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