def _bureau_and_balance(self, configs):
current_index = self.data_index['bureau']
major_index = self.data_index['application_train']
nan_as_category = configs.get('nan_as_category', False)
# Read data and merge
df = self.data_raw['bureau']
bb = self.data_raw['bureau_balance']
logger.info("Bureau: {}, Bureau Balance: {}".format(
df.shape, bb.shape))
if configs.get('onehot_encoding', False):
df, cat_cols, new_cols = process_one_hot_encode(
df, configs['onehot_columns'], nan_as_category)
bb, cat_cols_bb, new_cols_bb = process_one_hot_encode(
bb, configs['onehot_columns'], nan_as_category)
self.cols_one_hot.update({'bureau': new_cols + new_cols_bb})
agg_configs = self._split_configs(configs.copy(), 'bureau_balance')
bb_agg = self._aggregate_pipeline(bb, cat_cols_bb,
agg_configs)[current_index]
df = df.set_index(current_index).join(bb_agg, how='left')
bureau_cat_cols = cat_cols + [
c for c in bb_agg
if any([True if cc in c else False for cc in cat_cols_bb])
]
#condictional aggregation
# Bureau: Active credits - using only numerical aggregations
# Bureau: Closed credits - using only numerical aggregations
agg_configs = self._split_configs(configs.copy(), 'bureau')
bureau_agg = self._aggregate_pipeline(df, bureau_cat_cols,
agg_configs)[major_index]
return Cast64To32(bureau_agg)
def _application_train_test(self, configs):
nan_as_category = configs.get('nan_as_category', False)
# Read data and merge
major_index = self.data_index['application_train']
df = self.data_raw['application_train']
test_df = self.data_raw['application_test']
logger.info("Train samples: {}, test samples: {}".format(
df.shape, test_df.shape))
df = df.append(test_df, sort=False, ignore_index=True)
df = process_drop_rows(df, process_configs=configs['filter_rows'])
df = process_factorize(df,
process_configs=configs['factorize_columns'])
if configs.get('onehot_encoding', False):
df, cat_cols, new_cols = process_one_hot_encode(
df, configs['onehot_columns'], nan_as_category)
self.cols_one_hot.update({'application': new_cols})
else:
cat_cols = IdentifyCategoricalColumn(df)
df = process_replace(df, process_configs=configs['replace_rows'])
df, interact_cols = process_interaction(
df, process_configs=configs['interaction_columns'])
if configs.get('deep_interactions', []):
deep_interactions = configs.get('deep_interactions', [])
for c in deep_interactions:
df = process_deep_interactions(df, c)
logger.info('prepare decompostion, application={}'.format(df.shape))
df_ext = [
process_decomposition(df, c) for c in configs['decomposition']
]
df = pd.concat([df] + df_ext, axis=1, join='inner')
logger.info('finished decompositions, application={}'.format(df.shape))
df = Cast64To32(df)
# seperate train test
# Divide in training/validation and test data
train_df = df.loc[df[
self.target_column].notnull()].reset_index().set_index(major_index)
test_df = df.loc[df[
self.target_column].isnull()].reset_index().set_index(major_index)
logger.info("Split into train samples: {}, test samples: {}".format(
train_df.shape, test_df.shape))
del df
gc.collect()
return train_df, test_df
def _pos_cash_balance(self, configs):
current_index = self.data_index['pos_cash_balance']
major_index = self.data_index['application_train']
nan_as_category = configs.get('nan_as_category', False)
df = self.data_raw['pos_cash_balance']
logger.info("pos_cash: {}".format(df.shape))
if configs.get('onehot_encoding', False):
df, cat_cols, new_cols = process_one_hot_encode(
df, configs['onehot_columns'], nan_as_category)
self.cols_one_hot.update({'pos_cash': new_cols})
else:
cat_cols = IdentifyCategoricalColumn(df)
pos_cash_agg = self._aggregate_pipeline(df, cat_cols,
configs)[major_index]
return Cast64To32(pos_cash_agg)
def _installments_payments(self, configs):
current_index = self.data_index['installments_payments']
major_index = self.data_index['application_train']
nan_as_category = configs.get('nan_as_category', False)
df = self.data_raw['installments_payments']
logger.info("installments_payments: {}".format(df.shape))
cat_cols = []
if configs.get('onehot_encoding', False):
df, cat_cols, new_cols = process_one_hot_encode(
df, cat_cols, nan_as_category)
self.cols_one_hot.update({'installments_payments': new_cols})
else:
cat_cols = IdentifyCategoricalColumn(df)
df, interact_cols = process_interaction(
df, process_configs=configs['interaction_columns'])
installments_agg = self._aggregate_pipeline(df, cat_cols,
configs)[major_index]
return Cast64To32(installments_agg)
def _previous_application(self, configs):
current_index = self.data_index['previous_application']
major_index = self.data_index['application_train']
nan_as_category = configs.get('nan_as_category', False)
df = self.data_raw['previous_application']
logger.info("Previous application: {}".format(df.shape))
if configs.get('onehot_encoding', False):
df, cat_cols, new_cols = process_one_hot_encode(
df, configs['onehot_columns'], nan_as_category)
self.cols_one_hot.update({'previous_application': new_cols})
else:
cat_cols = IdentifyCategoricalColumn(df)
df = process_replace(df, process_configs=configs['replace_rows'])
df, interact_cols = process_interaction(
df, process_configs=configs['interaction_columns'])
# Previous applications categorical features
# Previous Applications: Approved Applications - only numerical features
# Previous Applications: Refused Applications - only numerical features
prev_agg = self._aggregate_pipeline(df, cat_cols, configs)[major_index]
return Cast64To32(prev_agg)