kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
1,316,642 | train = pd.read_csv('.. /input/train.csv')
test = pd.read_csv('.. /input/test.csv' )<count_missing_values> | gc.enable()
del app_train, app_test, app_train_domain, app_test_domain, bureau, bureau_balance, bureau_agg, bureau_balance_agg, bureau_balance_counts, bureau_by_loan, bureau_balance_by_client
gc.collect() | Home Credit Default Risk |
1,316,642 | train.isnull().sum()<count_missing_values> | def train_with_cv(train_data, test_data, n_folds, seed_varying):
train_ids = train_data['SK_ID_CURR']
test_ids = test_data['SK_ID_CURR']
train_labels = train_data['TARGET']
train_features = train_data.drop(columns = ['SK_ID_CURR', 'TARGET'])
test_features = test_data.drop(columns = ['SK_ID_CURR'])
feature_names = lis... | Home Credit Default Risk |
1,316,642 | test.isnull().sum()<count_missing_values> | train_times = 3
n_folds = 5
i = 0
metrics_all = np.zeros(( train_times, 2))
for seed_varying in range(train_times):
print('
=======================================================')
print('The ', seed_varying, ' time of train')
print('
=======================================================')
sub, fi, metrics = trai... | Home Credit Default Risk |
1,096,369 | test.isnull().sum()<count_values> | data1 = pd.read_csv('.. /input/lightgbm-with-simple-features-0-785-lb/submission_kernel00.csv')
data2 = pd.read_csv('.. /input/tidy-xgb-all-tables-0-782/tidy_xgb_0.77821.csv' ) | Home Credit Default Risk |
1,096,369 | train.Outcome.value_counts()<prepare_x_and_y> | data1['TARGET'] =(data1['TARGET']+data2['TARGET'])/2 | Home Credit Default Risk |
1,096,369 | <train_model><EOS> | data1.to_csv('blend1_.788lb.csv',index = False ) | Home Credit Default Risk |
1,078,102 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<predict_on_test> | import gc
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.preprocessing import StandardScaler,LabelEncoder
from sklearn.metrics import roc_auc_score | Home Credit Default Risk |
1,078,102 | predicted = clf.predict(test )<save_to_csv> | def add_noise(series, noise_level):
return series *(1 + noise_level * np.random.randn(len(series)))
def target_encode(trn_series=None,
tst_series=None,
target=None,
min_samples_leaf=1,
smoothing=1,
noise_level=0):
assert len(trn_series)== len(target)
assert trn_series.name == tst_series.name
temp = pd.concat([trn_s... | Home Credit Default Risk |
1,078,102 | output = pd.DataFrame(predicted,columns = ['Outcome'])
test = pd.read_csv('.. /input/test.csv')
output['Id'] = test['Id']
output[['Id','Outcome']].to_csv('submission_cloudy10.csv', index = False)
output.head()<import_modules> | def UseGPFeatures(data):
v = pd.DataFrame()
v["i0"] = np.tanh(((((( -1.0*(((( np.maximum(((data["EXT_SOURCE_2"])) ,(( data["EXT_SOURCE_3"])))) -(data["te_OCCUPATION_TYPE"])))))) -(((((((data["EXT_SOURCE_2"])+(data["EXT_SOURCE_3"])))* 2.0)) +(data["EXT_SOURCE_3"])))))* 2.0))
v["i1"] = np.tanh(((((data["te_OCCUPATION_TYP... | Home Credit Default Risk |
1,078,102 | import gc
import os
from pathlib import Path
import sys
import collections
import pandas as pd
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqdm
import joblib
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.model_selection import train_tes... | gc.enable()
buro_bal = pd.read_csv('.. /input/bureau_balance.csv')
print('Buro bal shape : ', buro_bal.shape)
print('transform to dummies')
buro_bal = pd.concat([buro_bal, pd.get_dummies(buro_bal.STATUS, prefix='buro_bal_status')], axis=1 ).drop('STATUS', axis=1)
print('Counting buros')
buro_counts = buro_bal[['SK... | Home Credit Default Risk |
1,078,102 | %matplotlib inline
plt.rcParams["figure.figsize"] =(15, 5)
pd.options.display.max_columns = 50<define_variables> | train.columns = train.columns.str.replace('[^A-Za-z0-9_]', '_')
test.columns = test.columns.str.replace('[^A-Za-z0-9_]', '_' ) | Home Credit Default Risk |
1,078,102 | VERSION = "1.6.6"<define_variables> | floattypes = []
inttypes = []
stringtypes = []
for c in test.columns:
if(train[c].dtype=='object'):
train[c] = train[c].astype('str')
stringtypes.append(c)
elif(train[c].dtype=='int64'):
train[c] = train[c].astype('int32')
inttypes.append(c)
else:
train[c] = train[c].astype('float32')
floattypes.append(c)
train =... | Home Credit Default Risk |
1,078,102 | IS_KAGGLE = "KAGGLE_URL_BASE" in os.environ
print(f"IS_KAGGLE: {IS_KAGGLE}")
USE_GPU = "NVIDIA_VISIBLE_DEVICES" in os.environ
print(f"USE_GPU: {USE_GPU}")
USE_CACHE = False<load_from_csv> | kf = KFold(n_splits=5, shuffle=True, random_state=42)
for col in stringtypes:
train['te_'+col] = 0.
test['te_'+col] = 0.
SMOOTHING = test[~test[col].isin(train[col])].shape[0]/test.shape[0]
for f,(vis_index, blind_index)in enumerate(kf.split(train)) :
_, train.loc[blind_index, 'te_'+col] = target_encode(train.loc[vi... | Home Credit Default Risk |
1,078,102 | train = pd.read_csv(".. /input/exam-for-students20200129/train.csv", index_col=0, na_values="" ).pipe(reduce_mem_usage)
X_test = pd.read_csv(".. /input/exam-for-students20200129/test.csv", index_col=0, na_values="" ).pipe(reduce_mem_usage )<load_from_csv> | kf = KFold(n_splits=5, shuffle=True, random_state=42)
for col in inttypes:
train['te_'+col] = 0.
test['te_'+col] = 0.
SMOOTHING = test[~test[col].isin(train[col])].shape[0]/test.shape[0]
for f,(vis_index, blind_index)in enumerate(kf.split(train)) :
_, train.loc[blind_index, 'te_'+col] = target_encode(train.loc[vis_i... | Home Credit Default Risk |
1,078,102 | df_country = pd.read_csv(".. /input/exam-for-students20200129/country_info.csv", decimal="," ).pipe(reduce_mem_usage )<data_type_conversions> | ntrainrows = train.shape[0]
test.insert(1,'TARGET',-1)
alldata = pd.concat([train,test])
del train ,test
gc.collect() | Home Credit Default Risk |
1,078,102 | df_country["GDP($ per capita)"] = df_country["GDP($ per capita)"].astype(np.float32 )<merge> | alldata['nans'] = alldata.isnull().sum(axis=1 ) | Home Credit Default Risk |
1,078,102 | train = train.reset_index().merge(df_country, how="left", on="Country" ).set_index("Respondent")
X_test = X_test.reset_index().merge(df_country, how="left", on="Country" ).set_index("Respondent" )<prepare_x_and_y> | for col in inttypes[1:]:
x = alldata[col].value_counts().reset_index(drop=False)
x.columns = [col,'cnt_'+col]
x['cnt_'+col]/=alldata.shape[0]
alldata = alldata.merge(x,on=col,how='left' ) | Home Credit Default Risk |
1,078,102 | X_train = train.drop(columns="ConvertedSalary")
y_train = np.log1p(train.ConvertedSalary )<define_variables> | alldata[features] = alldata[features].astype('float32' ) | Home Credit Default Risk |
1,078,102 | split_cols = [
"DevType",
"CommunicationTools",
"FrameworkWorkedWith",
"AdsActions",
"ErgonomicDevices",
"Gender",
"SexualOrientation",
"RaceEthnicity"
]<concatenate> | for c in features:
ss = StandardScaler()
alldata.loc[~alldata[c].isnull() ,c] = ss.fit_transform(alldata.loc[~alldata[c].isnull() ,c].values.reshape(-1,1))
alldata[c].fillna(alldata[c].mean() ,inplace=True ) | Home Credit Default Risk |
1,078,102 | def flatten(l):
for el in l:
if isinstance(el, collections.abc.Iterable)and not isinstance(el,(str, bytes)) :
yield from flatten(el)
else:
yield el<data_type_conversions> | train = alldata[:ntrainrows]
test = alldata[ntrainrows:] | Home Credit Default Risk |
1,078,102 | def split_text(df):
for col in split_cols:
categories = list(set(flatten(X_train[col].str.split(";" ).tolist())))
categories = [i for i in categories if str(i)!= 'nan']
for category in categories:
df[f"{col}_{category}"] = df[col].str.contains(category ).astype(np.float32)
df[f"count_{col}"] = df[col].str.count(";")
... | traintargets = train.TARGET.values
train = UseGPFeatures(train)
test = UseGPFeatures(test)
train['TARGET'] = traintargets | Home Credit Default Risk |
1,078,102 | X_train = split_text(X_train)
X_test = split_text(X_test )<rename_columns> | gc.enable() | Home Credit Default Risk |
1,078,102 | X_train.columns = ["".join(c if c.isalnum() else "_" for c in str(x)) for x in X_train.columns]
X_test.columns = ["".join(c if c.isalnum() else "_" for c in str(x)) for x in X_test.columns]<categorify> | folds = KFold(n_splits=5, shuffle=True, random_state=42)
oof_preds = np.zeros(train.shape[0])
sub_preds = np.zeros(test.shape[0])
feats = [f for f in train.columns if f not in ['SK_ID_CURR','TARGET']] | Home Credit Default Risk |
1,078,102 | class BaseTransformer(BaseEstimator, TransformerMixin):
def __init__(self):
pass
def fit(self, X, y=None):
return self
def transform(self, X):
return self
def get_feature_names(self):
pass
class KFoldTargetEncoder(BaseTransformer):
def __init__(self, cols=None,
n_splits=5, random_state=24, shuffle=True, **kwargs):
su... | for n_fold,(trn_idx, val_idx)in enumerate(folds.split(train)) :
trn_x, trn_y = train[feats].iloc[trn_idx], train.iloc[trn_idx]['TARGET']
val_x, val_y = train[feats].iloc[val_idx], train.iloc[val_idx]['TARGET']
clf = LGBMClassifier(
n_estimators=4000,
learning_rate=0.03,
num_leaves=30,
colsample_bytree=.8,
subsample=.9... | Home Credit Default Risk |
1,078,102 | feature_union1 = FeatureUnion([
("te", KFoldTargetEncoder(
cols=object_cols,
smoothing=.8
)) ,
], n_jobs=None, verbose=True )<categorify> | Submission = pd.DataFrame({ 'SK_ID_CURR': ID,'TARGET': sub_preds })
Submission.to_csv("hybridII.csv", index=False ) | Home Credit Default Risk |
1,068,284 | X_train1 = feature_union1.fit_transform(X_train, y_train)
X_test1 = feature_union1.transform(X_test )<categorify> | PATH = ".. /input"
list_of_files = os.listdir(PATH)
application_train = pd.read_csv(PATH+"/application_train.csv")
application_test = pd.read_csv(PATH+"/application_test.csv")
bureau = pd.read_csv(PATH+"/bureau.csv")
bureau_balance = pd.read_csv(PATH+"/bureau_balance.csv")
credit_card_balance = pd.read_csv(PATH+"/... | Home Credit Default Risk |
1,068,284 | oe = ce.OrdinalEncoder(cols=object_cols )<normalization> | total_IDS = np.concatenate(( application_test["SK_ID_CURR"].values, application_train["SK_ID_CURR"].values))
print(len(np.unique(np.array(total_IDS)))== len(total_IDS)) | Home Credit Default Risk |
1,068,284 | X_train = oe.fit_transform(X_train, y_train)
X_test = oe.transform(X_test )<concatenate> | POS_CASH_balance_IDS = POS_CASH_balance["SK_ID_CURR"].values
bureau_IDS = bureau["SK_ID_CURR"].values
credit_card_balance_IDS = credit_card_balance["SK_ID_CURR"].values
installments_payments_IDS = installments_payments["SK_ID_CURR"].values
previous_application_IDS = previous_application["SK_ID_CURR"].values
tot = len(t... | Home Credit Default Risk |
1,068,284 | X_train = np.hstack([X_train, X_train1])
X_test = np.hstack([X_test, X_test1] )<choose_model_class> | prev = previous_application["SK_ID_PREV"].values
POS_CASH_balance_IDS_prev = POS_CASH_balance["SK_ID_PREV"].values
credit_card_balance_IDS_prev = credit_card_balance["SK_ID_PREV"].values
installments_payments_IDS_prev = installments_payments["SK_ID_PREV"].values
prev_num = len(prev)
print(prev_num)
print(len(np.inter... | Home Credit Default Risk |
1,068,284 | models = []
seeds = [114]
for seed in seeds:
params = {
"objective": "regression",
"learning_rate":.02,
"tree_learner": "data",
"device_type": "cpu",
"num_leaves": 128,
"seed": seed,
"colsample_bytree":.8,
"max_depth": 7,
"subsample":.9,
"metric": ["rmse"]
}
skf = KFold(n_splits=5, random_state=seed, shuffle=True)
for... | bureau_br = np.unique(bureau["SK_ID_BUREAU"].values)
print(len(np.intersect1d(np.unique(bureau_balance["SK_ID_BUREAU"].values), bureau_br)) /len(bureau_br)*100 ) | Home Credit Default Risk |
1,068,284 | joblib.dump(models, model_dir / f"models-lgbm-{VERSION}.joblib" )<predict_on_test> | breau_total = np.unique(np.intersect1d(bureau_IDS, total_IDS))
bureau_filtered = bureau.loc[bureau["SK_ID_CURR"].isin(breau_total)]
b = np.intersect1d(np.unique(bureau_filtered["SK_ID_BUREAU"].values), np.unique(bureau_balance["SK_ID_BUREAU"].values))
bureau_filtered = bureau_filtered.loc[bureau_filtered["SK_ID_BUREAU"... | Home Credit Default Risk |
1,068,284 | for i, model in enumerate(models):
if i == 0:
y_preds = model.predict(X_test)
else:
y_preds = np.vstack(( y_preds, model.predict(X_test)) )<prepare_output> | print(len(np.unique(bureau_filtered["SK_ID_CURR"].values)) /tot*100 ) | Home Credit Default Risk |
1,068,284 | y_preds = np.expm1(y_preds )<prepare_output> | train = application_train.drop(["TARGET"], axis = 1)
train_target = application_train["TARGET"]
test= application_test.copy()
tr = len(application_train)
print(all(i ==True for i in train.columns==test.columns))
| Home Credit Default Risk |
1,068,284 | y_pred = y_preds.mean(axis=0 )<save_to_csv> | df = pd.concat([train, test])
del train, test, application_train, application_test
gc.collect()
def categorical_features(data):
features = [i for i in list(data.columns)if data[i].dtype == 'object']
return features
categorical = categorical_features(df)
numerical = [i for i in df.columns if i not in categorical]
nume... | Home Credit Default Risk |
1,068,284 | submission = pd.read_csv('.. /input/exam-for-students20200129/sample_submission.csv', index_col=0)
submission.ConvertedSalary = y_pred
submission.to_csv(model_dir / f'submission-{VERSION}.csv' )<feature_engineering> | for feature in categorical:
df[feature].fillna("unidentified")
print(f'Transforming {feature}...')
encoder = LabelEncoder()
encoder.fit(df[feature].astype(str))
df[feature] = encoder.transform(df[feature].astype(str))
df.head() | Home Credit Default Risk |
1,068,284 | feature_importances["mean"] = feature_importances.mean(axis=1 )<set_options> | for feats in df.columns:
df[feats] = df[feats].fillna(-1)
df.head() | Home Credit Default Risk |
1,068,284 | plt.style.use('ggplot')
%matplotlib inline
<set_options> | POS_CASH_balance_G1 = POS_CASH_balance.loc[POS_CASH_balance["SK_ID_CURR"].isin(total_IDS)]
print(len(np.unique(POS_CASH_balance_G1["SK_ID_CURR"].values)))
POS_CASH_balance_G1.head() | Home Credit Default Risk |
1,068,284 | pd.set_option('display.max_columns', 500 )<load_from_csv> | np.unique(POS_CASH_balance_G1["NAME_CONTRACT_STATUS"].values)
POS_CASH_balance_G1_num =(POS_CASH_balance_G1.groupby("SK_ID_CURR", as_index=False ).mean())
nb = POS_CASH_balance_G1[["SK_ID_CURR", "NAME_CONTRACT_STATUS"]].groupby("SK_ID_CURR", as_index = False ).count()
nb["num_in_POS_CASH"] = nb["NAME_CONTRACT_STATUS"... | Home Credit Default Risk |
1,068,284 | df_train = pd.read_csv('.. /input/exam-for-students20200129/train.csv', index_col=0)
df_test = pd.read_csv('.. /input/exam-for-students20200129/test.csv', index_col=0 )<count_missing_values> | bureau_G1 = bureau.drop(["SK_ID_BUREAU"], axis = 1 ).loc[bureau["SK_ID_CURR"].isin(total_IDS)]
print(len(np.unique(bureau_G1["SK_ID_CURR"].values)))
bureau_G1.head() | Home Credit Default Risk |
1,068,284 | df_train.isnull().sum()<count_missing_values> | bureau_G1_num =(bureau_G1.groupby("SK_ID_CURR", as_index=False ).mean())
nb = bureau_G1[["SK_ID_CURR", "CREDIT_ACTIVE"]].groupby("SK_ID_CURR", as_index = False ).count()
nb["num_in_bureau"] = nb["CREDIT_ACTIVE"]
df = df.merge(bureau_G1_num, on='SK_ID_CURR', how='left' ).fillna(-1)
df = df.merge(nb.drop("CREDIT_ACTIVE... | Home Credit Default Risk |
1,068,284 | df_test.isnull().sum()<count_values> | credit_card_balance_G1 = credit_card_balance.drop(["SK_ID_PREV"], axis = 1 ).loc[credit_card_balance["SK_ID_CURR"].isin(total_IDS)]
print(len(np.unique(credit_card_balance_G1["SK_ID_CURR"].values)))
credit_card_balance_G1.head() | Home Credit Default Risk |
1,068,284 | for col_name in df_train.columns:
print(df_train[col_name].value_counts())
print(df_train[col_name].value_counts() / len(df_train[col_name]))<count_values> | credit_card_balance_G1_num =(credit_card_balance_G1.groupby("SK_ID_CURR", as_index=False ).mean())
nb = credit_card_balance_G1[["SK_ID_CURR", "NAME_CONTRACT_STATUS"]].groupby("SK_ID_CURR", as_index = False ).count()
nb["num_in_credit_card"] = nb["NAME_CONTRACT_STATUS"]
df = df.merge(credit_card_balance_G1_num, on='SK_... | Home Credit Default Risk |
1,068,284 | for col_name in df_test.columns:
print(df_test[col_name].value_counts())
print(df_test[col_name].value_counts() / len(df_test[col_name]))<load_from_csv> | installments_payments_G1 = installments_payments.drop(["SK_ID_PREV"], axis = 1 ).loc[installments_payments["SK_ID_CURR"].isin(total_IDS)]
print(len(np.unique(installments_payments_G1["SK_ID_CURR"].values)))
installments_payments_G1.head() | Home Credit Default Risk |
1,068,284 | df_country_info = pd.read_csv('.. /input/exam-for-students20200129/country_info.csv' )<count_values> | installments_payments_G1_num =(installments_payments_G1.groupby("SK_ID_CURR", as_index=False ).mean())
nb = installments_payments_G1[["SK_ID_CURR", "NUM_INSTALMENT_VERSION"]].groupby("SK_ID_CURR", as_index = False ).count()
nb["num_in_install_pay"] = nb["NUM_INSTALMENT_VERSION"]
df = df.merge(installments_payments_G1_... | Home Credit Default Risk |
1,068,284 | df_country_info['Country'].value_counts()<create_dataframe> | previous_application_G1 = previous_application.drop(["SK_ID_PREV"], axis = 1 ).loc[previous_application["SK_ID_CURR"].isin(total_IDS)]
print(len(np.unique(previous_application_G1["SK_ID_CURR"].values)))
previous_application_G1.head() | Home Credit Default Risk |
1,068,284 | df_country_info_edit = df_country_info[['Country', 'Region']].copy()
df_country_info_edit<merge> | previous_application_G1_num =(previous_application_G1.groupby("SK_ID_CURR", as_index=False ).mean())
nb = previous_application_G1[["SK_ID_CURR", "NAME_CONTRACT_TYPE"]].groupby("SK_ID_CURR", as_index = False ).count()
nb["num_in_previous_app"] = nb["NAME_CONTRACT_TYPE"]
df = df.merge(previous_application_G1_num, on='SK... | Home Credit Default Risk |
1,068,284 | df_train_add_countryinfo = pd.merge(df_train, df_country_info_edit, how = 'left', on = ['Country'] ).copy()
df_test_add_countryinfo = pd.merge(df_test, df_country_info_edit, how = 'left', on = ['Country'] ).copy()<prepare_x_and_y> | train_X = df[:tr].drop("SK_ID_CURR", axis = 1)
test_X = df[tr:].drop("SK_ID_CURR", axis = 1)
y = train_target
x_train, x_val, y_train, y_val = train_test_split(train_X, y, test_size=0.2, random_state=18)
| Home Credit Default Risk |
1,068,284 | y_train = df_train_add_countryinfo['ConvertedSalary'].copy()
X_train = df_train_add_countryinfo.drop(['ConvertedSalary', 'Country'], axis=1 ).copy()
X_test = df_test_add_countryinfo.drop(['Country'], axis=1 ).copy()<count_unique_values> | lgt = lgb.Dataset(data=x_train, label=y_train)
lgv = lgb.Dataset(data=x_val, label=y_val)
params = {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'auc',
'learning_rate': 0.01, 'num_leaves': 48, 'num_iteration': 5000, 'verbose': 0 ,
'colsample_bytree':.8, 'subsample':.9, 'max_depth':7, 're... | Home Credit Default Risk |
1,068,284 | <categorify><EOS> | preds = model.predict(test_X)
submission = pd.read_csv(".. /input/sample_submission.csv")
submission['TARGET'] = preds
submission.to_csv("baseline.csv", index=False)
submission.head() | Home Credit Default Risk |
1,056,491 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<categorify> | import gc
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.preprocessing import StandardScaler,LabelEncoder
from sklearn.metrics import roc_auc_score | Home Credit Default Risk |
1,056,491 | X_train[cats] = encoder.fit_transform(X_train[cats])
X_test[cats] = encoder.transform(X_test[cats] )<categorify> | def add_noise(series, noise_level):
return series *(1 + noise_level * np.random.randn(len(series)))
def target_encode(trn_series=None,
tst_series=None,
target=None,
min_samples_leaf=1,
smoothing=1,
noise_level=0):
assert len(trn_series)== len(target)
assert trn_series.name == tst_series.name
temp = pd.concat([trn_s... | Home Credit Default Risk |
1,056,491 | target = 'ConvertedSalary'
t_encoding_col = ['Region', 'Employment', 'LastNewJob', 'YearsCodingProf', 'SalaryType', 'Currency', 'Age', 'Student', 'CompanySize', 'MilitaryUS', 'CareerSatisfaction', 'NumberMonitors', 'OperatingSystem', 'EducationParents']
for i, t_col in enumerate(t_encoding_col):
X_temp = pd.concat([X_t... | def UseGPFeatures(data):
v = pd.DataFrame()
v["i0"] = np.tanh(((((((data["te_ORGANIZATION_TYPE"])-(((data["EXT_SOURCE_3"])* 2.0)))) -(((data["EXT_SOURCE_2"])-(( -1.0*(( data["EXT_SOURCE_2"])))))))) -(data["EXT_SOURCE_1"])))
v["i1"] = np.tanh(((((data["NAME_CONTRACT_STATUS_Refused"])/ 2.0)) +(((((((np.minimum(((data["t... | Home Credit Default Risk |
1,056,491 | y_train_log = np.log1p(y_train ).copy()<train_on_grid> | gc.enable()
buro_bal = pd.read_csv('.. /input/bureau_balance.csv')
print('Buro bal shape : ', buro_bal.shape)
print('transform to dummies')
buro_bal = pd.concat([buro_bal, pd.get_dummies(buro_bal.STATUS, prefix='buro_bal_status')], axis=1 ).drop('STATUS', axis=1)
print('Counting buros')
buro_counts = buro_bal[['SK... | Home Credit Default Risk |
1,056,491 | scores = []
kf = KFold(n_splits=5, random_state=71, shuffle=True)
for i,(train_ix, test_ix)in tqdm(enumerate(kf.split(X_train, y_train_log))):
X_train_, y_train_ = X_train.values[train_ix], y_train_log.values[train_ix]
X_val, y_val = X_train.values[test_ix], y_train_log.values[test_ix]
reg = LGBMRegressor()
reg.fit(X_... | train.columns = train.columns.str.replace('[^A-Za-z0-9_]', '_')
test.columns = test.columns.str.replace('[^A-Za-z0-9_]', '_' ) | Home Credit Default Risk |
1,056,491 | np.expm1(y_pred )<predict_on_test> | floattypes = []
inttypes = []
stringtypes = []
for c in test.columns:
if(train[c].dtype=='object'):
train[c] = train[c].astype('str')
stringtypes.append(c)
elif(train[c].dtype=='int64'):
train[c] = train[c].astype('int32')
inttypes.append(c)
else:
train[c] = train[c].astype('float32')
floattypes.append(c)
train =... | Home Credit Default Risk |
1,056,491 | reg.fit(X_train, y_train_log, eval_metric='rmse')
y_pred = np.expm1(reg.predict(X_test))
<load_from_csv> | kf = KFold(n_splits=5, shuffle=True, random_state=42)
for col in stringtypes:
train['te_'+col] = 0.
test['te_'+col] = 0.
SMOOTHING = test[~test[col].isin(train[col])].shape[0]/test.shape[0]
_, test['te_'+col] = target_encode(train[col],
test[col],
target=train['TARGET'],
min_samples_leaf=100,
smoothing=SMOOTHING,
no... | Home Credit Default Risk |
1,056,491 | submission = pd.read_csv('.. /input/exam-for-students20200129/sample_submission.csv', index_col=0)
submission.ConvertedSalary = y_pred
submission.to_csv('submission.csv' )<compute_test_metric> | kf = KFold(n_splits=5, shuffle=True, random_state=42)
for col in inttypes:
train['te_'+col] = 0.
test['te_'+col] = 0.
SMOOTHING = test[~test[col].isin(train[col])].shape[0]/test.shape[0]
_, test['te_'+col] = target_encode(train[col],
test[col],
target=train['TARGET'],
min_samples_leaf=100,
smoothing=SMOOTHING,
noise... | Home Credit Default Risk |
1,056,491 | fti = reg.feature_importances_
print('Feature Importances:')
for i, feat in enumerate(X_train):
print('\t{0:20s} : {1:>.6f}'.format(feat, fti[i]))<set_options> | ntrainrows = train.shape[0]
alldata = pd.concat([train,test])
del train ,test
gc.collect() | Home Credit Default Risk |
1,056,491 | %matplotlib inline
plt.style.use("dark_background" )<define_variables> | alldata['nans'] = alldata.isnull().sum(axis=1 ) | Home Credit Default Risk |
1,056,491 | def seed_everything(seed=1234):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
tf.random.set_seed(seed)
np.random.seed(seed)
seed_everything(2020 )<define_variables> | for col in inttypes[1:]:
x = alldata[col].value_counts().reset_index(drop=False)
x.columns = [col,'cnt_'+col]
x['cnt_'+col]/=alldata.shape[0]
alldata = alldata.merge(x,on=col,how='left' ) | Home Credit Default Risk |
1,056,491 | main_dir = ".. /input/biobytes-contest"
<load_from_csv> | alldata[features] = alldata[features].astype('float32' ) | Home Credit Default Risk |
1,056,491 | with open(f"{main_dir}/Main_data.txt")as f:
temp = f.read()
temp = temp[:temp.index('>', 1)]
print("When printed it looks like so:
".upper() + temp)
print("Raw string:
".upper() + repr(temp))<load_from_csv> | for c in features:
ss = StandardScaler()
alldata.loc[~alldata[c].isnull() ,c] = ss.fit_transform(alldata.loc[~alldata[c].isnull() ,c].values.reshape(-1,1))
alldata[c].fillna(alldata[c].mean() ,inplace=True ) | Home Credit Default Risk |
1,056,491 | data = pd.read_csv(
f"{main_dir}/Main_data.txt",
sep='
', names=['Name', 'P_Seq', 'Target'],
lineterminator='>',
index_col=False
)
for name, value in zip(['Name', 'P_seq', 'Target'], data.iloc[0]):
print(f"{name}: {repr(value)}
" )<load_from_csv> | train = alldata[:ntrainrows]
test = alldata[ntrainrows:] | Home Credit Default Risk |
1,056,491 | test =(
pd.read_csv(
f"{main_dir}/Test_data.txt",
sep='
', names=['Name', 'P_Seq'],
lineterminator='>', index_col=False)
.applymap(lambda x: x.rstrip('\r'))
)
for name, value in zip(['Name', 'P_seq', 'Target'], test.iloc[0]):
print(f"{name}: {repr(value)}
" )<load_from_csv> | traintargets = train.TARGET.values
train = UseGPFeatures(train)
test = UseGPFeatures(test)
train['TARGET'] = traintargets | Home Credit Default Risk |
1,056,491 | sample_sub = pd.read_csv(f"{main_dir}/Sample_Solution.csv")
sample_sub.head()<feature_engineering> | gc.enable() | Home Credit Default Risk |
1,056,491 | data['P_Main_Class'] = data.Name.str.extract(":(\w)")
data['P_Sub_Class'] = data.Name.str.extract(" (.+):\w")
data['Seq_len'] = data.P_Seq.apply(len)
data['Uniq_seq_Count'] = data.P_Seq.apply(lambda x: len(set(x)))
data['B_Site_Count'] = data.Target.str.count('1')
data['B_Site_percent'] = data['B_Site_Count'] / da... | folds = KFold(n_splits=5, shuffle=True, random_state=42)
oof_preds = np.zeros(train.shape[0])
sub_preds = np.zeros(test.shape[0])
feats = [f for f in train.columns if f not in ['SK_ID_CURR','TARGET']] | Home Credit Default Risk |
1,056,491 | uniq = set()
for _, seq in data.P_Seq.iteritems() :
uniq |= set(seq)
print(uniq)
len(uniq )<categorify> | for n_fold,(trn_idx, val_idx)in enumerate(folds.split(train)) :
trn_x, trn_y = train[feats].iloc[trn_idx], train.iloc[trn_idx]['TARGET']
val_x, val_y = train[feats].iloc[val_idx], train.iloc[val_idx]['TARGET']
clf = LGBMClassifier(
n_estimators=4000,
learning_rate=0.03,
num_leaves=30,
colsample_bytree=.8,
subsample=.9... | Home Credit Default Risk |
1,056,491 | <categorify><EOS> | Submission = pd.DataFrame({ 'SK_ID_CURR': ID,'TARGET': sub_preds })
Submission.to_csv("hybrid.csv", index=False ) | Home Credit Default Risk |
1,030,120 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<feature_engineering> | pd.set_option('display.max_columns', None)
%matplotlib inline
color = sns.color_palette()
warnings.filterwarnings("ignore" ) | Home Credit Default Risk |
1,030,120 | temp = data.apply(lambda x: np.array(list(x[1])) [np.array(list(x[2])).astype(bool)], axis=1)
temp = temp.apply(pd.Series ).stack().reset_index(level=1, drop=True)
freq_occured = data.P_Seq.apply(lambda x: pd.Series(list(x)).value_counts() ).sum()
freq_bound = temp.value_counts()
freq = pd.merge(
pd.DataFrame(freq_o... | application_train = pd.read_csv('.. /input/application_train.csv')
application_test= pd.read_csv('.. /input/application_test.csv')
bureau = pd.read_csv('.. /input/bureau.csv')
bureau_balance = pd.read_csv('.. /input/bureau_balance.csv')
POS_CASH_balance = pd.read_csv('.. /input/POS_CASH_balance.csv')
credit_card_b... | Home Credit Default Risk |
1,030,120 | sub = test['P_Seq'].apply(list ).explode().reset_index()
sub = sub.rename({'index': 'Seq_No', "P_Seq": 'Peptide'}, axis=1)
sub['Id'] = sub.index
sub = sub.iloc[:, [-1, 0, 1]]
sub.head()<data_type_conversions> | application_train = pd.read_csv('.. /input/application_train.csv')
application_test= pd.read_csv('.. /input/application_test.csv' ) | Home Credit Default Risk |
1,030,120 | sub["Expected"] = sub.groupby("Seq_No")['Id'].transform(lambda x:(np.random.random(len(x)) < 0.35 ).astype(int))
sub.head()<save_to_csv> | def feature_type_split(data, special_list=[]):
cat_list = []
dis_num_list = []
num_list = []
for i in data.columns.tolist() :
if data[i].dtype == 'object':
cat_list.append(i)
elif data[i].nunique() < 25:
dis_num_list.append(i)
elif i in special_list:
dis_num_list.append(i)
else:
num_list.append(i)
return cat_list, ... | Home Credit Default Risk |
1,030,120 | sub[['Id', 'Expected']].to_csv("Naive_submission.csv", index=False )<categorify> | application_train['TERM'] = application_train.AMT_CREDIT / application_train.AMT_ANNUITY
application_test['TERM'] = application_test.AMT_CREDIT / application_test.AMT_ANNUITY | Home Credit Default Risk |
1,030,120 | mapper = dict(zip(freq.Peptide, freq.Percent))
print(mapper )<data_type_conversions> | application_train['OVER_EXPECT_CREDIT'] =(application_train.AMT_CREDIT > application_train.AMT_GOODS_PRICE ).map({False:0, True:1})
application_test['OVER_EXPECT_CREDIT'] =(application_test.AMT_CREDIT > application_test.AMT_GOODS_PRICE ).map({False:0, True:1} ) | Home Credit Default Risk |
1,030,120 | sub['Expected'] =(
sub.Peptide.map(mapper)/ 100
> sub.groupby('Peptide')['Id'].transform(lambda x: np.random.random(len(x)))
).astype(int )<categorify> | application_train['MEAN_BUILDING_SCORE_AVG'] = application_train.iloc[:, 44:58].mean(skipna=True, axis=1)
application_train['TOTAL_BUILDING_SCORE_AVG'] = application_train.iloc[:, 44:58].sum(skipna=True, axis=1)
application_test['MEAN_BUILDING_SCORE_AVG'] = application_test.iloc[:, 44:58].mean(skipna=True, axis=1)
a... | Home Credit Default Risk |
1,030,120 | temp = np.random.choice(list(mapper.keys()))
mapper[temp] / 100, sub.loc[sub.Peptide == temp, 'Expected'].mean()<save_to_csv> | application_train['FLAG_DOCUMENT_TOTAL'] = application_train.iloc[:, 96:116].sum(axis=1)
application_test['FLAG_DOCUMENT_TOTAL'] = application_test.iloc[:, 96:116].sum(axis=1 ) | Home Credit Default Risk |
1,030,120 | sub[['Id', 'Expected']].to_csv("Peptide_Based_Fprediction.csv", index=False )<categorify> | application_train['AMT_REQ_CREDIT_BUREAU_TOTAL'] = application_train.iloc[:, 116:122].sum(axis=1)
application_test['AMT_REQ_CREDIT_BUREAU_TOTAL'] = application_test.iloc[:, 116:122].sum(axis=1 ) | Home Credit Default Risk |
1,030,120 | Y = data.Target.apply(list ).explode().values.astype(int)
y_hat_naive =(np.random.random(len(Y)) < 0.35 ).astype(int)
y_hat_freq_based =(
data.P_Seq.apply(list ).explode().reset_index(drop=True ).map(mapper)/ 100
>
(data.P_Seq.apply(list ).explode().to_frame().reset_index().groupby("P_Seq")
.transform(lambda x: np.... | application_train['BIRTH_EMPLOTED_INTERVEL'] = application_train.DAYS_EMPLOYED - application_train.DAYS_BIRTH
application_test['BIRTH_EMPLOTED_INTERVEL'] = application_test.DAYS_EMPLOYED - application_test.DAYS_BIRTH | Home Credit Default Risk |
1,030,120 | flat_data = pd.DataFrame({
"P_Seq": data.P_Seq.apply(list ).explode() ,
"Target": data.Target.apply(list ).explode().astype(int)
})
flat_data.head()<choose_model_class> | application_train['BIRTH_REGISTRATION_INTERVEL'] = application_train.DAYS_REGISTRATION - application_train.DAYS_BIRTH
application_test['BIRTH_REGISTRATION_INTERVEL'] = application_test.DAYS_REGISTRATION - application_test.DAYS_BIRTH | Home Credit Default Risk |
1,030,120 | tf.keras.backend.clear_session()
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Input(shape=(len(peptide_mapper),)))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=tf.keras.metrics.AUC())
model.summary()<train_model> | application_train['MEAN_BUILDING_SCORE_AVG'] = application_train.iloc[:, 44:58].mean(skipna=True, axis=1)
application_train['TOTAL_BUILDING_SCORE_AVG'] = application_train.iloc[:, 44:58].sum(skipna=True, axis=1)
application_test['MEAN_BUILDING_SCORE_AVG'] = application_test.iloc[:, 44:58].mean(skipna=True, axis=1)
a... | Home Credit Default Risk |
1,030,120 | hist = model.fit(
tf.one_hot(flat_data.P_Seq.map(peptide_mapper), depth=20),
flat_data.Target,
validation_split=0.2,
callbacks=tf.keras.callbacks.EarlyStopping(patience=10),
epochs=100,
verbose=0)
print("Train Best ROC_AUC Score: {:.2f}".format(hist.history['auc'][-1]))
print("Val Best ROC_AUC Score: {:6.2f}".format(... | import lightgbm as lgb
from lightgbm import LGBMClassifier
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold | Home Credit Default Risk |
1,030,120 | sub['Expected'] =(
model.predict(tf.one_hot(
sub.Peptide.map(peptide_mapper),
depth=len(peptide_mapper)))
)<save_to_csv> | X = application_train.drop(['SK_ID_CURR', 'TARGET'], axis=1)
y = application_train.TARGET
X_pred = application_test.drop(['SK_ID_CURR'], axis=1 ) | Home Credit Default Risk |
1,030,120 | sub[['Id', 'Expected']].to_csv("Linear_model_one_hot.csv", index=False )<categorify> | folds = StratifiedKFold(n_splits=5,random_state=6)
oof_preds = np.zeros(X.shape[0])
sub_preds = np.zeros(X_pred.shape[0] ) | Home Credit Default Risk |
1,030,120 | flat_data['Percent'] = flat_data.P_Seq.map(mapper ).astype(float)/ 100
flat_data = flat_data.merge(data.iloc[:, 3:7], right_index=True, left_index=True)
flat_data['Position'] = flat_data.groupby(flat_data.index)['P_Seq'].transform(lambda x: np.arange(len(x)) / len(x))
flat_data.head()<feature_engineering> | start = time.time()
valid_score = 0
for n_fold,(trn_idx, val_idx)in enumerate(folds.split(X, y)) :
trn_x, trn_y = X.iloc[trn_idx], y[trn_idx]
val_x, val_y = X.iloc[val_idx], y[val_idx]
train_data = lgb.Dataset(data=trn_x, label=trn_y,categorical_feature=categorical_feats)
valid_data = lgb.Dataset(data=val_x, label=val... | Home Credit Default Risk |
1,030,120 | <categorify><EOS> | application_test= pd.read_csv('.. /input/application_test.csv')
output = pd.DataFrame({'SK_ID_CURR':application_test.SK_ID_CURR, 'TARGET': sub_preds})
output.to_csv('only_application_pred.csv', index=False ) | Home Credit Default Risk |
1,020,569 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<choose_model_class> | import gc
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler | Home Credit Default Risk |
1,020,569 | tf.keras.backend.clear_session()
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Input(shape=(shape,)))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=tf.keras.metrics.AUC())
hist = model.fit(
process_flat_df(flat_data.drop... | def add_noise(series, noise_level):
return series *(1 + noise_level * np.random.randn(len(series)))
def target_encode(trn_series=None,
tst_series=None,
target=None,
min_samples_leaf=1,
smoothing=1,
noise_level=0):
assert len(trn_series)== len(target)
assert trn_series.name == tst_series.name
temp = pd.concat([trn_s... | Home Credit Default Risk |
1,020,569 | predictions = model.predict(
process_flat_df(
sub.drop(['Id'], axis=1),
ohc=[('P_Sub_Class', len(psc_mapper)) ])
)
sub['Expected'] = predictions
sub.head()<save_to_csv> | gc.enable()
buro_bal = pd.read_csv('.. /input/bureau_balance.csv')
print('Buro bal shape : ', buro_bal.shape)
print('transform to dummies')
buro_bal = pd.concat([buro_bal, pd.get_dummies(buro_bal.STATUS, prefix='buro_bal_status')], axis=1 ).drop('STATUS', axis=1)
print('Counting buros')
buro_counts = buro_bal[['SK... | Home Credit Default Risk |
1,020,569 | sub[['Id', 'Expected']].to_csv("Linear_model_with_meta.csv", index=False )<categorify> | floattypes = []
inttypes = []
stringtypes = []
for c in test.columns:
if(train[c].dtype=='object'):
train[c] = train[c].astype('str')
stringtypes.append(c)
elif(train[c].dtype=='int64'):
train[c] = train[c].astype('int32')
inttypes.append(c)
else:
train[c] = train[c].astype('float32')
floattypes.append(c ) | Home Credit Default Risk |
1,020,569 | def get_freq(series, min_thresh=0.65, fit=False):
'Returns a 20 * n matrix containing frequencies for each sequence'
global cnt
if fit:
cnt = CountVectorizer(analyzer='char', ngram_range=(1, 2), min_df=min_thresh, lowercase=False)
return pd.DataFrame(cnt.fit_transform(series ).todense() , columns=cnt.get_feature_names... | kf = KFold(n_splits=5, shuffle=True, random_state=42)
for col in stringtypes:
train['te_'+col] = 0.
test['te_'+col] = 0.
SMOOTHING = test[~test[col].isin(train[col])].shape[0]/test.shape[0]
_, test['te_'+col] = target_encode(train[col],
test[col],
target=train['TARGET'],
min_samples_leaf=100,
smoothing=SMOOTHING,
no... | Home Credit Default Risk |
1,020,569 | dc=[
'P_Sub_Class',
]
ohc=[
('P_Sub_Class', len(psc_mapper)) ,
]
temp = process_flat_df(
flat_data.drop("Target", 1),
ohc=ohc,
dc=dc,
pep_freq=(data.P_Seq, True),
as_df=True
).head(3)
shape = temp.shape[1]
temp<choose_model_class> | kf = KFold(n_splits=5, shuffle=True, random_state=42)
for col in inttypes:
train['te_'+col] = 0.
test['te_'+col] = 0.
SMOOTHING = test[~test[col].isin(train[col])].shape[0]/test.shape[0]
_, test['te_'+col] = target_encode(train[col],
test[col],
target=train['TARGET'],
min_samples_leaf=100,
smoothing=SMOOTHING,
noise... | Home Credit Default Risk |
1,020,569 | tf.keras.backend.clear_session()
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Input(shape=(shape,)))
model.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=tf.keras.optimizers.Adam(0.0025),
metrics=tf.keras.metrics.AUC())
hist = model.fit(... | test.insert(1,'TARGET',-1)
alldata = pd.concat([train,test])
del train ,test
gc.collect() | Home Credit Default Risk |
1,020,569 | predictions = model.predict(
process_flat_df(
sub.drop(["Expected", "Id"], 1),
ohc=ohc,
dc=dc,
pep_freq=(test.P_Seq, False)) ,
)
sub['Expected'] = predictions
sub.head(3 )<save_to_csv> | alldata[features] = alldata[features].astype('float32' ) | Home Credit Default Risk |
1,020,569 | sub[['Id', 'Expected']].to_csv("Linear_model_with_Seq_freq.csv", index=False )<categorify> | alldata['nans'] = alldata.isnull().sum(axis=1 ) | Home Credit Default Risk |
1,020,569 | SHIFT = 2
dc=[
'P_Main_Class',
'Uniq_seq_Count',
]
ohc=[
]
temp = process_flat_df(
flat_data.drop("Target", 1),
ohc=ohc,
dc=dc,
pep_freq=(data.P_Seq, True),
seq_shift=SHIFT,
as_df=True
).head(5)
shape = temp.shape[1]
print("The number of columns in data that would be fit to our model is:", shape)
temp.iloc[:3, 29:29... | for col in inttypes[1:]:
x = alldata[col].value_counts().reset_index(drop=False)
x.columns = [col,'cnt_'+col]
x['cnt_'+col]/=alldata.shape[0]
alldata = alldata.merge(x,on=col,how='left' ) | Home Credit Default Risk |
1,020,569 | flat_data['P_Seq'].head(5 ).map(peptide_mapper ).values<choose_model_class> | for c in features:
ss = StandardScaler()
alldata.loc[~alldata[c].isnull() ,c] = ss.fit_transform(alldata.loc[~alldata[c].isnull() ,c].values.reshape(-1,1))
alldata[c].fillna(alldata[c].mean() ,inplace=True ) | Home Credit Default Risk |
1,020,569 | SHIFT = 3
tf.keras.backend.clear_session()
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Input(shape=(shape,)))
model.add(tf.keras.layers.Dropout(0.1))
model.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=tf.keras.optimizers.Adam(0.005),
m... | train = alldata[alldata.TARGET>-1]
test = alldata[alldata.TARGET==-1] | Home Credit Default Risk |
1,020,569 | predictions = model.predict(
process_flat_df(
sub.drop(["Expected", "Id"], 1),
ohc=ohc,
dc=dc,
pep_freq=(test.P_Seq, False)
),
)
sub['Expected'] = predictions
sub.head(3 )<save_to_csv> | def GPI(data):
return Output(0.100000*np.tanh(((((((((data["EXT_SOURCE_3"])* 2.0)) -(((data["te_OCCUPATION_TYPE"])-(((((( data["EXT_SOURCE_1"])>(data["DAYS_CREDIT"])) *1.))+(data["EXT_SOURCE_2"])))))))*(-2.0)))-(data["EXT_SOURCE_2"])))+
0.100000*np.tanh(((((((((data["te_OCCUPATION_TYPE"])-(data["EXT_SOURCE_3"])))+(( -1... | Home Credit Default Risk |
1,020,569 | sub[['Id', 'Expected']].to_csv("Linear_model_with_hist.csv", index=False )<import_modules> | def GPII(data):
return Output(0.100000*np.tanh(((((((data["te_ORGANIZATION_TYPE"])-(((data["EXT_SOURCE_3"])* 2.0)))) -(((data["EXT_SOURCE_2"])-(( -1.0*(( data["EXT_SOURCE_2"])))))))) -(data["EXT_SOURCE_1"])))+
0.100000*np.tanh(((((data["NAME_CONTRACT_STATUS_Refused"])/ 2.0)) +(((((((np.minimum(((data["te_OCCUPATION_TYP... | Home Credit Default Risk |
1,020,569 | for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
<load_from_csv> | def GPIII(data):
return Output(0.100000*np.tanh(((( 6.72599077224731445)) -(np.where(((((( -1.0*(((((((((-1.0*(( data["EXT_SOURCE_3"])))) -(data["EXT_SOURCE_1"])))/ 2.0)) * 2.0)))))* 2.0)) * 2.0)>0,(11.37177181243896484), data["EXT_SOURCE_3"])))) +
0.100000*np.tanh(((data["DAYS_BIRTH"])+(((((((((data["NAME_CONTRACT_STA... | Home Credit Default Risk |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.