class SecondaryPriceMechanicKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(SecondaryPriceMechanicKpi,
self).__init__(data_provider,
config_params=config_params,
**kwargs)
self.util = PepsicoUtil(None, data_provider)
self.kpi_name = self._config_params['kpi_type']
def kpi_type(self):
pass
def calculate(self):
self.util.filtered_scif_secondary, self.util.filtered_matches_secondary = \
self.util.commontools.set_filtered_scif_and_matches_for_specific_kpi(self.util.filtered_scif_secondary,
self.util.filtered_matches_secondary,
self.kpi_name)
filtered_matches = self.util.filtered_matches_secondary.merge(
self.util.all_products, on=[ScifConsts.PRODUCT_FK], how='left')
filtered_matches = filtered_matches[filtered_matches[ScifConsts.PRODUCT_TYPE] \
== 'POS'].drop_duplicates(subset=[ScifConsts.PRODUCT_FK])
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.kpi_name)
for i, row in filtered_matches.iterrows():
self.write_to_db_result(fk=kpi_fk,
numerator_id=row[ScifConsts.PRODUCT_FK],
denominator_id=row['display_id'],
denominator_result=row['display_id'],
context_id=row['store_area_fk'],
result=1)
self.util.reset_secondary_filtered_scif_and_matches_to_exclusion_all_state(
)
class SecondarySosBrandOfSegmentKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(SecondarySosBrandOfSegmentKpi,
self).__init__(data_provider,
config_params=config_params,
**kwargs)
self.util = PepsicoUtil(None, data_provider)
self.kpi_name = self._config_params['kpi_type']
def kpi_type(self):
pass
def calculate(self):
self.util.filtered_scif_secondary, self.util.filtered_matches_secondary = \
self.util.commontools.set_filtered_scif_and_matches_for_specific_kpi(self.util.filtered_scif_secondary,
self.util.filtered_matches_secondary,
self.kpi_name)
self.calculate_brand_out_of_sub_category_sos()
self.util.reset_secondary_filtered_scif_and_matches_to_exclusion_all_state(
)
def calculate_brand_out_of_sub_category_sos(self):
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.kpi_name)
filtered_matches = self.util.filtered_matches_secondary
filtered_matches = filtered_matches[~(
filtered_matches[ScifConsts.SUB_CATEGORY_FK].isnull())]
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')
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)
class SecondarySOSBrandofCategoryKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(SecondarySOSBrandofCategoryKpi,
self).__init__(data_provider,
config_params=config_params,
**kwargs)
self.util = PepsicoUtil(None, data_provider)
self.kpi_name = self._config_params['kpi_type']
def kpi_type(self):
pass
def calculate(self):
self.util.filtered_scif_secondary, self.util.filtered_matches_secondary = \
self.util.commontools.set_filtered_scif_and_matches_for_specific_kpi(self.util.filtered_scif_secondary,
self.util.filtered_matches_secondary,
self.kpi_name)
self.calculate_brand_out_of_category_sos()
self.util.reset_secondary_filtered_scif_and_matches_to_exclusion_all_state(
)
def calculate_brand_out_of_category_sos(self):
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.kpi_name)
filtered_scif = self.util.filtered_scif_secondary
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)
class SeondaryPromoPriceKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(SeondaryPromoPriceKpi,
self).__init__(data_provider,
config_params=config_params,
**kwargs)
self.util = PepsicoUtil(None, data_provider)
self.kpi_name = self._config_params['kpi_type']
def kpi_type(self):
pass
def calculate(self):
self.util.filtered_scif_secondary, self.util.filtered_matches_secondary = \
self.util.commontools.set_filtered_scif_and_matches_for_specific_kpi(self.util.filtered_scif_secondary,
self.util.filtered_matches_secondary,
self.kpi_name)
filtered_matches = self.util.filtered_matches_secondary
if not filtered_matches.empty:
product_display = filtered_matches.drop_duplicates(
subset=[MatchesConsts.PRODUCT_FK, 'display_id'])
store_area = self.util.filtered_scif_secondary[
'store_area_fk'].values[0]
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.kpi_name)
for i, row in product_display.iterrows():
price = 0
prices_df = filtered_matches[(
~(filtered_matches[MatchesConsts.PROMOTION_PRICE].isnull())
) & (filtered_matches[ScifConsts.PRODUCT_FK] == row[
MatchesConsts.PRODUCT_FK]) &
(filtered_matches['display_id']
== row['display_id'])]
if not prices_df.empty:
price = 1
result = self.util.commontools.get_yes_no_result(price)
self.write_to_db_result(
fk=kpi_fk,
numerator_id=row[MatchesConsts.PRODUCT_FK],
denominator_id=row[MatchesConsts.PRODUCT_FK],
denominator_result=row['display_id'],
context_id=store_area,
result=result)
self.util.reset_secondary_filtered_scif_and_matches_to_exclusion_all_state(
)
class SecondaryHeroLengthByHeroTypeKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(SecondaryHeroLengthByHeroTypeKpi,
self).__init__(data_provider,
config_params=config_params,
**kwargs)
self.util = PepsicoUtil(None, data_provider)
self.kpi_name = self._config_params['kpi_type']
def calculate(self):
self.util.filtered_scif_secondary, self.util.filtered_matches_secondary = \
self.util.commontools.set_filtered_scif_and_matches_for_specific_kpi(self.util.filtered_scif_secondary,
self.util.filtered_matches_secondary,
self.kpi_name)
total_skus_in_ass = len(self.util.lvl3_ass_result)
if not total_skus_in_ass:
self.util.reset_secondary_filtered_scif_and_matches_to_exclusion_all_state(
)
return
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.kpi_name)
lvl3_ass_res_df = self.dependencies_data
if lvl3_ass_res_df.empty and total_skus_in_ass:
self.write_to_db_result(fk=kpi_fk,
numerator_id=self.util.own_manuf_fk,
result=0,
score=0,
denominator_id=self.util.store_id)
self.util.reset_secondary_filtered_scif_and_matches_to_exclusion_all_state(
)
return
if not lvl3_ass_res_df.empty:
available_hero_list = lvl3_ass_res_df[(lvl3_ass_res_df['numerator_result'] == 1)] \
['numerator_id'].unique().tolist()
filtered_scif = self.util.filtered_scif_secondary[self.util.filtered_scif_secondary[ScifConsts.PRODUCT_FK].\
isin(available_hero_list)]
result_df = filtered_scif.groupby([self.util.HERO_SKU_LABEL],
as_index=False).agg({
'updated_gross_length':
np.sum
})
result_df = result_df.merge(self.util.hero_type_custom_entity_df,
left_on=self.util.HERO_SKU_LABEL,
right_on='name',
how='left')
for i, row in result_df.iterrows():
self.write_to_db_result(fk=kpi_fk,
numerator_id=row['entity_fk'],
denominator_id=row['entity_fk'],
result=row['updated_gross_length'],
score=row['updated_gross_length'])
# add a function that resets to secondary scif and matches
self.util.reset_secondary_filtered_scif_and_matches_to_exclusion_all_state(
)
def kpi_type(self):
pass
class SecondaryLinearSpacePerProductKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(SecondaryLinearSpacePerProductKpi,
self).__init__(data_provider,
config_params=config_params,
**kwargs)
self.util = PepsicoUtil(None, data_provider)
self.kpi_name = self._config_params['kpi_type']
def kpi_type(self):
pass
def calculate(self):
self.util.filtered_scif_secondary, self.util.filtered_matches_secondary = \
self.util.commontools.set_filtered_scif_and_matches_for_specific_kpi(self.util.filtered_scif_secondary,
self.util.filtered_matches_secondary,
self.kpi_name)
if not self.util.filtered_matches_secondary.empty:
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.kpi_name)
filtered_matches = self.util.filtered_matches_secondary.copy()
store_area = self.util.filtered_scif_secondary[
'store_area_fk'].values[0]
result_df = filtered_matches.groupby(
[MatchesConsts.PRODUCT_FK, 'display_id'],
as_index=False).agg({MatchesConsts.WIDTH_MM_ADVANCE: np.sum})
for i, row in result_df.iterrows():
self.write_to_db_result(
fk=kpi_fk,
numerator_result=row[MatchesConsts.WIDTH_MM_ADVANCE],
result=row[MatchesConsts.WIDTH_MM_ADVANCE],
numerator_id=row[MatchesConsts.PRODUCT_FK],
denominator_id=row[MatchesConsts.PRODUCT_FK],
denominator_result=row['display_id'],
context_id=store_area)
self.util.reset_secondary_filtered_scif_and_matches_to_exclusion_all_state(
)
class SecondaryAllHeroSOSofCSNCategoryKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(SecondaryAllHeroSOSofCSNCategoryKpi, self).__init__(data_provider, config_params=config_params, **kwargs)
self.util = PepsicoUtil(None, data_provider)
self.kpi_name = self._config_params['kpi_type']
def calculate(self):
# pass secondary scif and matches
self.util.filtered_scif_secondary, self.util.filtered_matches_secondary = \
self.util.commontools.set_filtered_scif_and_matches_for_specific_kpi(self.util.filtered_scif_secondary,
self.util.filtered_matches_secondary,
self.kpi_name)
total_skus_in_ass = len(self.util.lvl3_ass_result)
if not total_skus_in_ass:
self.util.reset_secondary_filtered_scif_and_matches_to_exclusion_all_state()
return
lvl3_ass_res_df = self.dependencies_data
if lvl3_ass_res_df.empty:
self.util.reset_secondary_filtered_scif_and_matches_to_exclusion_all_state()
return
# talk to Nitzan
if not lvl3_ass_res_df.empty:
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.kpi_name)
filtered_scif = self.util.filtered_scif_secondary
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)
available_hero_list = lvl3_ass_res_df[(lvl3_ass_res_df['numerator_result'] == 1)] \
['numerator_id'].unique().tolist()
filtered_scif = filtered_scif[filtered_scif[ScifConsts.PRODUCT_FK].isin(available_hero_list)]
all_hero_cat_df = filtered_scif.groupby([ScifConsts.CATEGORY_FK], as_index=False).\
agg({'updated_gross_length': np.sum})
all_hero_cat_df = all_hero_cat_df.merge(category_df, on=ScifConsts.CATEGORY_FK, how='left')
all_hero_cat_df['cat_len'] = all_hero_cat_df['cat_len'].fillna(0)
if not all_hero_cat_df.empty:
all_hero_cat_df['sos'] = all_hero_cat_df.apply(self.calculate_sos, axis=1)
for i, row in all_hero_cat_df.iterrows():
self.write_to_db_result(fk=kpi_fk, numerator_id=self.util.own_manuf_fk,
numerator_result=row['updated_gross_length'],
denominator_id=row[ScifConsts.CATEGORY_FK],
denominator_result=row['cat_len'], result=row['sos'])
# add a function that resets to secondary scif and matches
self.util.reset_secondary_filtered_scif_and_matches_to_exclusion_all_state()
def kpi_type(self):
pass
@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 SecondaryHeroTotalLengthKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(SecondaryHeroTotalLengthKpi,
self).__init__(data_provider,
config_params=config_params,
**kwargs)
self.util = PepsicoUtil(None, data_provider)
self.kpi_name = self._config_params['kpi_type']
def calculate(self):
# pass secondary scif and matches
self.util.filtered_scif_secondary, self.util.filtered_matches_secondary = \
self.util.commontools.set_filtered_scif_and_matches_for_specific_kpi(self.util.filtered_scif_secondary,
self.util.filtered_matches_secondary,
self.kpi_name)
total_skus_in_ass = len(self.util.lvl3_ass_result)
if not total_skus_in_ass:
self.util.reset_secondary_filtered_scif_and_matches_to_exclusion_all_state(
)
return
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.kpi_name)
lvl3_ass_res_df = self.dependencies_data
if lvl3_ass_res_df.empty:
self.write_to_db_result(fk=kpi_fk,
numerator_id=self.util.own_manuf_fk,
result=0,
score=0,
denominator_id=self.util.store_id)
self.util.reset_secondary_filtered_scif_and_matches_to_exclusion_all_state(
)
return
if not lvl3_ass_res_df.empty:
available_hero_list = lvl3_ass_res_df[(lvl3_ass_res_df['numerator_result'] == 1)] \
['numerator_id'].unique().tolist()
hero_len = self.util.filtered_scif_secondary[self.util.filtered_scif_secondary[ScifConsts.PRODUCT_FK].isin(available_hero_list)]\
['updated_gross_length'].sum()
self.write_to_db_result(fk=kpi_fk,
numerator_id=self.util.own_manuf_fk,
denominator_id=self.util.store_id,
result=hero_len,
score=hero_len)
# add a function that resets to secondary scif and matches
self.util.reset_secondary_filtered_scif_and_matches_to_exclusion_all_state(
)
def kpi_type(self):
pass
