE_Refreshed_MEAN"], data["PREV_RATE_DOWN_PAYMENT_MIN"])))))))))))))* 2.0))
v["i127"] = 0.049420*np.tanh(((((( data["INSTAL_DBD_SUM"])<(data["APPROVED_AMT_CREDIT_MAX"])) *1.))-(((data["OCCUPATION_TYPE_Medicine_staff"])-(np.where(data["NEW_CAR_TO_BIRTH_RATIO"] > -1, data["APPROVED_AMT_ANNUITY_MEAN"], np.maximum(((((data["POS_NAME_CONTRACT_STATUS_Returned_to_the_store_MEAN"])* 2.0))),(( data["NAME_HOUSING_TYPE_Rented_apartment"])))))))))
v["i128"] = 0.030880*np.tanh(np.where(((data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])*(( -1.0*(( data["PREV_AMT_DOWN_PAYMENT_MAX"])))))<0,(( data["FLAG_WORK_PHONE"])* 2.0), np.where(data["INSTAL_PAYMENT_PERC_SUM"]>0,(( data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])*(data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])) ,(( data["INSTAL_AMT_PAYMENT_MIN"])* 2.0))))
v["i129"] = 0.047000*np.tanh(np.where(data["PREV_AMT_DOWN_PAYMENT_MIN"]>0, data["REGION_RATING_CLIENT_W_CITY"],(( np.maximum(((data["CC_CNT_DRAWINGS_POS_CURRENT_VAR"])) ,(( np.where(data["REGION_POPULATION_RELATIVE"] > -1, np.where(data["INSTAL_PAYMENT_DIFF_MEAN"] > -1, data["NEW_EXT_SOURCES_MEAN"],(-1.0*(( data["NEW_EXT_SOURCES_MEAN"])))) , data["REGION_RATING_CLIENT_W_CITY"])))))* 2.0)))
v["i130"] = 0.048680*np.tanh(np.where(data["BURO_AMT_CREDIT_SUM_DEBT_SUM"] > -1,(( data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])-(((data["ACTIVE_AMT_CREDIT_SUM_MEAN"])*(((((((-1.0*(( data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])))) <(((( data["BURO_AMT_CREDIT_SUM_MAX"])<(data["BURO_AMT_CREDIT_SUM_DEBT_SUM"])) *1.))) *1.))* 2.0))))), data["POS_SK_DPD_DEF_MAX"]))
v["i131"] = 0.049972*np.tanh(np.where(data["OBS_60_CNT_SOCIAL_CIRCLE"]<0, np.where(data["FLAG_DOCUMENT_3"]>0, data["REGION_POPULATION_RELATIVE"], np.where(data["REGION_POPULATION_RELATIVE"]>0, data["FLAG_DOCUMENT_3"], np.where(data["AMT_INCOME_TOTAL"]>0, data["FLAG_DOCUMENT_3"],(-1.0*(( data["BURO_DAYS_CREDIT_ENDDATE_MEAN"])))))) , data["BURO_DAYS_CREDIT_ENDDATE_MEAN"]))
v["i132"] = 0.048698*np.tanh(np.where(data["APARTMENTS_MODE"]>0, data["CC_NAME_CONTRACT_STATUS_Active_SUM"], np.where(data["BURO_AMT_CREDIT_SUM_SUM"]>0, data["PREV_CODE_REJECT_REASON_LIMIT_MEAN"],(-1.0*(((( data["INSTAL_AMT_PAYMENT_SUM"])+(((data["NEW_DOC_IND_AVG"])-(((( data["INSTAL_AMT_PAYMENT_SUM"])<(data["REFUSED_AMT_GOODS_PRICE_MAX"])) *1.)))))))))))
v["i133"] = 0.049969*np.tanh(np.where(data["ORGANIZATION_TYPE_School"]>0, data["BURO_STATUS_1_MEAN_MEAN"], np.where(((data["EXT_SOURCE_1"])/ 2.0)> -1, data["BURO_AMT_CREDIT_SUM_DEBT_SUM"],(-1.0*(((( data["BURO_AMT_CREDIT_SUM_MEAN"])-(np.where(data["CLOSED_AMT_CREDIT_SUM_MAX"]>0, data["ORGANIZATION_TYPE_Self_employed"], data["NEW_LIVE_IND_STD"])))))))))
v["i134"] = 0.049501*np.tanh(np.where(data["NEW_RATIO_BURO_DAYS_CREDIT_MAX"]>0, data["PREV_NAME_YIELD_GROUP_XNA_MEAN"],(((( np.where(((data["NEW_RATIO_PREV_AMT_CREDIT_MIN"])* 2.0)>0,(( data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__high_MEAN"])* 2.0),(((data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])>(((data["BURO_AMT_CREDIT_SUM_LIMIT_SUM"])/ 2.0)))*1.))) * 2.0)) * 2.0)))
v["i135"] = 0.046770*np.tanh(np.where(data["NEW_ANNUITY_TO_INCOME_RATIO"] > -1, np.where(data["COMMONAREA_MODE"] > -1, data["OCCUPATION_TYPE_Drivers"],(((((((( data["PREV_CODE_REJECT_REASON_HC_MEAN"])+(data["PREV_PRODUCT_COMBINATION_POS_other_with_interest_MEAN"])) /2.0)) +(data["NAME_HOUSING_TYPE_Rented_apartment"])) /2.0)) * 2.0)) , data["APPROVED_AMT_APPLICATION_MAX"]))
v["i136"] = 0.047902*np.tanh(( -1.0*(((((np.maximum(((np.maximum(((np.maximum(((data["PREV_PRODUCT_COMBINATION_Cash_X_Sell__low_MEAN"])) ,(( data["PREV_NAME_GOODS_CATEGORY_Sport_and_Leisure_MEAN"]))))),(( data["PREV_NAME_GOODS_CATEGORY_Photo___Cinema_Equipment_MEAN"]))))),(( data["OCCUPATION_TYPE_High_skill_tech_staff"])))) +(np.where(data["NEW_PHONE_TO_BIRTH_RATIO"] > -1, data["EXT_SOURCE_2"], data["PREV_NAME_SELLER_INDUSTRY_XNA_MEAN"])))/2.0)))))
v["i137"] = 0.048544*np.tanh(np.where(data["ORGANIZATION_TYPE_Kindergarten"]>0, data["AMT_REQ_CREDIT_BUREAU_QRT"], np.where(data["AMT_REQ_CREDIT_BUREAU_QRT"]>0, data["ACTIVE_CREDIT_DAY_OVERDUE_MAX"],(((( np.maximum(((np.where(data["APPROVED_DAYS_DECISION_MAX"]>0, data["APPROVED_AMT_APPLICATION_MAX"], data["PREV_NAME_CLIENT_TYPE_Repeater_MEAN"]))),(( data["INSTAL_DPD_MEAN"])))) * 2.0)) * 2.0))))
v["i138"] = 0.048922*np.tanh(np.where(((( data["CC_AMT_DRAWINGS_CURRENT_VAR"])+(data["NEW_SOURCES_PROD"])) /2.0)<0, np.where(data["PREV_CODE_REJECT_REASON_SCO_MEAN"]<0,(( data["NEW_SOURCES_PROD"])-(((( data["CC_AMT_DRAWINGS_CURRENT_VAR"])+(data["BURO_AMT_CREDIT_SUM_LIMIT_MEAN"])) /2.0))), data["CC_AMT_DRAWINGS_CURRENT_VAR"]),(-1.0*(((5.22825956344604492))))))
v["i139"] = 0.047001*np.tanh(((data["NEW_EXT_SOURCES_MEAN"])-(((data["NEW_EXT_SOURCES_MEAN"])*(((((( data["EXT_SOURCE_3"])/ 2.0)) +(np.maximum(((data["NEW_SCORES_STD"])) ,(( np.maximum(((data["PREV_AMT_ANNUITY_MIN"])) ,(((( data["NEW_EXT_SOURCES_MEAN"])*(data["NEW_EXT_SOURCES_MEAN"])))))))))) /2.0))))))
v["i140"] = 0.020806*np.tanh(((np.where(data["PREV_CNT_PAYMENT_MEAN"]<0, data["EXT_SOURCE_3"],(( data["INSTAL_AMT_INSTALMENT_SUM"])*(data["PREV_CNT_PAYMENT_MEAN"])))) -(((data["ACTIVE_AMT_CREDIT_SUM_DEBT_MAX"])+(((data["INSTAL_AMT_INSTALMENT_SUM"])+(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])*(data["PREV_CNT_PAYMENT_MEAN"])))))))))
v["i141"] = 0.005614*np.tanh(np.where(data["REFUSED_AMT_GOODS_PRICE_MEAN"]>0, data["PREV_NAME_SELLER_INDUSTRY_Consumer_electronics_MEAN"], np.where(data["CC_AMT_CREDIT_LIMIT_ACTUAL_SUM"]>0, data["PREV_AMT_GOODS_PRICE_MIN"],(((data["INSTAL_AMT_PAYMENT_SUM"])<(((np.where(data["BURO_CREDIT_ACTIVE_Closed_MEAN"]>0, data["REGION_POPULATION_RELATIVE"],(( data["PREV_AMT_GOODS_PRICE_MAX"])* 2.0)))/ 2.0)))*1.))))
v["i142"] = 0.049999*np.tanh(((((((( data["INSTAL_AMT_INSTALMENT_SUM"])<(np.where(data["APPROVED_AMT_APPLICATION_MIN"]>0, data["POS_NAME_CONTRACT_STATUS_Completed_MEAN"],(-1.0*(( np.where(data["PREV_PRODUCT_COMBINATION_POS_household_without_interest_MEAN"]>0, data["APPROVED_APP_CREDIT_PERC_MIN"],(((data["PREV_NAME_GOODS_CATEGORY_Audio_Video_MEAN"])<(data["APPROVED_AMT_APPLICATION_MIN"])) *1.))))))))*1.))* 2.0)) * 2.0))
v["i143"] = 0.040040*np.tanh(np.where(data["ACTIVE_AMT_CREDIT_SUM_SUM"]>0, data["NEW_ANNUITY_TO_INCOME_RATIO"],(( np.where(data["NEW_ANNUITY_TO_INCOME_RATIO"] > -1, np.minimum(((data["HOUR_APPR_PROCESS_START"])) ,(((((-1.0*(( data["DAYS_EMPLOYED"])))) / 2.0)))) , np.maximum(((data["APPROVED_CNT_PAYMENT_SUM"])) ,(( data["PREV_CHANNEL_TYPE_AP___Cash_loan__MEAN"])))))* 2.0)))
v["i144"] = 0.049999*np.tanh(np.where(data["BURO_AMT_CREDIT_SUM_OVERDUE_MEAN"]>0, 3.141593,(( np.maximum(((data["HOUR_APPR_PROCESS_START"])) ,(((( data["BASEMENTAREA_AVG"])+(np.maximum(((data["NONLIVINGAREA_MEDI"])) ,(((( data["HOUR_APPR_PROCESS_START"])* 2.0)))))))))) *(data["WEEKDAY_APPR_PROCESS_START_SATURDAY"]))))
v["i145"] = 0.049975*np.tanh(((data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])*(np.where(data["CC_AMT_CREDIT_LIMIT_ACTUAL_MEAN"] > -1, data["ACTIVE_MONTHS_BALANCE_MIN_MIN"], np.tanh(( np.where(data["REGION_POPULATION_RELATIVE"]<0, data["CLOSED_AMT_CREDIT_SUM_SUM"], np.where(data["BURO_CREDIT_TYPE_Mortgage_MEAN"]<0,(-1.0*(( data["ACTIVE_AMT_ANNUITY_MAX"]))), data["CC_AMT_CREDIT_LIMIT_ACTUAL_MEAN"]))))))))
v["i146"] = 0.042400*np.tanh(np.where(data["PREV_PRODUCT_COMBINATION_Cash_MEAN"]>0, data["BURO_CREDIT_TYPE_Credit_card_MEAN"], np.where(( -1.0*(((((data["PREV_PRODUCT_COMBINATION_Cash_MEAN"])<(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])/ 2.0)))*1.))))> -1, data["NEW_RATIO_BURO_AMT_CREDIT_MAX_OVERDUE_MEAN"],(((data["NAME_INCOME_TYPE_State_servant"])>(((data["NEW_CREDIT_TO_ANNUITY_RATIO"])/ 2.0)))*1.))))
v["i147"] = 0.049749*np.tanh(np.where(np.maximum(((((data["APPROVED_DAYS_DECISION_MIN"])*(((( data["INSTAL_DPD_MEAN"])>(data["NAME_EDUCATION_TYPE_Lower_secondary"])) *1.))))) ,(( data["APARTMENTS_MEDI"])))>0,(((data["REFUSED_AMT_ANNUITY_MEAN"])<(data["INSTAL_DPD_MEAN"])) *1.) ,(((( data["NAME_EDUCATION_TYPE_Lower_secondary"])* 2.0)) * 2.0)))
v["i148"] = 0.049970*np.tanh(np.where(data["INSTAL_DPD_SUM"]>0, data["PREV_APP_CREDIT_PERC_MIN"], np.where(data["CC_AMT_PAYMENT_TOTAL_CURRENT_MAX"]<0,(((((data["ACTIVE_DAYS_CREDIT_ENDDATE_MIN"])>(np.maximum(((data["ORGANIZATION_TYPE_Construction"])) ,(( data["ORGANIZATION_TYPE_Construction"])))))*1.))* 2.0),(( data["PREV_NAME_TYPE_SUITE_Family_MEAN"])-(data["DAYS_LAST_PHONE_CHANGE"])))))
v["i149"] = 0.048725*np.tanh(np.where(data["NEW_INC_PER_CHLD"]<0, np.minimum(((data["REGION_POPULATION_RELATIVE"])) ,(((( data["BURO_STATUS_X_MEAN_MEAN"])*(((data["NAME_FAMILY_STATUS_Separated"])* 2.0)))))) , np.where(data["CLOSED_DAYS_CREDIT_VAR"]<0, data["NAME_FAMILY_STATUS_Separated"],(( data["NEW_EMPLOY_TO_BIRTH_RATIO"])*(data["BURO_STATUS_X_MEAN_MEAN"])))))
return Output(v.sum(axis=1)) | Home Credit Default Risk |
1,443,616 | class SigmoidNeuron:
def __init__(self):
self.w = None
self.b = None
def perceptron(self, x):
return np.dot(x, self.w.T)+ self.b
def sigmoid(self, x):
return 1.0/(1.0 + np.exp(-x))
def grad_w_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)* x
def grad_b_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)
def grad_w_ce(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
if y == 0:
return y_pred * x
elif y == 1:
return -1 *(1 - y_pred)* x
else:
raise ValueError("y should be 0 or 1")
def grad_b_ce(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
if y == 0:
return y_pred
elif y == 1:
return -1 *(1 - y_pred)
else:
raise ValueError("y should be 0 or 1")
def fit(self, X, Y, epochs=1, learning_rate=1, initialise=True, loss_fn="mse", display_loss=False):
if initialise:
self.w = np.random.randn(1, X.shape[1])
self.b = 0
if display_loss:
loss = {}
for i in tqdm_notebook(range(epochs), total=epochs, unit="epoch"):
dw = 0
db = 0
for x, y in zip(X, Y):
if loss_fn == "mse":
dw += self.grad_w_mse(x, y)
db += self.grad_b_mse(x, y)
elif loss_fn == "ce":
dw += self.grad_w_ce(x, y)
db += self.grad_b_ce(x, y)
self.w -= learning_rate * dw
self.b -= learning_rate * db
if display_loss:
Y_pred = self.sigmoid(self.perceptron(X))
if loss_fn == "mse":
loss[i] = mean_squared_error(Y, Y_pred)
elif loss_fn == "ce":
loss[i] = log_loss(Y, Y_pred)
if display_loss:
plt.plot(loss.values())
plt.xlabel('Epochs')
if loss_fn == "mse":
plt.ylabel('Mean Squared Error')
elif loss_fn == "ce":
plt.ylabel('Log Loss')
plt.show()
def predict(self, X):
Y_pred = []
for x in X:
y_pred = self.sigmoid(self.perceptron(x))
Y_pred.append(y_pred)
return np.array(Y_pred )<load_pretrained> | roc_auc_score(train_df.TARGET,GP1(train_df)) | Home Credit Default Risk |
1,443,616 | languages = ['ta', 'hi', 'en']
images_train = read_all(".. /input/level_4b_train/"+LEVEL+"/"+"background", key_prefix='bgr_')
for language in languages:
images_train.update(read_all(".. /input/level_4b_train/"+LEVEL+"/"+language, key_prefix=language+"_"))
print(len(images_train))
images_test = read_all(".. /input/level_4b_test/kaggle_"+LEVEL, key_prefix='')
print(len(images_test))<normalization> | roc_auc_score(train_df.TARGET,GP2(train_df)) | Home Credit Default Risk |
1,443,616 | <compute_test_metric><EOS> | x = test_df[['SK_ID_CURR']].copy()
x['TARGET'] =.5*GP1(test_df)+.5*GP2(test_df)
x.to_csv('pure_submission.csv', index = False ) | Home Credit Default Risk |
1,136,016 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<train_model> | pd.set_option("display.max_columns",100)
%matplotlib inline
py.init_notebook_mode(connected=True)
print(os.listdir(".. /input"))
| Home Credit Default Risk |
1,136,016 |
<train_model> | df_train = pd.read_csv('.. /input/application_train.csv')
df_train.head() | Home Credit Default Risk |
1,136,016 | sn_ce = SigmoidNeuronMy()
sn_ce.fit(X_scaled_train, Y_train, epochs=800, learning_rate=0.00005, loss_fn="ce", display_loss=True)
<predict_on_test> | df_test = pd.read_csv('.. /input/application_test.csv')
| Home Credit Default Risk |
1,136,016 | def print_accuracy(sn):
Y_pred_train = sn.predict(X_scaled_train)
Y_pred_binarised_train =(Y_pred_train >= 0.5 ).astype("int" ).ravel()
accuracy_train = accuracy_score(Y_pred_binarised_train, Y_train)
print("Train Accuracy : ", accuracy_train)
print("-"*50 )<compute_test_metric> | y = df_train['TARGET']
X = df_train.drop(['TARGET'], axis=1)
X.head() | Home Credit Default Risk |
1,136,016 | print_accuracy(sn_ce )<save_to_csv> | print(X.shape)
X = pd.concat([X, df_test], axis=0)
X.shape | Home Credit Default Risk |
1,136,016 | Y_pred_test = sn_ce.predict(X_scaled_test)
Y_pred_binarised_test =(Y_pred_test >= 0.5 ).astype("int" ).ravel()
submission = {}
submission['ImageId'] = ID_test
submission['Class'] = Y_pred_binarised_test
submission = pd.DataFrame(submission)
submission = submission[['ImageId', 'Class']]
submission = submission.sort_values(['ImageId'])
submission.to_csv("submisision.csv", index=False )<set_options> | cat_X = X.drop(['SK_ID_CURR'], axis=1)
cat_X = [col for col in X.columns if X[col].dtype == 'object']
SK_ID = X['SK_ID_CURR']
cat_X = X[cat_X]
cat_X.head() | Home Credit Default Risk |
1,136,016 | np.random.seed(100)
LEVEL = 'level_4b'
warnings.filterwarnings("ignore" )<compute_test_metric> | ncat_X = ncat_X.fillna(0)
| Home Credit Default Risk |
1,136,016 | class SigmoidNeuron:
def __init__(self):
self.w = None
self.b = None
def perceptron(self, x):
return np.dot(x, self.w.T)+ self.b
def sigmoid(self, x):
return 1.0/(1.0 + np.exp(-x))
def grad_w_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)* x
def grad_b_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)
def grad_w_ce(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
if y == 0:
return y_pred * x
elif y == 1:
return -1 *(1 - y_pred)* x
else:
raise ValueError("y should be 0 or 1")
def grad_b_ce(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
if y == 0:
return y_pred
elif y == 1:
return -1 *(1 - y_pred)
else:
raise ValueError("y should be 0 or 1")
def fit(self, X, Y, epochs=1, learning_rate=1, initialise=True, loss_fn="mse", display_loss=False):
if initialise or self.w is None:
self.w = np.random.randn(1, X.shape[1])
self.b = 0
if display_loss:
loss = {}
for i in tqdm_notebook(range(epochs), total=epochs, unit="epoch"):
dw = 0
db = 0
for x, y in zip(X, Y):
if loss_fn == "mse":
dw += self.grad_w_mse(x, y)
db += self.grad_b_mse(x, y)
elif loss_fn == "ce":
dw += self.grad_w_ce(x, y)
db += self.grad_b_ce(x, y)
self.w -= learning_rate * dw
self.b -= learning_rate * db
if display_loss:
Y_pred = self.sigmoid(self.perceptron(X))
if loss_fn == "mse":
loss[i] = mean_squared_error(Y, Y_pred)
elif loss_fn == "ce":
loss[i] = log_loss(Y, Y_pred)
if display_loss:
plt.plot(loss.values())
plt.xlabel('Epochs')
if loss_fn == "mse":
plt.ylabel('Mean Squared Error')
elif loss_fn == "ce":
plt.ylabel('Log Loss')
plt.show()
def predict(self, X):
Y_pred = []
for x in X:
y_pred = self.sigmoid(self.perceptron(x))
Y_pred.append(y_pred)
return np.array(Y_pred )<import_modules> | ncat_X = pd.DataFrame(ncat_X, columns=cols_ncat_X)
ncat_X['SK_ID_CURR'] = SK_ID.values
ncat_X.head(10 ) | Home Credit Default Risk |
1,136,016 |
<load_pretrained> | ncat_X['PERC_INCOME'] = ncat_X.AMT_CREDIT/ncat_X.AMT_INCOME_TOTAL
ncat_X.head() | Home Credit Default Risk |
1,136,016 | languages = ['ta', 'hi', 'en']
images_train = read_all(".. /input/level_4b_train/"+LEVEL+"/"+"background", key_prefix='bgr_')
for language in languages:
images_train.update(read_all(".. /input/level_4b_train/"+LEVEL+"/"+language, key_prefix=language+"_"))
print(len(images_train))
images_test = read_all(".. /input/level_4b_test/kaggle_"+LEVEL, key_prefix='')
print(len(images_test))<normalization> | ncat_X['GOODS_BIGGER_CREDIT'] = np.where(ncat_X.AMT_GOODS_PRICE > ncat_X.AMT_CREDIT, 1, 0)
ncat_X['AMOUNT_NOT_CREDIT'] = ncat_X.AMT_CREDIT - ncat_X.AMT_GOODS_PRICE
ncat_X.head(20 ) | Home Credit Default Risk |
1,136,016 | scaler = StandardScaler()
X_scaled_train = scaler.fit_transform(X_train)
X_scaled_test = scaler.transform(X_test )<choose_model_class> | ncat_X['AMT_INCOME_TOTAL'] = np.log(ncat_X.AMT_INCOME_TOTAL ) | Home Credit Default Risk |
1,136,016 | kfold = KFold(5, True, 10)
<find_best_model_class> | ncat_X['AMT_CREDIT'] = np.log(ncat_X.AMT_CREDIT ) | Home Credit Default Risk |
1,136,016 | sn_ce = SigmoidNeuron()
for train, test in kfold.split(X_scaled_train,Y_train):
sn_ce.fit(X_scaled_train[train], Y_train[train], epochs=500, learning_rate=0.00002, loss_fn="ce", display_loss=True,initialise=False)
Y_pred_train = sn_ce.predict(X_scaled_train[test])
Y_pred_binarised_train =(Y_pred_train >= 0.5 ).astype("int" ).ravel()
accuracy_train = accuracy_score(Y_pred_binarised_train, Y_train[test])
print("Train Accuracy : ", accuracy_train)
print("-"*50)
<predict_on_test> | ncat_X['AMT_ANNUITY'] = np.log(ncat_X.AMT_ANNUITY ) | Home Credit Default Risk |
1,136,016 | def print_accuracy(sn):
Y_pred_train = sn.predict(X_scaled_train)
Y_pred_binarised_train =(Y_pred_train >= 0.5 ).astype("int" ).ravel()
accuracy_train = accuracy_score(Y_pred_binarised_train, Y_train)
print("Train Accuracy : ", accuracy_train)
print("-"*50 )<compute_test_metric> | ncat_X['AMT_GOODS_PRICE'] = np.log(ncat_X.AMT_GOODS_PRICE ) | Home Credit Default Risk |
1,136,016 | print_accuracy(sn_ce )<save_to_csv> | ncat_X['DAYS_BIRTH'] = ncat_X.DAYS_BIRTH.apply(lambda x: x/-365)
ncat_X['DAYS_EMPLOYED'] = ncat_X.DAYS_EMPLOYED.apply(lambda x: x/-365)
ncat_X['DAYS_REGISTRATION'] = ncat_X.DAYS_REGISTRATION.apply(lambda x: x/-365)
ncat_X['DAYS_ID_PUBLISH'] = ncat_X.DAYS_ID_PUBLISH.apply(lambda x: x/-365)
ncat_X['DAYS_LAST_PHONE_CHANGE'] = ncat_X.DAYS_LAST_PHONE_CHANGE.apply(lambda x: x/-365)
ncat_X.head() | Home Credit Default Risk |
1,136,016 | Y_pred_test = sn_ce.predict(X_scaled_test)
Y_pred_binarised_test =(Y_pred_test >= 0.5 ).astype("int" ).ravel()
submission = {}
submission['ImageId'] = ID_test
submission['Class'] = Y_pred_binarised_test
submission = pd.DataFrame(submission)
submission = submission[['ImageId', 'Class']]
submission = submission.sort_values(['ImageId'])
submission.to_csv("submisision.csv", index=False )<set_options> |
for x in cat_X.columns.values:
print(x)
keys = cat_X[x].unique()
dicts = dict(zip(keys, range(len(keys))))
cat_X[x] = cat_X[x].map(dicts ).astype(int)
cat_X.head() | Home Credit Default Risk |
1,136,016 | warnings.simplefilter("ignore")
np.random.seed(100)
LEVEL = 'level_4b'<compute_test_metric> | cat_X = pd.concat([SK_ID, cat_X], axis=1 ) | Home Credit Default Risk |
1,136,016 | class SigmoidNeuron:
def __init__(self):
self.w = None
self.b = None
def perceptron(self, x):
return np.dot(x, self.w.T)+ self.b
def sigmoid(self, x):
return 1.0/(1.0 + np.exp(-x))
def grad_w_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)* x
def grad_b_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)
def grad_w_ce(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
if y == 0:
return y_pred * x
elif y == 1:
return -1 *(1 - y_pred)* x
else:
raise ValueError("y should be 0 or 1")
def grad_b_ce(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
if y == 0:
return y_pred
elif y == 1:
return -1 *(1 - y_pred)
else:
raise ValueError("y should be 0 or 1")
def fit(self, X, Y, epochs=1, learning_rate=1, initialise=True, loss_fn="mse", display_loss=False):
if initialise:
self.w = X.mean(axis=0)
self.b = 0
if display_loss:
loss = {}
for i in tqdm_notebook(range(epochs), total=epochs, unit="epoch"):
dw = 0
db = 0
for x, y in zip(X, Y):
if loss_fn == "mse":
dw += self.grad_w_mse(x, y)
db += self.grad_b_mse(x, y)
self.w -= learning_rate * dw
self.b -= learning_rate * db
elif loss_fn == "ce":
dw += self.grad_w_ce(x, y)
db += self.grad_b_ce(x, y)
self.w -= learning_rate * dw
self.b -= learning_rate * db
if display_loss:
Y_pred = self.sigmoid(self.perceptron(X))
if loss_fn == "mse":
loss[i] = mean_squared_error(Y, Y_pred)
flag = loss[i]
elif loss_fn == "ce":
loss[i] = log_loss(Y, Y_pred)
flag = loss[i]
if display_loss:
plt.plot(loss.values())
plt.xlabel('Epochs')
if loss_fn == "mse":
plt.ylabel('Mean Squared Error')
elif loss_fn == "ce":
plt.ylabel('Log Loss')
plt.show()
min_key = min(loss, key=loss.get)
print(min_key)
print(loss.get(min_key))
print(i)
def predict(self, X):
Y_pred = []
for x in X:
y_pred = self.sigmoid(self.perceptron(x))
Y_pred.append(y_pred)
return np.array(Y_pred )<load_pretrained> | X = ncat_X.merge(cat_X, how='left', on='SK_ID_CURR' ) | Home Credit Default Risk |
1,136,016 | languages = ['en','ta', 'hi']
images_train = read_all(".. /input/"+LEVEL+"_train/"+LEVEL+"/"+"background", key_prefix='bgr_')
for language in languages:
images_train.update(read_all(".. /input/"+LEVEL+"_train/"+LEVEL+"/"+language, key_prefix=language+"_"))
print(len(images_train))
images_test = read_all(".. /input/"+LEVEL+"_test/kaggle_"+LEVEL, key_prefix='')
print(len(images_test))<define_variables> | gc.enable()
del ncat_X
del cat_X
gc.collect() | Home Credit Default Risk |
1,136,016 | X_train = []
Y_train = []
ID_train = []
for key, value in images_train.items() :
X_train.append(value)
ID_train.append(key)
if key[:4] == "bgr_":
Y_train.append(0)
else:
Y_train.append(1)
ID_test = []
X_test = []
for key, value in images_test.items() :
ID_test.append(int(key))
X_test.append(value)
X_train = np.array(X_train)
Y_train = np.array(Y_train)
X_test = np.array(X_test)
print(X_train.shape, Y_train.shape)
print(X_test.shape )<normalization> | credit_card_balance = pd.read_csv('.. /input/credit_card_balance.csv')
credit_card_balance.head() | Home Credit Default Risk |
1,136,016 | scaler = StandardScaler()
X_scaled_train = scaler.fit_transform(X_train)
X_scaled_test = scaler.transform(X_test )<train_model> | credit_card_balance['NAME_CONTRACT_STATUS'] = pd.get_dummies(credit_card_balance['NAME_CONTRACT_STATUS'])
credit_card_balance = credit_card_balance.fillna(0)
credit_card_balance.head(10 ) | Home Credit Default Risk |
1,136,016 | sn_ce = SigmoidNeuron()
sn_ce.fit(X_scaled_train, Y_train, epochs=100, learning_rate=1,initialise=True,loss_fn="ce",display_loss=True)
sn_ce.fit(X_scaled_train, Y_train, epochs=200, learning_rate=0.1,initialise=False,loss_fn="ce",display_loss=True)
sn_ce.fit(X_scaled_train, Y_train, epochs=200, learning_rate=0.01,initialise=False,loss_fn="ce" ,display_loss=True)
sn_ce.fit(X_scaled_train, Y_train, epochs=300, learning_rate=0.001,initialise=False,loss_fn="ce",display_loss=True)
sn_ce.fit(X_scaled_train, Y_train, epochs=500, learning_rate=0.0001,initialise=False,loss_fn="ce",display_loss=True)
sn_ce.fit(X_scaled_train, Y_train, epochs=800, learning_rate=0.00001,initialise=False,loss_fn="ce",display_loss=True)
sn_ce.fit(X_scaled_train, Y_train, epochs=1000, learning_rate=0.000005,initialise=False,loss_fn="ce",display_loss=True )<predict_on_test> | ID_PREV = credit_card_balance[['SK_ID_CURR', 'SK_ID_PREV']].groupby('SK_ID_CURR' ).count()
credit_card_balance['SK_ID_PREV'] = credit_card_balance['SK_ID_CURR'].map(ID_PREV['SK_ID_PREV'] ) | Home Credit Default Risk |
1,136,016 | def print_accuracy(sn):
Y_pred_train = sn.predict(X_scaled_train)
Y_pred_binarised_train =(Y_pred_train >= 0.5 ).astype("int" ).ravel()
accuracy_train = accuracy_score(Y_pred_binarised_train, Y_train)
print("Train Accuracy : ", accuracy_train)
print("-"*50)
mismatch_name = []
for x,y,z in zip(Y_pred_binarised_train, Y_train, ID_train):
if(x!=y):
mismatch_name.append(z)
print(mismatch_name )<compute_test_metric> | credit_card_mean = credit_card_balance.groupby('SK_ID_CURR' ).mean()
credit_card_mean.columns = ['cc_' + col for col in credit_card_mean.columns]
credit_card_mean = credit_card_mean.reset_index()
credit_card_mean.head() | Home Credit Default Risk |
1,136,016 | print_accuracy(sn_ce )<save_to_csv> | X = X.merge(right=credit_card_mean, how='left', on='SK_ID_CURR')
X.head(10 ) | Home Credit Default Risk |
1,136,016 | Y_pred_test = sn_ce.predict(X_scaled_test)
Y_pred_binarised_test =(Y_pred_test >= 0.5 ).astype("int" ).ravel()
submission = {}
submission['ImageId'] = ID_test
submission['Class'] = Y_pred_binarised_test
submission = pd.DataFrame(submission)
submission = submission[['ImageId', 'Class']]
submission = submission.sort_values(['ImageId'])
submission.to_csv("submisision.csv", index=False )<compute_test_metric> | gc.enable()
del credit_card_balance
del credit_card_mean
gc.collect() | Home Credit Default Risk |
1,136,016 | class SigmoidNeuron:
def __init__(self):
self.w = None
self.b = None
self.best_epoch = 0
def perceptron(self, x):
return np.dot(x, self.w.T)+ self.b
def sigmoid(self, x):
return 1.0/(1.0 + np.exp(-x))
def grad_w_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)* x
def grad_b_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)
def grad_w_ce(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
if y == 0:
return y_pred * x
elif y == 1:
return -1 *(1 - y_pred)* x
else:
raise ValueError("y should be 0 or 1")
def grad_b_ce(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
if y == 0:
return y_pred
elif y == 1:
return -1 *(1 - y_pred)
else:
raise ValueError("y should be 0 or 1")
def fit(self, X, Y, epochs=1, learning_rate=1, initialise=True, loss_fn="mse", display_loss=False):
if initialise:
self.w = np.random.randn(1, X.shape[1])
self.b = 0
loss = {}
best_w = self.w.copy()
best_b = 0
best_loss = 999
best_epoch = 1
for i in tqdm_notebook(range(epochs), total=epochs, unit="epoch"):
dw = 0
db = 0
for x, y in zip(X, Y):
if loss_fn == "mse":
dw += self.grad_w_mse(x, y)
db += self.grad_b_mse(x, y)
elif loss_fn == "ce":
dw += self.grad_w_ce(x, y)
db += self.grad_b_ce(x, y)
self.w -= learning_rate * dw
self.b -= learning_rate * db
Y_pred = self.sigmoid(self.perceptron(X))
if loss_fn == "mse":
loss[i] = mean_squared_error(Y, Y_pred)
elif loss_fn == "ce":
loss[i] = log_loss(Y, Y_pred)
if best_loss == 999:
best_loss = loss[i]
best_w = self.w.copy()
best_b = self.b
best_epoch = i
elif loss[i] < best_loss:
best_loss = loss[i]
best_w = self.w.copy()
best_b = self.b
best_epoch = i
self.w = best_w.copy()
self.b = best_b
self.best_epoch = best_epoch
if display_loss:
plt.plot(loss.values())
plt.xlabel('Epochs')
if loss_fn == "mse":
plt.ylabel('Mean Squared Error')
elif loss_fn == "ce":
plt.ylabel('Log Loss')
plt.show()
def predict(self, X):
Y_pred = []
for x in X:
y_pred = self.sigmoid(self.perceptron(x))
Y_pred.append(y_pred)
return np.array(Y_pred )<define_search_space> | POS_CASH_balance = pd.read_csv('.. /input/POS_CASH_balance.csv')
POS_CASH_balance.head() | Home Credit Default Risk |
1,136,016 | a = np.array([1,2,3])
c = list(a)
print(a)
print(c)
b = [(lambda x: 0 if x < 2 else 255 )(x)for x in c]
print(b )<load_pretrained> | POS_CASH_balance['NAME_CONTRACT_STATUS'] = pd.get_dummies(POS_CASH_balance['NAME_CONTRACT_STATUS'])
POS_CASH_balance = POS_CASH_balance.fillna(0 ) | Home Credit Default Risk |
1,136,016 | languages = ['ta', 'hi', 'en']
images_train = read_all(".. /input/level_4b_train/level_4b/" + "background/", key_prefix='bgr_')
for language in languages:
images_train.update(read_all(".. /input/level_4b_train/level_4b/"+language, key_prefix=language+"_"))
print(len(images_train))
images_test = read_all(".. /input/level_4b_test/kaggle_level_4b/", key_prefix='')
print(len(images_test))<categorify> | pcb_count = POS_CASH_balance[['SK_ID_CURR', 'SK_ID_PREV']].groupby('SK_ID_CURR' ).count()
POS_CASH_balance['SK_ID_PREV'] = POS_CASH_balance['SK_ID_CURR'].map(pcb_count['SK_ID_PREV'] ) | Home Credit Default Risk |
1,136,016 | a = np.array(( [1,2],[3,4]), dtype = int)
print(a)
b = a.flatten()
print(b)
c = b.reshape(2,-1)
print(c)
<normalization> | pos_cash_mean = POS_CASH_balance.groupby('SK_ID_CURR' ).mean()
pos_cash_mean.columns = ['pc_' + col for col in pos_cash_mean.columns]
pos_cash_mean = pos_cash_mean.reset_index()
pos_cash_mean.head() | Home Credit Default Risk |
1,136,016 | scaler = StandardScaler()
X_scaled_train = scaler.fit_transform(X_train)
X_scaled_test = scaler.transform(X_test)
<train_model> | X = X.merge(right=pos_cash_mean, how='left', on='SK_ID_CURR')
X.shape | Home Credit Default Risk |
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