class HeroAvailabilityKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(HeroAvailabilityKpi, self).__init__(data_provider,
config_params=config_params,
**kwargs)
self.util = PepsicoUtil(None, data_provider)
def calculate(self):
lvl3_ass_res_df = self.dependencies_data
distribution_kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(
self.util.HERO_SKU_AVAILABILITY)
if not lvl3_ass_res_df.empty:
location_type_fk = self.util.scif[self.util.scif[ScifConsts.LOCATION_TYPE] == 'Primary Shelf'] \
[ScifConsts.LOCATION_TYPE_FK].values[0]
total_skus_in_ass = len(lvl3_ass_res_df)
in_store_skus = len(
self.util.get_available_hero_sku_list(self.dependencies_data))
res = np.divide(float(in_store_skus),
float(total_skus_in_ass)) * 100
score = 100 if res >= 100 else 0
self.write_to_db_result(fk=distribution_kpi_fk,
numerator_id=self.util.own_manuf_fk,
numerator_result=in_store_skus,
result=res,
denominator_id=self.util.store_id,
denominator_result=total_skus_in_ass,
score=score,
context_id=location_type_fk)
self.util.add_kpi_result_to_kpi_results_df([
distribution_kpi_fk, self.util.own_manuf_fk,
self.util.store_id, res, score, None
])
def kpi_type(self):
pass
class SosVsTargetHeroSkuKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(SosVsTargetHeroSkuKpi, 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.util.HERO_SKU_SOS)
self.calculate_hero_sku_sos()
self.util.reset_filtered_scif_and_matches_to_exclusion_all_state()
def calculate_hero_sku_sos(self):
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.util.HERO_SKU_SOS)
filtered_scif = self.util.filtered_scif
location_type_fk = self.util.all_templates[self.util.all_templates[ScifConsts.LOCATION_TYPE] == 'Primary Shelf'] \
[ScifConsts.LOCATION_TYPE_FK].values[0]
if (not filtered_scif.empty) and (not self.dependencies_data.empty):
category_df = filtered_scif.groupby([ScifConsts.CATEGORY_FK],
as_index=False).agg({'updated_gross_length': np.sum})
category_df.rename(columns={'updated_gross_length': 'cat_len'}, inplace=True)
av_hero_list = self.util.get_available_hero_sku_list(self.dependencies_data)
filtered_scif = filtered_scif[filtered_scif[ScifConsts.PRODUCT_FK].isin(av_hero_list)]
unav_hero_list = self.util.get_unavailable_hero_sku_list(self.dependencies_data)
unav_hero_df = self.util.all_products[self.util.all_products[ScifConsts.PRODUCT_FK].isin(unav_hero_list)] \
[[ScifConsts.PRODUCT_FK, ScifConsts.CATEGORY_FK]]
unav_hero_df['updated_gross_length'] = 0
filtered_scif = filtered_scif.append(unav_hero_df)
hero_cat_df = filtered_scif.groupby([ScifConsts.PRODUCT_FK, ScifConsts.CATEGORY_FK],
as_index=False).agg({'updated_gross_length': np.sum})
hero_cat_df = hero_cat_df.merge(category_df, on=ScifConsts.CATEGORY_FK, how='left')
hero_cat_df['cat_len'] = hero_cat_df['cat_len'].fillna(0)
hero_cat_df['sos'] = hero_cat_df.apply(self.calculate_sos, axis=1)
for i, row in hero_cat_df.iterrows():
self.write_to_db_result(fk=kpi_fk, numerator_id=row[ScifConsts.PRODUCT_FK],
numerator_result=row['updated_gross_length'],
denominator_id=row[ScifConsts.CATEGORY_FK],
denominator_result=row['cat_len'], result=row['sos'],
context_id=location_type_fk)
self.util.add_kpi_result_to_kpi_results_df(
[kpi_fk, row[ScifConsts.PRODUCT_FK], row[ScifConsts.CATEGORY_FK], row['sos'], None, None])
@staticmethod
def calculate_sos(row):
sos = 0
if row['cat_len'] != 0:
sos = float(row['updated_gross_length']) / row['cat_len'] * 100
return sos
class ShareOfAssortmentByHeroTypeKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(ShareOfAssortmentByHeroTypeKpi,
self).__init__(data_provider,
config_params=config_params,
**kwargs)
self.util = PepsicoUtil(None, data_provider)
def calculate(self):
lvl3_ass_res_df = self.dependencies_data
if not lvl3_ass_res_df.empty:
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(
self.util.SHARE_OF_ASSORTMENT_BY_HERO_TYPE)
in_store_skus = len(
self.util.get_available_hero_sku_list(
self.dependencies_data)) # total recognized
location_type_fk = self.util.all_templates[self.util.all_templates[ScifConsts.LOCATION_TYPE] == 'Primary Shelf']\
[ScifConsts.LOCATION_TYPE_FK].values[0]
product_hero_df = self.util.all_products[[
ScifConsts.PRODUCT_FK, self.util.HERO_SKU_LABEL
]]
lvl3_ass_res_df = lvl3_ass_res_df.merge(
product_hero_df,
left_on='numerator_id',
right_on=ScifConsts.PRODUCT_FK,
how='left')
lvl3_ass_res_df = lvl3_ass_res_df.merge(
self.util.hero_type_custom_entity_df,
left_on=self.util.HERO_SKU_LABEL,
right_on='name',
how='left')
kpi_res_df = lvl3_ass_res_df.groupby(
[self.util.HERO_SKU_LABEL, 'entity_fk'],
as_index=False).agg({'numerator_result': np.sum})
kpi_res_df['in_store_total'] = in_store_skus
kpi_res_df['result'] = kpi_res_df.apply(self.get_result, axis=1)
kpi_res_df['score'] = kpi_res_df['result'].apply(
lambda x: 100 if x >= 100 else 0)
for i, res in kpi_res_df.iterrows():
self.write_to_db_result(
fk=kpi_fk,
numerator_id=res['entity_fk'],
numerator_result=res['numerator_result'],
result=res['result'],
denominator_id=self.util.store_id,
denominator_result=res['in_store_total'],
score=res['score'],
context_id=location_type_fk)
self.util.add_kpi_result_to_kpi_results_df([
kpi_fk, res['entity_fk'], res['entity_fk'], res['result'],
res['score'], location_type_fk
])
def kpi_type(self):
pass
@staticmethod
def get_result(row):
rv = float(
row['numerator_result']
) / row['in_store_total'] * 100 if row['in_store_total'] else 0
return rv
