class HeroSKUAvailabilityByHeroTypeKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(HeroSKUAvailabilityByHeroTypeKpi, 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
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.util.HERO_SKU_AVAILABILITY_BY_HERO_TYPE)
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]
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')
lvl3_ass_res_df['count'] = 1
kpi_res_df = lvl3_ass_res_df.groupby([self.util.HERO_SKU_LABEL, 'entity_fk'],
as_index=False).agg({'numerator_result': np.sum, 'count': np.sum})
kpi_res_df['result'] = kpi_res_df['numerator_result'] / kpi_res_df['count'] * 100
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=res['entity_fk'], denominator_result=res['count'],
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
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 PriceKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(PriceKpi, 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.PRICE_SCENE)
sku_list = self.util.filtered_scif[ScifConsts.PRODUCT_FK]
price_kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.util.PRICE_SCENE)
for sku in sku_list:
self.calculate_hero_sku_price(sku, price_kpi_fk)
self.util.reset_filtered_scif_and_matches_to_exclusion_all_state()
def calculate_hero_sku_price(self, sku, kpi_fk):
price = -1
prices_df = self.util.filtered_matches[((~(self.util.filtered_matches[MatchesConsts.PRICE].isnull())) |
(~(self.util.filtered_matches[MatchesConsts.PROMOTION_PRICE].isnull()))) &
(self.util.filtered_matches[ScifConsts.PRODUCT_FK] == sku)]
if not prices_df.empty:
prices_list = prices_df[MatchesConsts.PRICE].values.tolist()
prices_list.extend(prices_df[MatchesConsts.PROMOTION_PRICE].values.tolist())
prices_list = filter(lambda v: v == v, prices_list)
prices_list = filter(lambda v: v is not None, prices_list)
if prices_list:
price = max(prices_list)
self.write_to_db_result(fk=kpi_fk, numerator_id=sku, denominator_id=sku,result=price)
self.util.add_kpi_result_to_kpi_results_df([kpi_fk, sku, None, price, None, None])
class SosBrandOfSegmentKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(SosBrandOfSegmentKpi, 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.BRAND_SOS_OF_SEGMENT)
self.calculate_brand_out_of_sub_category_sos()
self.util.reset_filtered_scif_and_matches_to_exclusion_all_state()
def calculate_brand_out_of_sub_category_sos(self):
location_type_fk = self.util.all_templates[self.util.all_templates[ScifConsts.LOCATION_TYPE] == 'Primary Shelf'] \
[ScifConsts.LOCATION_TYPE_FK].values[0]
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(
self.util.BRAND_SOS_OF_SEGMENT)
filtered_matches = self.util.filtered_matches[~(
self.util.filtered_matches[ScifConsts.SUB_CATEGORY_FK].isnull())]
products_df = self.util.all_products[[
MatchesConsts.PRODUCT_FK, ScifConsts.BRAND_FK,
ScifConsts.CATEGORY_FK, ScifConsts.PRODUCT_TYPE
]]
filtered_matches = filtered_matches.merge(products_df,
on=MatchesConsts.PRODUCT_FK,
how='left')
sub_cat_df = filtered_matches.groupby(
[ScifConsts.SUB_CATEGORY_FK],
as_index=False).agg({MatchesConsts.WIDTH_MM_ADVANCE: np.sum})
sub_cat_df.rename(
columns={MatchesConsts.WIDTH_MM_ADVANCE: 'sub_cat_len'},
inplace=True)
brand_sub_cat_df = filtered_matches.groupby(
[ScifConsts.BRAND_FK, ScifConsts.SUB_CATEGORY_FK],
as_index=False).agg({MatchesConsts.WIDTH_MM_ADVANCE: np.sum})
brand_sub_cat_df = brand_sub_cat_df.merge(
sub_cat_df, on=ScifConsts.SUB_CATEGORY_FK, how='left')
brand_sub_cat_df['sos'] = brand_sub_cat_df[
MatchesConsts.WIDTH_MM_ADVANCE] / brand_sub_cat_df['sub_cat_len']
for i, row in brand_sub_cat_df.iterrows():
self.write_to_db_result(
fk=kpi_fk,
numerator_id=row[ScifConsts.BRAND_FK],
numerator_result=row[MatchesConsts.WIDTH_MM_ADVANCE],
denominator_id=row[ScifConsts.SUB_CATEGORY_FK],
denominator_result=row['sub_cat_len'],
result=row['sos'] * 100,
context_id=location_type_fk)
self.util.add_kpi_result_to_kpi_results_df([
kpi_fk, row[ScifConsts.BRAND_FK],
row[ScifConsts.SUB_CATEGORY_FK], row['sos'] * 100, None, None
])
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 HeroAvailabilitySkuKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(HeroAvailabilitySkuKpi,
self).__init__(data_provider,
config_params=config_params,
**kwargs)
self.util = PepsicoUtil(None, data_provider)
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_AVAILABILITY_SKU)
self.calculate_kpi_for_main_shelf()
self.util.reset_filtered_scif_and_matches_to_exclusion_all_state()
def kpi_type(self):
pass
def calculate_kpi_for_main_shelf(self):
location_type_fk = self.util.all_templates[self.util.all_templates[ScifConsts.LOCATION_TYPE] == 'Primary Shelf'] \
[ScifConsts.LOCATION_TYPE_FK].values[0]
lvl3_ass_res = self.util.lvl3_ass_result
if lvl3_ass_res.empty:
return
if not self.util.filtered_scif.empty:
products_in_session = self.util.filtered_scif.loc[
self.util.filtered_scif['facings'] > 0]['product_fk'].values
products_df = self.util.all_products[[
ScifConsts.PRODUCT_FK, ScifConsts.MANUFACTURER_FK
]]
lvl3_ass_res.loc[
lvl3_ass_res['product_fk'].isin(products_in_session),
'in_store'] = 1
lvl3_ass_res = lvl3_ass_res.merge(products_df,
on=ScifConsts.PRODUCT_FK,
how='left')
for i, result in lvl3_ass_res.iterrows():
score = result.in_store * 100
custom_res = self.util.commontools.get_yes_no_result(score)
self.write_to_db_result(fk=result.kpi_fk_lvl3,
numerator_id=result.product_fk,
numerator_result=result.in_store,
result=custom_res,
denominator_id=result.manufacturer_fk,
denominator_result=1,
score=score,
context_id=location_type_fk)
self.util.add_kpi_result_to_kpi_results_df([
result['kpi_fk_lvl3'], result['product_fk'],
self.util.store_id, custom_res, score, None
])
class SosVsTargetBrandKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(SosVsTargetBrandKpi, 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.BRAND_SOS)
self.calculate_brand_out_of_category_sos()
self.util.reset_filtered_scif_and_matches_to_exclusion_all_state()
def calculate_brand_out_of_category_sos(self):
location_type_fk = self.util.all_templates[self.util.all_templates[ScifConsts.LOCATION_TYPE] == 'Primary Shelf'] \
[ScifConsts.LOCATION_TYPE_FK].values[0]
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.util.BRAND_SOS)
filtered_scif = self.util.filtered_scif
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)
brand_cat_df = filtered_scif.groupby(
[ScifConsts.BRAND_FK, ScifConsts.CATEGORY_FK],
as_index=False).agg({'updated_gross_length': np.sum})
brand_cat_df = brand_cat_df.merge(category_df,
on=ScifConsts.CATEGORY_FK,
how='left')
brand_cat_df['sos'] = brand_cat_df[
'updated_gross_length'] / brand_cat_df['cat_len']
for i, row in brand_cat_df.iterrows():
self.write_to_db_result(
fk=kpi_fk,
numerator_id=row[ScifConsts.BRAND_FK],
numerator_result=row['updated_gross_length'],
denominator_id=row[ScifConsts.CATEGORY_FK],
denominator_result=row['cat_len'],
result=row['sos'] * 100,
context_id=location_type_fk)
self.util.add_kpi_result_to_kpi_results_df([
kpi_fk, row[ScifConsts.BRAND_FK], row[ScifConsts.CATEGORY_FK],
row['sos'] * 100, None, None
])
class BrandFullBayKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(BrandFullBayKpi, 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.BRAND_FULL_BAY)
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.util.BRAND_FULL_BAY)
external_kpi_targets = self.util.commontools.all_targets_unpacked[
self.util.commontools.all_targets_unpacked['kpi_level_2_fk'] == kpi_fk]
external_kpi_targets = external_kpi_targets.reset_index(drop=True)
if not external_kpi_targets.empty:
external_kpi_targets['group_fk'] = external_kpi_targets['Group Name'].apply(lambda x:
self.util.custom_entities[
self.util.custom_entities[
'name'] == x][
'pk'].values[0])
filtered_matches = self.util.filtered_matches[~(self.util.filtered_matches['bay_number'] == -1)]
if not filtered_matches.empty:
scene_bay_product = filtered_matches.groupby(['scene_fk', 'bay_number', 'product_fk'],
as_index=False).agg({'count': np.sum})
scene_bay_product = scene_bay_product.merge(self.util.all_products, on='product_fk', how='left')
scene_bay = scene_bay_product.groupby(['scene_fk', 'bay_number'], as_index=False).agg({'count': np.sum})
scene_bay.rename(columns={'count': 'total_facings'}, inplace=True)
for i, row in external_kpi_targets.iterrows():
filters = self.util.get_full_bay_and_positional_filters(row)
brand_relevant_df = scene_bay_product[
self.util.toolbox.get_filter_condition(scene_bay_product, **filters)]
result_df = brand_relevant_df.groupby(['scene_fk', 'bay_number'], as_index=False).agg(
{'count': np.sum})
result_df = result_df.merge(scene_bay, on=['scene_fk', 'bay_number'], how='left')
result_df['ratio'] = result_df['count'] / result_df['total_facings']
target_ratio = float(self._config_params['ratio'])
result = len(result_df[result_df['ratio'] >= target_ratio])
self.write_to_db_result(fk=row['kpi_level_2_fk'], numerator_id=row['group_fk'], result=result,
score=result, target=target_ratio*100)
self.util.add_kpi_result_to_kpi_results_df(
[row['kpi_level_2_fk'], row['group_fk'], None, None, result, None])
self.util.reset_filtered_scif_and_matches_to_exclusion_all_state()
class SosVsTargetSegmentKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(SosVsTargetSegmentKpi, self).__init__(data_provider, config_params=config_params, **kwargs)
self.util = PepsicoUtil(None, data_provider)
def kpi_type(self):
pass
def calculate(self):
# sos_targets = self.util.sos_vs_target_targets.copy()
# sos_targets = sos_targets[sos_targets['type'] == self._config_params['kpi_type']]
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.PEPSICO_SEGMENT_SOS)
# self.calculate_pepsico_segment_space_sos_vs_target(sos_targets)
self.calculate_pepsico_segment_space_sos()
self.util.reset_filtered_scif_and_matches_to_exclusion_all_state()
def calculate_pepsico_segment_space_sos(self):
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.util.PEPSICO_SEGMENT_SOS)
filtered_matches = self.util.filtered_matches
products_df = self.util.all_products[[MatchesConsts.PRODUCT_FK, ScifConsts.BRAND_FK, ScifConsts.CATEGORY_FK]]
filtered_matches = filtered_matches.merge(products_df, on=MatchesConsts.PRODUCT_FK, how='left')
cat_df = filtered_matches.groupby([ScifConsts.CATEGORY_FK],
as_index=False).agg({MatchesConsts.WIDTH_MM_ADVANCE: np.sum})
cat_df.rename(columns={MatchesConsts.WIDTH_MM_ADVANCE: 'cat_len'}, inplace=True)
# filtered_scif = filtered_scif[filtered_scif[ScifConsts.MANUFACTURER_FK] == self.util.own_manuf_fk]
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_matches.empty:
sub_cat_df = filtered_matches.groupby([ScifConsts.SUB_CATEGORY_FK, ScifConsts.CATEGORY_FK],
as_index=False).agg({MatchesConsts.WIDTH_MM_ADVANCE: np.sum})
if not sub_cat_df.empty:
sub_cat_df = sub_cat_df.merge(cat_df, on=ScifConsts.CATEGORY_FK, how='left')
sub_cat_df['sos'] = sub_cat_df[MatchesConsts.WIDTH_MM_ADVANCE] / sub_cat_df['cat_len']
for i, row in sub_cat_df.iterrows():
self.write_to_db_result(fk=kpi_fk, numerator_id=row[ScifConsts.SUB_CATEGORY_FK],
numerator_result=row[MatchesConsts.WIDTH_MM_ADVANCE],
denominator_id=row[ScifConsts.CATEGORY_FK],
denominator_result=row['cat_len'], result=row['sos'] * 100,
context_id=location_type_fk)
self.util.add_kpi_result_to_kpi_results_df(
[kpi_fk, row[ScifConsts.SUB_CATEGORY_FK], row[ScifConsts.CATEGORY_FK], row['sos'] * 100,
None, None])
class CategoryFullBayKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(CategoryFullBayKpi, 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.CATEGORY_FULL_BAY)
category_fk = self.util.all_products[self.util.all_products[ProductsConsts.CATEGORY] == self.util.CSN]\
[ProductsConsts.CATEGORY_FK].values[0]
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.util.CATEGORY_FULL_BAY)
filtered_matches = self.util.filtered_matches[~(self.util.filtered_matches[MatchesConsts.BAY_NUMBER] == -1)]
if not filtered_matches.empty:
scene_bay_product = filtered_matches.groupby([MatchesConsts.SCENE_FK, MatchesConsts.BAY_NUMBER,
ScifConsts.PRODUCT_FK], as_index=False).agg({'count': np.sum})
scene_bay_product = scene_bay_product.merge(self.util.all_products, on=ProductsConsts.PRODUCT_FK, how='left')
scene_bay = scene_bay_product.groupby([MatchesConsts.SCENE_FK, MatchesConsts.BAY_NUMBER],
as_index=False).agg({'count': np.sum})
scene_bay.rename(columns={'count': 'total_facings'}, inplace=True)
cat_relevant_df = scene_bay_product[scene_bay_product[ProductsConsts.CATEGORY_FK] == category_fk]
result_df = cat_relevant_df.groupby([MatchesConsts.SCENE_FK, MatchesConsts.BAY_NUMBER],
as_index=False).agg({'count': np.sum})
result_df = result_df.merge(scene_bay, on=[MatchesConsts.SCENE_FK, MatchesConsts.BAY_NUMBER], how='left')
result_df['ratio'] = result_df['count'] / result_df['total_facings']
target_ratio = float(self._config_params['ratio'])
result = len(result_df[result_df['ratio'] >= target_ratio])
self.write_to_db_result(fk=kpi_fk, numerator_id=category_fk, denominator_id=self.util.store_id,
score=result, result=result, target=float(self._config_params['ratio'])*100)
self.util.add_kpi_result_to_kpi_results_df([kpi_fk, category_fk, self.util.store_id, result, result, None])
self.util.reset_filtered_scif_and_matches_to_exclusion_all_state()
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()
class HeroSOSofCategoryByHeroTypeKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(HeroSOSofCategoryByHeroTypeKpi,
self).__init__(data_provider,
config_params=config_params,
**kwargs)
self.util = PepsicoUtil(None, data_provider)
def kpi_type(self):
pass
def calculate(self):
hero_sos_kpi_results = self.dependencies_data
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(
self.util.HERO_SKU_SOS_OF_CAT_BY_HERO_TYPE)
if not hero_sos_kpi_results.empty:
category_len_df = hero_sos_kpi_results.drop_duplicates(
subset=['denominator_id'])
category_len_df = category_len_df[[
'denominator_id', 'denominator_result'
]]
location_type_fk = self.util.scif[self.util.scif[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
]]
hero_sos_kpi_results = hero_sos_kpi_results.merge(
product_hero_df,
left_on='numerator_id',
right_on=ScifConsts.PRODUCT_FK,
how='left')
hero_sos_kpi_results = hero_sos_kpi_results.merge(
self.util.hero_type_custom_entity_df,
left_on=self.util.HERO_SKU_LABEL,
right_on='name',
how='left')
hero_type_by_cat = hero_sos_kpi_results.groupby([self.util.HERO_SKU_LABEL, 'entity_fk',
'denominator_id'], as_index=False).\
agg({'numerator_result': np.sum})
hero_type_by_cat = hero_type_by_cat.merge(category_len_df,
on='denominator_id',
how='left')
hero_type_by_cat['sos'] = hero_type_by_cat[
'numerator_result'] / hero_type_by_cat[
'denominator_result'] * 100
hero_type_by_cat['score'] = hero_type_by_cat['sos'].apply(
lambda x: 100 if x >= 100 else 0)
for i, res in hero_type_by_cat.iterrows():
self.write_to_db_result(
fk=kpi_fk,
score=res['score'],
result=res['sos'],
numerator_id=res['entity_fk'],
denominator_id=res['denominator_id'],
numerator_result=res['numerator_result'],
denominator_result=res['denominator_result'],
context_id=location_type_fk)
self.util.add_kpi_result_to_kpi_results_df([
kpi_fk, res['entity_fk'], res['denominator_id'],
res['sos'], res['score'], location_type_fk
])
class FacingsPerProductKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(FacingsPerProductKpi, 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.FACINGS_PER_PRODUCT)
if not self.util.filtered_matches.empty:
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(
self.util.FACINGS_PER_PRODUCT)
filtered_matches = self.util.filtered_matches.copy()
shelves_arr = np.sort(filtered_matches[
MatchesConsts.SHELF_NUMBER].unique()).tolist()
filtered_matches[MatchesConsts.SHELF_NUMBER] = filtered_matches[MatchesConsts.SHELF_NUMBER]. \
apply(lambda x: shelves_arr.index(x)+1)
filtered_matches = filtered_matches.merge(
self.util.all_products,
on=MatchesConsts.PRODUCT_FK,
how='left')
filtered_matches.loc[filtered_matches['att1'] == 'display cardboard box', 'count'] = \
filtered_matches['count'] * 3
result_df = filtered_matches.groupby([
MatchesConsts.PRODUCT_FK, MatchesConsts.SHELF_NUMBER,
MatchesConsts.BAY_NUMBER
],
as_index=False).agg(
{'count': np.sum})
shelves_cust_entity = self.util.custom_entities[
self.util.custom_entities['entity_type'] == 'shelf_number']
shelves_cust_entity['name'] = shelves_cust_entity['name'].astype(
int)
bays_cust_entity = self.util.custom_entities[
self.util.custom_entities['entity_type'] == 'bay_number']
bays_cust_entity['name'] = bays_cust_entity['name'].astype(int)
result_df = result_df.merge(shelves_cust_entity,
left_on=MatchesConsts.SHELF_NUMBER,
right_on='name',
how='left')
result_df.rename(columns={'pk': 'shelf_fk'}, inplace=True)
result_df = result_df.merge(bays_cust_entity,
left_on=MatchesConsts.BAY_NUMBER,
right_on='name',
how='left')
result_df.rename(columns={'pk': 'bay_fk'}, inplace=True)
for i, row in result_df.iterrows():
self.write_to_db_result(
fk=kpi_fk,
numerator_result=row[MatchesConsts.SHELF_NUMBER],
result=row['count'],
numerator_id=row[MatchesConsts.PRODUCT_FK],
denominator_id=row[MatchesConsts.PRODUCT_FK],
denominator_result=row[MatchesConsts.BAY_NUMBER],
context_id=row['shelf_fk'],
by_scene=True)
self.util.add_kpi_result_to_kpi_results_df([
kpi_fk, row[MatchesConsts.PRODUCT_FK], row['bay_fk'],
row['count'], None, row['shelf_fk']
])
self.util.reset_filtered_scif_and_matches_to_exclusion_all_state()
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
