class LinearBrandVsBrandIndexKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(LinearBrandVsBrandIndexKpi, self).__init__(data_provider, config_params=config_params, **kwargs)
self.util = PepsicoUtil(None, data_provider)
def kpi_type(self):
pass
def calculate(self):
index_targets = self.util.get_relevant_sos_vs_target_kpi_targets(brand_vs_brand=True)
index_targets['numerator_id'] = index_targets.apply(self.util.retrieve_relevant_item_pks, axis=1,
args=('numerator_type', 'numerator_value'))
index_targets['denominator_id'] = index_targets.apply(self.util.retrieve_relevant_item_pks, axis=1,
args=('denominator_type', 'denominator_value'))
index_targets['identifier_parent'] = index_targets['KPI Parent'].apply(lambda x:
self.util.common.get_dictionary(
kpi_fk=int(float(x))))
index_targets = index_targets[index_targets['type'] == self._config_params['kpi_type']]
for i, row in index_targets.iterrows():
general_filters = {row['additional_filter_type_1']: row['additional_filter_value_1']}
numerator_sos_filters = {row['numerator_type']: row['numerator_value']}
num_num_linear, num_denom_linear = self.util.calculate_sos(numerator_sos_filters, **general_filters)
numerator_sos = num_num_linear/num_denom_linear if num_denom_linear else 0
denominator_sos_filters = {row['denominator_type']: row['denominator_value']}
denom_num_linear, denom_denom_linear = self.util.calculate_sos(denominator_sos_filters, **general_filters)
denominator_sos = denom_num_linear/denom_denom_linear if denom_denom_linear else 0
index = numerator_sos / denominator_sos if denominator_sos else 0
self.write_to_db_result(fk=row.kpi_level_2_fk, numerator_id=row.numerator_id,
numerator_result=num_num_linear, denominator_id=row.denominator_id,
denominator_result=denom_num_linear, result=index, score=index)
self.util.add_kpi_result_to_kpi_results_df([row.kpi_level_2_fk, row.numerator_id, row.denominator_id, index,
index])
class LinearBrandVsBrandIndexKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(LinearBrandVsBrandIndexKpi,
self).__init__(data_provider,
config_params=config_params,
**kwargs)
self.util = PepsicoUtil(None, data_provider)
def kpi_type(self):
pass
def calculate(self):
self.util.filtered_scif, self.util.filtered_matches = \
self.util.commontools.set_filtered_scif_and_matches_for_specific_kpi(self.util.filtered_scif,
self.util.filtered_matches,
self._config_params['kpi_type'])
index_targets = self.util.get_relevant_sos_vs_target_kpi_targets(
brand_vs_brand=True)
index_targets['numerator_id'] = index_targets.apply(
self.util.retrieve_relevant_item_pks,
axis=1,
args=('numerator_type', 'numerator_value'))
index_targets['denominator_id'] = index_targets.apply(
self.util.retrieve_relevant_item_pks,
axis=1,
args=('denominator_type', 'denominator_value'))
index_targets['identifier_parent'] = index_targets['KPI Parent'].apply(
lambda x: self.util.common.get_dictionary(kpi_fk=int(float(x))))
index_targets = index_targets[index_targets['type'] ==
self._config_params['kpi_type']]
location_type_fk = self.util.all_templates[self.util.all_templates[ScifConsts.LOCATION_TYPE] == 'Primary Shelf'] \
[ScifConsts.LOCATION_TYPE_FK].values[0]
for i, row in index_targets.iterrows():
general_filters = {
row['additional_filter_type_1']:
row['additional_filter_value_1']
}
numerator_sos_filters = {
row['numerator_type']: row['numerator_value']
}
num_num_linear, num_denom_linear = self.util.calculate_sos(
numerator_sos_filters, **general_filters)
numerator_sos = num_num_linear / num_denom_linear if num_denom_linear else 0
denominator_sos_filters = {
row['denominator_type']: row['denominator_value']
}
denom_num_linear, denom_denom_linear = self.util.calculate_sos(
denominator_sos_filters, **general_filters)
denominator_sos = denom_num_linear / denom_denom_linear if denom_denom_linear else 0
if denominator_sos == 0:
index = 0 if numerator_sos == 0 else 1
else:
index = numerator_sos / denominator_sos
self.write_to_db_result(fk=row.kpi_level_2_fk,
numerator_id=row.numerator_id,
numerator_result=num_num_linear,
denominator_id=row.denominator_id,
denominator_result=denom_num_linear,
result=index,
score=index,
context_id=location_type_fk)
self.util.add_kpi_result_to_kpi_results_df([
row.kpi_level_2_fk, row.numerator_id, row.denominator_id,
index, index, None
])
self.util.reset_filtered_scif_and_matches_to_exclusion_all_state()
