def _create_feature(cls, conf) -> pd.DataFrame:
app = Application.get_df(conf)
features = pd.DataFrame({'SK_ID_CURR': app['SK_ID_CURR'].unique()})
features['annuity_income_percentage'] = app['AMT_ANNUITY'] / app[
'AMT_INCOME_TOTAL']
features['car_to_birth_ratio'] = app['OWN_CAR_AGE'] / app['DAYS_BIRTH']
features[
'car_to_employ_ratio'] = app['OWN_CAR_AGE'] / app['DAYS_EMPLOYED']
features[
'children_ratio'] = app['CNT_CHILDREN'] / app['CNT_FAM_MEMBERS']
features[
'credit_to_annuity_ratio'] = app['AMT_CREDIT'] / app['AMT_ANNUITY']
features['credit_to_goods_ratio'] = app['AMT_CREDIT'] / app[
'AMT_GOODS_PRICE']
features['credit_to_income_ratio'] = app['AMT_CREDIT'] / app[
'AMT_INCOME_TOTAL']
features['days_employed_percentage'] = app['DAYS_EMPLOYED'] / app[
'DAYS_BIRTH']
features['income_credit_percentage'] = app['AMT_INCOME_TOTAL'] / app[
'AMT_CREDIT']
features['income_per_child'] = app['AMT_INCOME_TOTAL'] / (
1 + app['CNT_CHILDREN'])
features['income_per_person'] = app['AMT_INCOME_TOTAL'] / app[
'CNT_FAM_MEMBERS']
features['payment_rate'] = app['AMT_ANNUITY'] / app['AMT_CREDIT']
features['phone_to_birth_ratio'] = app['DAYS_LAST_PHONE_CHANGE'] / app[
'DAYS_BIRTH']
features['phone_to_employ_ratio'] = app[
'DAYS_LAST_PHONE_CHANGE'] / app['DAYS_EMPLOYED']
features[
'external_sources_weighted'] = app.EXT_SOURCE_1 * 2 + app.EXT_SOURCE_2 * 3 + app.EXT_SOURCE_3 * 4
features[
'cnt_non_child'] = app['CNT_FAM_MEMBERS'] - app['CNT_CHILDREN']
features['child_to_non_child_ratio'] = app['CNT_CHILDREN'] / features[
'cnt_non_child']
features['income_per_non_child'] = app['AMT_INCOME_TOTAL'] / features[
'cnt_non_child']
features[
'credit_per_person'] = app['AMT_CREDIT'] / app['CNT_FAM_MEMBERS']
features['credit_per_child'] = app['AMT_CREDIT'] / (
1 + app['CNT_CHILDREN'])
features['credit_per_non_child'] = app['AMT_CREDIT'] / features[
'cnt_non_child']
for function_name in ['min', 'max', 'sum', 'mean', 'nanmedian']:
features['external_sources_{}'.format(function_name)] = eval(
'np.{}'.format(function_name))(
app[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']],
axis=1)
features['short_employment'] = (app['DAYS_EMPLOYED'] <
-2000).astype(int)
features['young_age'] = (app['DAYS_BIRTH'] < -14000).astype(int)
return features
def _create_feature(cls, conf) -> pd.DataFrame:
df = Application.get_df(conf)
# fit with train data and transform both data
train_df = df[df['TARGET'].notnull()].copy()
categorical_columns = [
col for col in df.columns if df[col].dtype == 'object'
]
df = TargetEncoder(cols=categorical_columns).fit(
train_df, train_df['TARGET']).transform(df)
return df[categorical_columns + ['SK_ID_CURR']].rename(columns={
col: f"{col}_target_encode"
for col in categorical_columns
})
def _create_feature(cls, conf) -> pd.DataFrame:
df = Application.get_df(conf)
categorical_columns = [
col for col in df.columns if df[col].dtype == 'object'
]
df = df[categorical_columns + ['SK_ID_CURR']]
for c in categorical_columns:
feature = df[[c, 'SK_ID_CURR']].groupby(c)[[
'SK_ID_CURR'
]].count().reset_index().rename(
columns={'SK_ID_CURR': f'app_{c}_value_count'})
df = df.merge(feature, on=c, how='left')
return df.drop(categorical_columns, axis=1)
def _create_feature(cls, conf) -> pd.DataFrame:
df = Application.get_df(conf)
categorical_columns = [
col for col in df.columns if df[col].dtype == 'object'
]
df = df[categorical_columns + ['SK_ID_CURR']]
cat_pairs = itertools.combinations(categorical_columns, 2)
for col1, col2 in tqdm(list(cat_pairs)):
feature = df[[col1, col2, 'SK_ID_CURR']].groupby(by=[col1, col2])[[
'SK_ID_CURR'
]].count().reset_index().rename(
columns={'SK_ID_CURR': f'app_{col1}_{col2}_value_count'})
df = df.merge(feature, on=[col1, col2], how='left')
return df.drop(categorical_columns, axis=1)
def _create_feature(cls, conf) -> pd.DataFrame:
df = Application.get_df(conf)
features = pd.DataFrame({'SK_ID_CURR': df['SK_ID_CURR'].unique()})
features['app EXT_SOURCE_1 * EXT_SOURCE_2'] = df['EXT_SOURCE_1'] * df[
'EXT_SOURCE_2']
features['app EXT_SOURCE_1 * EXT_SOURCE_3'] = df['EXT_SOURCE_1'] * df[
'EXT_SOURCE_3']
features['app EXT_SOURCE_2 * EXT_SOURCE_3'] = df['EXT_SOURCE_2'] * df[
'EXT_SOURCE_3']
features['app EXT_SOURCE_1 * DAYS_EMPLOYED'] = df['EXT_SOURCE_1'] * df[
'DAYS_EMPLOYED']
features['app EXT_SOURCE_2 * DAYS_EMPLOYED'] = df['EXT_SOURCE_2'] * df[
'DAYS_EMPLOYED']
features['app EXT_SOURCE_3 * DAYS_EMPLOYED'] = df['EXT_SOURCE_3'] * df[
'DAYS_EMPLOYED']
features['app EXT_SOURCE_1 / DAYS_BIRTH'] = df['EXT_SOURCE_1'] / df[
'DAYS_BIRTH']
features['app EXT_SOURCE_2 / DAYS_BIRTH'] = df['EXT_SOURCE_2'] / df[
'DAYS_BIRTH']
features['app EXT_SOURCE_3 / DAYS_BIRTH'] = df['EXT_SOURCE_3'] / df[
'DAYS_BIRTH']
features['app AMT_CREDIT - AMT_GOODS_PRICE'] = df['AMT_CREDIT'] - df[
'AMT_GOODS_PRICE']
features['app AMT_INCOME_TOTAL / 12 - AMT_ANNUITY'] = df[
'AMT_INCOME_TOTAL'] / 12. - df['AMT_ANNUITY']
features['app AMT_INCOME_TOTAL / AMT_ANNUITY'] = df[
'AMT_INCOME_TOTAL'] / df['AMT_ANNUITY']
features['app AMT_INCOME_TOTAL - AMT_GOODS_PRICE'] = df[
'AMT_INCOME_TOTAL'] - df['AMT_GOODS_PRICE']
features['app most popular AMT_GOODS_PRICE'] = df[
'AMT_GOODS_PRICE'].isin([225000, 450000, 675000, 900000]).map({
True:
1,
False:
0
})
features['app popular AMT_GOODS_PRICE'] = df['AMT_GOODS_PRICE'].isin(
[1125000, 1350000, 1575000, 1800000, 2250000]).map({
True: 1,
False: 0
})
features['app DAYS_EMPLOYED - DAYS_BIRTH'] = df['DAYS_EMPLOYED'] - df[
'DAYS_BIRTH']
return features
def _create_feature(cls, conf) -> pd.DataFrame:
df = Application.get_df(conf)[[
'SK_ID_CURR', 'TARGET', cls._col1, cls._col2
]]
df = OrdinalEncoder(cols=[cls._col1, cls._col2]).fit_transform(df)
latent_vectors = lda(cls._n_components, df, cls._col1, cls._col2)
dic = defaultdict(list)
for v in latent_vectors:
for i, s in enumerate(v):
dic[f"{cls._col1}_LDA_{cls._col2}_dim{i}"].append(s)
df_latent_vectors = pd.DataFrame(dic)
return df.merge(df_latent_vectors,
how="left",
left_on=cls._col1,
right_index=True).drop(
['TARGET', cls._col1, cls._col2], axis=1)
def _create_feature(cls, conf) -> pd.DataFrame:
df = Application.get_df(conf)
# fit with train data and transform both data
categorical_columns = [
col for col in df.columns if df[col].dtype == 'object'
]
train_df = df[df['TARGET'].notnull()].copy()
test_df = df[df['TARGET'].isnull()].copy()
feature = pd.DataFrame()
folds = StratifiedKFold(**conf.model.kfold_params)
for n_fold, (train_idx, valid_idx) in tqdm(
enumerate(
folds.split(train_df[categorical_columns],
train_df['TARGET'])),
total=conf.model.kfold_params.n_splits):
encoder = TargetEncoder(cols=categorical_columns).fit(
train_df.iloc[train_idx][categorical_columns + ['SK_ID_CURR']],
train_df.iloc[train_idx]['TARGET'])
valid_te = encoder.transform(
train_df.iloc[valid_idx][categorical_columns +
['SK_ID_CURR']]).rename(columns={
col: f"{col}_target_encode"
for col in categorical_columns
})
test_te = encoder.transform(
test_df[categorical_columns + ['SK_ID_CURR']]).rename(columns={
col: f"{col}_target_encode"
for col in categorical_columns
})
feature = feature.append(valid_te, sort=True).append(test_te,
sort=True)
# take mean of oof target mean for test data
feature = feature.groupby('SK_ID_CURR').mean()
return feature
def _create_feature(cls, conf) -> pd.DataFrame:
df = Application.get_df(conf)
df = ApplicationFeatures._features_from_kernel(df)
df = ApplicationFeatures._binarize_features(df)
df, _cat_cols = one_hot_encoder(df, True)
return ApplicationFeatures._filter_features(df)
def _create_feature(cls, conf) -> pd.DataFrame:
return Application.get_df(conf)[["SK_ID_CURR", "TARGET"]]