def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.common = Common(self.data_provider)
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.current_date = datetime.now()
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_info = self.data_provider[Data.STORE_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.own_manufacturer_id = int(self.data_provider[Data.OWN_MANUFACTURER][self.data_provider[Data.OWN_MANUFACTURER]['param_name'] == 'manufacturer_id']['param_value'].tolist()[0])
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.rds_conn = PSProjectConnector(self.project_name, DbUsers.CalculationEng)
self.toolbox = GENERALToolBox(data_provider)
self.assortment = Assortment(self.data_provider, self.output, common=self.common)
self.kpi_static_data = self.common.get_kpi_static_data()
self.kpi_results_queries = []
self.template_path = self.get_template_path()
self.template_data = self.get_template_data()
self.sos_store_policies = self.get_sos_store_policies(self.visit_date.strftime('%Y-%m-%d'))
self.result_values = self.get_result_values()
self.scores = pd.DataFrame()
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.ps_data_provider = PsDataProvider(data_provider)
self.kpi_result_values = self.ps_data_provider.get_result_values()
self.common_v2 = Common(self.data_provider)
self.store_id = self.data_provider[Data.STORE_FK]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.assortment = Assortment(self.data_provider)
self.own_manufacturer_fk = int(
self.data_provider.own_manufacturer.param_value.values[0])
self.kpi_static_data = self.common_v2.kpi_static_data[[
'pk', StaticKpis.TYPE
]]
# self.custom_entity = self.ps_data_provider.get_custom_entities(Consts.PRODUCT_GROUP)
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.project_name = self.data_provider.project_name
self.rds_conn = PSProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.external_targets = self._retrieve_completeness_external_targets()
self.products_trax_cat = self._get_products_with_trax_categories()
self.ass_groups_present = {
Consts.DISTR_SNACKS: 0,
Consts.DISTR_SABRA: 0
}
def __init__(self, data_provider, output):
self.k_engine = BaseCalculationsScript(data_provider, output)
self.output = output
self.data_provider = data_provider
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.store_info = self.data_provider[Data.STORE_INFO]
self.store_type = self.store_info['store_type'].iloc[0]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.rds_conn = PSProjectConnector(self.project_name, DbUsers.CalculationEng)
self.general_tools = CCKHGENERALToolBox(self.data_provider, self.output)
self.template = CCKHTemplateConsts()
self.kpi_static_data = self.get_kpi_static_data()
self.kpi_results_queries = []
self.commonV2 = CommonV2(self.data_provider)
self.kpi_new_static_data = self.commonV2.get_new_kpi_static_data()
self.manufacturer = int(self.data_provider.own_manufacturer.param_value.values[0])
self.ps_data_provider = PsDataProvider(self.data_provider, self.output)
self.external_targets = self.ps_data_provider.get_kpi_external_targets()
self.assortment = Assortment(self.data_provider, self.output)
self.templates_info = self.external_targets[self.external_targets[CCKHTemplateConsts.TEMPLATE_OPERATION] ==
CCKHTemplateConsts.BASIC_SHEET]
self.visibility_info = self.external_targets[self.external_targets[CCKHTemplateConsts.TEMPLATE_OPERATION]
== CCKHTemplateConsts.VISIBILITY_SHEET]
self.cooler_info = self.external_targets[self.external_targets[CCKHTemplateConsts.TEMPLATE_OPERATION]
== CCKHTemplateConsts.COOLER_SHEET]
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.session_id = self.data_provider.session_id
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.rds_conn = PSProjectConnector(self.project_name,
DbUsers.CalculationEng)
# self.tools = RISPARKWINEDEGENERALToolBox(
# self.data_provider, self.output, rds_conn=self.rds_conn)
# self.New_kpi_static_data = self.get_new_kpi_static_data()
self.kpi_results_new_tables_queries = []
# self.store_assortment = PSAssortmentDataProvider(self.data_provider).execute()
self.store_info = self.data_provider[Data.STORE_INFO]
self.current_date = datetime.now()
self.assortment = Assortment(self.data_provider, self.output)
self.store_assortment = self.assortment.store_assortment
self.common = Common(self.data_provider)
def __init__(self, data_provider, output, common):
self.output = output
self.data_provider = data_provider
self.common = common
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.session_id = self.data_provider.session_id
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.scene_results = self.data_provider[Data.SCENE_KPI_RESULTS]
self.templates = self.data_provider[Data.TEMPLATES]
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.store_info = self.data_provider[Data.STORE_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.scif['store_fk'] = self.store_id
self.scif = self.scif[~(self.scif['product_type'].isin([Const.IRRELEVANT, Const.EMPTY]))]
self.scif = self.scif[self.scif['facings'] > 0]
self.rds_conn = PSProjectConnector(self.project_name, DbUsers.CalculationEng)
self.kpis = self.load_kpis()
self.results_values = self.load_results_values()
self.kpi_static_data = self.common.get_kpi_static_data()
self.manufacturer_fk = int(self.data_provider[Data.OWN_MANUFACTURER].iloc[0, 1])
self.kpi_results = []
self.assortment = Assortment(self.data_provider, self.output)
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.common = Common(self.data_provider)
self.common_v2 = CommonV2(self.data_provider)
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.template_info = self.data_provider.all_templates
self.rds_conn = ProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.ps_data_provider = PsDataProvider(self.data_provider)
self.thresholds_and_results = {}
self.result_df = []
self.writing_to_db_time = datetime.timedelta(0)
self.kpi_results_queries = []
self.potential_products = {}
self.shelf_square_boundaries = {}
self.average_shelf_values = {}
self.kpi_static_data = self.common.get_kpi_static_data()
self.kpi_results_queries = []
self.all_template_data = parse_template(TEMPLATE_PATH, "KPI")
self.spacing_template_data = parse_template(TEMPLATE_PATH, "Spacing")
self.fixture_width_template = pd.read_excel(FIXTURE_WIDTH_TEMPLATE,
"Fixture Width",
dtype=pd.Int64Dtype())
self.facings_to_feet_template = pd.read_excel(FIXTURE_WIDTH_TEMPLATE,
"Conversion Table",
dtype=pd.Int64Dtype())
self.header_positions_template = pd.read_excel(FIXTURE_WIDTH_TEMPLATE,
"Header Positions")
self.flip_sign_positions_template = pd.read_excel(
FIXTURE_WIDTH_TEMPLATE, "Flip Sign Positions")
self.custom_entity_data = self.ps_data_provider.get_custom_entities(
1005)
self.ignore_stacking = False
self.facings_field = 'facings' if not self.ignore_stacking else 'facings_ign_stack'
self.INCLUDE_FILTER = 1
self.assortment = Assortment(self.data_provider,
output=self.output,
ps_data_provider=self.ps_data_provider)
self.store_assortment = self.assortment.get_lvl3_relevant_ass()
self.kpi_new_static_data = self.common.get_new_kpi_static_data()
try:
self.mpis = self.match_product_in_scene.merge(self.products, on='product_fk', suffixes=['', '_p']) \
.merge(self.scene_info, on='scene_fk', suffixes=['', '_s']) \
.merge(self.template_info, on='template_fk', suffixes=['', '_t'])
except KeyError:
Log.warning('MPIS cannot be generated!')
return
self.adp = AltriaDataProvider(self.data_provider)
def __init__(self, data_provider, output):
GlobalSessionToolBox.__init__(self, data_provider, output)
self.assortment = Assortment(data_provider)
self.ps_data = PsDataProvider(data_provider)
self.display_in_scene = data_provider.match_display_in_scene
self.static_display = data_provider.static_display
self.manufacturer_fk = int(self.manufacturer_fk)
self._add_display_data_to_scif()
self._add_client_name_and_sub_brand_data()
def __init__(self, data_provider, output):
GlobalSessionToolBox.__init__(self, data_provider, output)
self.own_manufacturer_fk = int(self.data_provider.own_manufacturer.param_value.values[0])
self.parser = Parser
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.assortment = Assortment(self.data_provider, self.output)
self.ps_data = PsDataProvider(self.data_provider, self.output)
self.kpi_external_targets = self.ps_data.get_kpi_external_targets(key_fields=Consts.KEY_FIELDS,
data_fields=Consts.DATA_FIELDS)
def __init__(self, output, data_provider):
super(PepsicoUtil, self).__init__(data_provider)
self.output = output
self.common = Common(self.data_provider)
# self.common_v1 = CommonV1(self.data_provider)
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK] if self.data_provider[Data.STORE_FK] is not None \
else self.session_info['store_fk'].values[0]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.rds_conn = PSProjectConnector(self.project_name, DbUsers.CalculationEng)
self.display_scene = self.get_match_display_in_scene()
self.kpi_static_data = self.common.get_kpi_static_data()
self.kpi_results_queries = []
self.probe_groups = self.get_probe_group()
self.match_product_in_scene = self.match_product_in_scene.merge(self.probe_groups, on='probe_match_fk',
how='left')
self.toolbox = GENERALToolBox(self.data_provider)
self.commontools = PEPSICOUKCommonToolBox(self.data_provider, self.rds_conn)
self.all_templates = self.commontools.all_templates
self.custom_entities = self.commontools.custom_entities
self.on_display_products = self.commontools.on_display_products
self.exclusion_template = self.commontools.exclusion_template
self.filtered_scif = self.commontools.filtered_scif.copy()
self.filtered_matches = self.commontools.filtered_matches.copy()
self.filtered_matches = self.filtered_matches.merge(self.probe_groups, on='probe_match_fk', how='left')
self.filtered_scif_secondary = self.commontools.filtered_scif_secondary.copy()
self.filtered_matches_secondary = self.commontools.filtered_matches_secondary.copy()
self.scene_bay_shelf_product = self.commontools.scene_bay_shelf_product
self.ps_data = PsDataProvider(self.data_provider, self.output)
self.full_store_info = self.commontools.full_store_info.copy()
self.external_targets = self.commontools.external_targets
self.assortment = Assortment(self.commontools.data_provider, self.output)
self.lvl3_ass_result = self.get_lvl3_relevant_assortment_result()
self.own_manuf_fk = self.all_products[self.all_products['manufacturer_name'] == self.PEPSICO]['manufacturer_fk'].values[0]
self.scene_kpi_results = self.get_results_of_scene_level_kpis()
self.kpi_results_check = pd.DataFrame(columns=['kpi_fk', 'numerator', 'denominator', 'result', 'score',
'context'])
self.sos_vs_target_targets = self.construct_sos_vs_target_base_df()
self.all_targets_unpacked = self.commontools.all_targets_unpacked.copy()
self.block_results = pd.DataFrame(columns=['Group Name', 'Score'])
self.hero_type_custom_entity_df = self.get_hero_type_custom_entity_df()
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.common_v2 = Common(self.data_provider)
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.store_id = self.data_provider[Data.STORE_FK]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.assortment = Assortment(self.data_provider, self.output)
self.own_manufacturer_fk = int(self.data_provider.own_manufacturer.param_value.values[0])
self.db_handler = DBHandler(self.data_provider.project_name, self.data_provider.session_uid)
self.previous_oos_results = self.db_handler.get_last_session_oos_results()
self.kpi_result_types = self.db_handler.get_kpi_result_value()
self.oos_store_results = list()
self.initial_scif = self.scif.copy()
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.project_name = self.data_provider.project_name
self.commonV2 = CommonV2(self.data_provider)
self.common = Common(self.data_provider)
self.assortment = Assortment(self.data_provider, self.output)
self.own_manuf_fk = int(
self.data_provider.own_manufacturer.param_value.values[0])
self.store_info = self.data_provider[Data.STORE_INFO]
self.new_kpi_static_data = self.commonV2.get_new_kpi_static_data()
self.all_products_include_deleted = self.data_provider[
Data.ALL_PRODUCTS_INCLUDING_DELETED]
self.visit_date = self.data_provider[Data.VISIT_DATE]
def __init__(self, data_provider, common, output, template_path):
self.output = output
self.data_provider = data_provider
self.common = common
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.session_id = self.data_provider.session_id
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.scene_results = self.data_provider[Data.SCENE_KPI_RESULTS]
self.templates = self.data_provider[Data.TEMPLATES]
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.mpis = self.make_mpis()
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.store_info = self.data_provider[Data.STORE_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.jump_shelves = pd.read_excel(template_path).T.to_dict('index')
self.scif = self.generate_scif()
self.scif['store_fk'] = self.store_id
self.scif = self.scif[~(self.scif['product_type'] == Const.IRRELEVANT)]
self.rds_conn = PSProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.kpis = self.load_kpis()
self.kpi_static_data = self.common.get_kpi_static_data()
self.dist_oos = self.dist_kpi_to_oos_kpi()
self.manufacturer_fk = int(
self.data_provider[Data.OWN_MANUFACTURER].iloc[0, 1])
self.gp_manufacturer = self.get_gp_manufacturer()
self.gp_categories = self.get_gp_categories()
self.gp_brands = self.get_gp_brands()
self.all_man = self.scif[['manufacturer_name', 'manufacturer_fk']].set_index('manufacturer_name') \
['manufacturer_fk'].to_dict()
self.all_brands = self.scif[[
'brand_name', 'brand_fk'
]].set_index('brand_name')['brand_fk'].to_dict()
self.man_fk_filter = {
'manufacturer_name': list(self.gp_manufacturer.keys())
}
self.cat_filter = {'category': list(self.gp_categories.keys())}
self.brand_filter = {'brand_name': list(self.gp_brands.keys())}
self.all_brands_filter = {'brand_name': list(self.all_brands.keys())}
self.all_man_filter = {'manufacturer_name': list(self.all_man.keys())}
self.kpi_results = []
self.assortment = Assortment(self.data_provider, self.output)
self.assortment.scif = self.scif
def __init__(self, data_provider, output, common):
self.output = output
self.data_provider = data_provider
self.common = common
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.rds_conn = PSProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.kpi_static_data = self.common.get_kpi_static_data()
self.kpi_results_queries = []
self.manufacturer_fk = self.products['manufacturer_fk'][
self.products['manufacturer_name'] ==
'MONDELEZ INTERNATIONAL INC'].iloc[0]
self.store_assortment = pd.DataFrame()
self.assortment = Assortment(self.data_provider, common=self.common)
self.store_number = self.get_store_number()
self.ps_data_provider = PsDataProvider(self.data_provider, self.output)
self.custom_entities = self.ps_data_provider.get_custom_entities(
Const.PPG_ENTITY_TYPE_FK)
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.common_v2 = CommonV2(self.data_provider)
self.common_v1 = CommonV1(self.data_provider)
self.data_provider.common_v2 = self.common_v2
self.data_provider.common_v1 = self.common_v1
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.rds_conn = PSProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.channel = self.get_store_channel(self.store_id)
self.kpi_static_data = self.common_v2.get_kpi_static_data()
self.data_provider.kpi_sheets = {}
self.kpi_sheets = self.data_provider.kpi_sheets
self.old_kpi_static_data = self.common_v1.get_kpi_static_data()
for name in SHEETS_NAME:
parsed_template = ParseTemplates.parse_template(TEMPLATE_PATH,
sheet_name=name)
self.kpi_sheets[name] = parsed_template[parsed_template['Channel']
== self.channel]
self.data_provider.sos = SOS(self.data_provider, output=None)
self.data_provider.assortment = Assortment(self.data_provider,
output=None)
def main_calculation(self):
"""
This function calculates the KPI results.
"""
self.calculate_share_of_shelf()
self.calculate_count_of_display()
Assortment(self.data_provider, self.output,
common=self.common).main_assortment_calculation()
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.common = Common(self.data_provider)
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.rds_conn = PSProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.kpi_static_data = self.common.get_new_kpi_static_data()
self.kpi_results_queries = []
self.assortment = Assortment(self.data_provider, self.output)
def __init__(self, output, data_provider):
super(StraussfritolayilUtil, self).__init__(data_provider)
self.output = output
self.common = Common(self.data_provider)
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.ps_data = PsDataProvider(self.data_provider, self.output)
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.brand_mix_df = self.get_brand_mix_df()
self.add_sub_brand_to_scif()
self.add_brand_mix_to_scif()
self.match_probe_in_scene = self.ps_data.get_product_special_attribute_data(self.session_uid)
self.match_product_in_scene = self.data_provider[Data.MATCHES]
if not self.match_product_in_scene.empty:
self.match_product_in_scene = self.match_product_in_scene.merge(self.scif[Consts.RELEVENT_FIELDS],
on=["scene_fk", "product_fk"], how="left")
self.filter_scif_and_mpis_to_contain_only_primary_shelf()
else:
unique_fields = [ele for ele in Consts.RELEVENT_FIELDS if ele not in ["product_fk", "scene_fk"]]
self.match_product_in_scene = pd.concat([self.match_product_in_scene,
pd.DataFrame(columns=unique_fields)], axis=1)
self.match_product_in_scene_wo_hangers = self.exclude_special_attribute_products(df=self.match_product_in_scene)
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_info = self.data_provider[Data.STORE_INFO]
self.additional_attribute_2 = self.store_info[Consts.ADDITIONAL_ATTRIBUTE_2].values[0]
self.additional_attribute_3 = self.store_info[Consts.ADDITIONAL_ATTRIBUTE_3].values[0]
self.additional_attribute_4 = self.store_info[Consts.ADDITIONAL_ATTRIBUTE_4].values[0]
self.store_id = self.store_info['store_fk'].values[0] if self.store_info['store_fk'] is not None else 0
self.rds_conn = PSProjectConnector(self.project_name, DbUsers.CalculationEng)
self.toolbox = GENERALToolBox(self.data_provider)
self.kpi_external_targets = self.ps_data.get_kpi_external_targets(key_fields=Consts.KEY_FIELDS,
data_fields=Consts.DATA_FIELDS)
self.filter_external_targets()
self.assortment = Assortment(self.data_provider, self.output)
self.lvl3_assortment = self.set_updated_assortment()
self.own_manuf_fk = int(self.data_provider.own_manufacturer.param_value.values[0])
self.own_manufacturer_matches_wo_hangers = self.match_product_in_scene_wo_hangers[
self.match_product_in_scene_wo_hangers['manufacturer_fk'] == self.own_manuf_fk]
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.common = Common(self.data_provider)
self.common_v1 = CommonV1(self.data_provider)
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.store_info = self.data_provider[Data.STORE_INFO]
self.visit_type = self.store_info[
Const.ADDITIONAL_ATTRIBUTE_2].values[0]
self.all_templates = self.data_provider[Data.ALL_TEMPLATES]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.scif = self.scif.loc[~(self.scif[Const.PRODUCT_TYPE]
== Const.IRRELEVANT)] # Vitaly's request
self.rds_conn = PSProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.kpi_static_data = self.common.get_kpi_static_data()
self.kpi_results_queries = []
self.k_engine = BaseCalculationsGroup(data_provider, output)
self.toolbox = GENERALToolBox(data_provider)
self.assortment = Assortment(self.data_provider,
self.output,
common=self.common_v1)
if not self.scif.empty:
self.pepsico_fk = self.get_relevant_pk_by_name(
Const.MANUFACTURER, Const.PEPSICO)
self.categories_to_calculate = self.get_relevant_categories_for_session(
)
self.main_shelves = self.get_main_shelves()
def __init__(self, data_provider, common, output):
self.output = output
self.data_provider = data_provider
self.common = common
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.scene = self.scene_info.loc[0, 'scene_fk']
self.templates = self.data_provider[Data.TEMPLATES]
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.store_id = self.data_provider[Data.STORE_FK]
self.store_info = self.data_provider[Data.STORE_INFO]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
# self.scif = self.scif[~(self.scif['product_type'] == 'Irrelevant')]
self.rds_conn = PSProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.kpi_static_data = self.common.get_kpi_static_data()
self.mdis = self.get_match_display_in_scene()
self.mpis = self.get_mpis()
self.manufacturer_fk = self.products['manufacturer_fk'][
self.products['manufacturer_name'] ==
'MONDELEZ INTERNATIONAL INC'].iloc[0]
self.static_task_area_location = self.get_store_task_area()
# self.vtw_points_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', 'Data',
# "VTW_POINTS_SCORE.xlsx")
self.dmi_template = os.path.join(
os.path.dirname(os.path.realpath(__file__)), '..', 'Data',
"MondelezDMI_KPITemplatev7.1.xlsx")
self.points_template = pd.read_excel(self.dmi_template,
sheetname='VTW_POINTS')
self.goldzone_template = pd.read_excel(self.dmi_template,
sheetname='GOLD_ZONE')
self.assortment = Assortment(self.data_provider, common=self.common)
# self.ps_data_provider = PsDataProvider(self.data_provider, self.output)
self.store_areas = self.get_store_area_df()
class ToolBox(GlobalSessionToolBox):
def __init__(self, data_provider, output):
GlobalSessionToolBox.__init__(self, data_provider, output)
self.assortment = Assortment(self.data_provider, self.output)
def main_calculation(self):
self.calculate_assortment()
@kpi_runtime()
def calculate_assortment(self):
lvl3_result = self.assortment.calculate_lvl3_assortment()
if lvl3_result.empty:
return
kpi_fks = lvl3_result[Consts.KPI_FK_LVL_2].unique()
for kpi_fk in kpi_fks:
df = lvl3_result[lvl3_result[Consts.KPI_FK_LVL_2] == kpi_fk]
sku_kpi_fk = df[Consts.KPI_FK_LVL_3].values[0]
assortment_group_fk = df[
AssortmentGroupConsts.ASSORTMENT_GROUP_FK].values[0]
assortment_fk = df[AssortmentProductConsts.ASSORTMENT_FK].values[0]
df.apply(lambda row: self.write_to_db(
fk=sku_kpi_fk,
numerator_id=row[ProductsConsts.PRODUCT_FK],
denominator_id=assortment_fk,
result=row[Consts.IN_STORE],
score=row[Consts.IN_STORE],
identifier_parent=kpi_fk,
should_enter=True),
axis=1)
denominator = len(df)
numerator = len(df[df[Consts.IN_STORE] == 1])
result = numerator / float(denominator)
self.write_to_db(fk=kpi_fk,
numerator_id=assortment_group_fk,
denominator_id=self.store_id,
result=result,
score=result,
numerator_result=numerator,
denominator_result=denominator,
identifier_result=kpi_fk)
class CSTOREToolBox:
def __init__(self, data_provider, output, common):
self.output = output
self.data_provider = data_provider
self.common = common
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.session_id = self.data_provider.session_id
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.scene_results = self.data_provider[Data.SCENE_KPI_RESULTS]
self.templates = self.data_provider[Data.TEMPLATES]
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.store_info = self.data_provider[Data.STORE_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.scif['store_fk'] = self.store_id
self.scif = self.scif[~(self.scif['product_type'].isin([Const.IRRELEVANT, Const.EMPTY]))]
self.scif = self.scif[self.scif['facings'] > 0]
self.rds_conn = PSProjectConnector(self.project_name, DbUsers.CalculationEng)
self.kpis = self.load_kpis()
self.results_values = self.load_results_values()
self.kpi_static_data = self.common.get_kpi_static_data()
self.manufacturer_fk = int(self.data_provider[Data.OWN_MANUFACTURER].iloc[0, 1])
self.kpi_results = []
self.assortment = Assortment(self.data_provider, self.output)
def main_calculation(self, *args, **kwargs):
"""
This function calculates the KPI results.
"""
# if not self.filter_df(self.scif, self.brand_filter).empty:
self.calculate_displays()
self.calculate_assortment()
for result in self.kpi_results:
self.write_to_db(**result)
return
def calculate_facings_sos(self):
self.safety_func('Facings SOS', self.calculate_sos, [Const.FACING_SOS_KPI, {}])
def safety_func(self, group, func, args):
try:
func(*args)
Log.info('{} KPIs Calculated'.format(group))
except Exception as e:
Log.error('ERROR {} KPIs Failed to Calculate'.format(group))
Log.error(e)
def calculate_displays(self):
relevant_kpis = self.kpis[self.kpis[Const.KPI_FAMILY] == Const.DISPLAY]
relevant_kpis['num_types'] = self.name_to_col_name(relevant_kpis[Const.NUMERATOR])
df_base = self.scif[self.scif['template_name'] == 'Displays - Mondelez Brands Only']
df_base = df_base[df_base['manufacturer_fk'] == self.manufacturer_fk]
df_base['numerator_result'], df_base['result'] = 1, 1
if not df_base.empty:
for i, kpi in relevant_kpis.iterrows():
parent = relevant_kpis[relevant_kpis['type'] == Const.SOS_HIERARCHY[kpi['type']]] # Note, parent is df, kpi is a series
df = df_base.copy()
df = self.update_and_rename_df(df, kpi, parent)
df = self.transform_new_col(df, 'numerator_id', 'numerator_result')
df.drop('scene_id', axis=1, inplace=True)
df.drop_duplicates(inplace=True)
df['result'] = df['numerator_result']
if parent.empty:
df['denominator_id'] = self.store_id
df.drop('ident_parent', axis=1, inplace=True)
self.update_results(df)
else:
df = pd.DataFrame([self.store_id], columns=['denominator_id'])
df['numerator_id'] = self.manufacturer_fk
df['numerator_result'], df['result'] = 0, 0
df['kpi_name'] = [key for key, val in Const.SOS_HIERARCHY.items() if val == 'ihavenoparent'][0]
self.update_results(df)
def update_results(self, df):
results = [val for key, val in df.to_dict('index').items()]
self.kpi_results += results
def update_and_rename_df(self, df, kpi, parent):
df['ident_result'] = ['{}_{}'.format(row[kpi['num_types']], kpi['type'])
for i, row in df.iterrows()]
df['ident_parent'] = ['{}_{}_{}'.format(row[kpi['num_types']], 'Parent', kpi['type'])
for i, row in df.iterrows()]
parent_col = ['ident_parent']
if not parent.empty:
df['ident_parent'] = ['{}_{}'.format(row[parent['num_types'].iloc[0]], parent['type'].iloc[0])
for i, row in df.iterrows()] #parent is a df, hence the iloc
df = df[['scene_id', 'numerator_result', 'result', kpi['num_types'], 'ident_result'] + parent_col]
df.drop_duplicates(inplace=True)
df.rename(columns={kpi['num_types']: 'numerator_id'}, inplace=True)
df['kpi_name'] = kpi['type']
return df
def name_to_col_name(self, col):
return ['{}_fk'.format(num) for num in col]
def transform_new_col(self, df, group_col, sum_col):
df[sum_col] = df.groupby(group_col)[sum_col].transform('sum')
return df
def grouper(self, filter, df):
return self.filter_df(df, filter).groupby(filter.keys()[0])
def calculate_assortment(self):
lvl3_results = self.assortment.calculate_lvl3_assortment()
if not lvl3_results.empty:
for kpi in lvl3_results['kpi_fk_lvl3'].unique():
lvl3_result = lvl3_results[lvl3_results['kpi_fk_lvl3']==kpi]
lvl3_result['target'] = 1
lvlx_result = pd.DataFrame()
lvl1_result = pd.DataFrame()
# For Dist, they have assortments, but want the results by category
# and since there is only one policy per store (barring new which is
# handled elsewhere) we will kust pretend that category_fk is the
# level 2 assortment group. God rest the soul of whomever needs
# to implement additional policies.
if kpi == 4000:
lvl3_result = lvl3_result.set_index('product_fk').join(self.all_products.set_index('product_fk')
['category_fk']).reset_index()\
.drop_duplicates()
lvl3_result = lvl3_result.rename(columns={'assortment_group_fk': 'ass_grp_fk',
'category_fk': 'assortment_group_fk'})
lvlx_result = self.assortment_additional(lvl3_result)
lvl2_result = self.assortment.calculate_lvl2_assortment(lvl3_result)
if not lvl2_result['kpi_fk_lvl1'].any():
# lvl3_result['assortment_group_fk'] = self.manufacturer_fk
lvl2_result['assortment_group_fk'] = self.manufacturer_fk
lvl2_result['assortment_super_group_fk'] = self.store_id
else:
lvl1_result = self.assortment.calculate_lvl1_assortment(lvl2_result)
lvl1_result['total'] = lvl2_result['total'].sum()
lvl1_result['passes'] = lvl2_result['passes'].sum()
lvl1_result['num_id'] = self.manufacturer_fk
lvl1_result['den_id'] = self.store_id
lvl3_result['in_store'] = lvl3_result['in_store'].apply(lambda x:
self.results_values[Const.RESULTS_TYPE_DICT[x]])
self.parse_assortment_results(lvl3_result, 'kpi_fk_lvl3', 'product_fk', 'in_store', 'assortment_group_fk',
'target', None, 'assortment_group_fk')
self.parse_assortment_results(lvlx_result, 'kpi_fk_lvl3', 'product_fk', 'in_store', 'assortment_group_fk',
'target', None, 'assortment_group_fk')
self.parse_assortment_results(lvl2_result, 'kpi_fk_lvl2', 'assortment_group_fk', 'passes', 'assortment_fk',
'total', 'assortment_group_fk', 'assortment_super_group_fk')
self.parse_assortment_results(lvl1_result, 'kpi_fk_lvl1', 'num_id', 'passes',
'den_id', 'total', 'assortment_super_group_fk', None)
Log.info('Assortment KPIs Calculated')
def parse_assortment_results(self, df, kpi_col, num_id_col, num_col, den_id_col, den_col, self_id, parent):
for i, row in df.iterrows():
kpi_res = {'kpi_fk': row[kpi_col],
'numerator_id': row[num_id_col],
'numerator_result': row[num_col],
'denominator_id': row[den_id_col],
'denominator_result': row[den_col],
'score': self.safe_divide(row[num_col], row[den_col]),
'result': self.safe_divide(row[num_col], row[den_col]),
'ident_result': row[self_id] if self_id else None,
'ident_parent': row[parent] if parent else None}
self.kpi_results.append(kpi_res)
def assortment_additional(self, lvl3_result):
assort = set(lvl3_result['product_fk'])
additional_sku_df = self.scif[~self.scif['product_fk'].isin(assort)]
additional_sku_df = additional_sku_df[additional_sku_df['manufacturer_fk'] == self.manufacturer_fk]
additional_sku_df = additional_sku_df[additional_sku_df['facings'] != 0][['product_fk', 'category_fk']]
additional_sku_df['kpi_fk_lvl3'] = lvl3_result['kpi_fk_lvl3'].values[0]
additional_sku_df['in_store'] = self.results_values['Additional']
additional_sku_df['target'] = 0
additional_sku_df = additional_sku_df.rename(columns={'category_fk': 'assortment_group_fk'})
return additional_sku_df
def safe_divide(self, num, den):
res = num
if num <= den:
res = round((float(num) / den) * 100, 2) if num and den else 0
res = '{:.2f}'.format(res)
return res
@staticmethod
def filter_df(df, filters, exclude=0):
cols = set(df.columns)
for key, val in filters.items():
if key not in cols:
return pd.DataFrame()
if not isinstance(val, list):
val = [val]
if exclude:
df = df[~df[key].isin(val)]
else:
df = df[df[key].isin(val)]
return df
def load_kpis(self):
return pd.read_sql_query(Const.KPI_QUERY, self.rds_conn.db)
def load_results_values(self):
return pd.read_sql_query(Const.RESULT_TYPE_QUERY, self.rds_conn.db).set_index('value')['pk'].to_dict()
def write_to_db(self, kpi_name=None, score=0, result=None, target=None, numerator_result=None, scene_result_fk=None,
denominator_result=None, numerator_id=999, denominator_id=999, ident_result=None, ident_parent=None,
kpi_fk = None):
"""
writes result in the DB
:param kpi_name: str
:param score: float
:param display_text: str
:param result: str
:param threshold: int
"""
if not kpi_fk and kpi_name:
kpi_fk = self.common.get_kpi_fk_by_kpi_type(kpi_name)
self.common.write_to_db_result(fk=kpi_fk, score=score, result=result, should_enter=True, target=target,
numerator_result=numerator_result, denominator_result=denominator_result,
numerator_id=numerator_id, denominator_id=denominator_id,
identifier_result=ident_result, identifier_parent=ident_parent,
scene_result_fk=scene_result_fk)
class ToolBox(GlobalSessionToolBox):
def __init__(self, data_provider, output):
GlobalSessionToolBox.__init__(self, data_provider, output)
self.own_manufacturer_fk = int(self.data_provider.own_manufacturer.param_value.values[0])
self.parser = Parser
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.assortment = Assortment(self.data_provider, self.output)
self.ps_data = PsDataProvider(self.data_provider, self.output)
self.kpi_external_targets = self.ps_data.get_kpi_external_targets(key_fields=Consts.KEY_FIELDS,
data_fields=Consts.DATA_FIELDS)
def main_calculation(self):
self.calculate_score_sos()
self.calculate_oos_and_distribution(assortment_type="Core")
self.calculate_oos_and_distribution(assortment_type="Launch")
self.calculate_oos_and_distribution(assortment_type="Focus")
self.calculate_hierarchy_sos(calculation_type='FACINGS')
self.calculate_hierarchy_sos(calculation_type='LINEAR')
@kpi_runtime()
def calculate_oos_and_distribution(self, assortment_type):
dis_numerator = total_facings = 0
oos_store_kpi_fk = self.common.get_kpi_fk_by_kpi_type(kpi_type=assortment_type + Consts.OOS)
oos_sku_kpi_fk = self.common.get_kpi_fk_by_kpi_type(kpi_type=assortment_type + Consts.OOS_SKU)
dis_store_kpi_fk = self.common.get_kpi_fk_by_kpi_type(kpi_type=assortment_type + Consts.DISTRIBUTION)
dis_cat_kpi_fk = self.common.get_kpi_fk_by_kpi_type(kpi_type=assortment_type + Consts.DISTRIBUTION_CAT)
dis_sku_kpi_fk = self.common.get_kpi_fk_by_kpi_type(kpi_type=assortment_type + Consts.DISTRIBUTION_SKU)
assortment_df = self.assortment.get_lvl3_relevant_ass()
assortment_df = assortment_df[assortment_df['kpi_fk_lvl3'] == dis_sku_kpi_fk]
product_fks = assortment_df['product_fk'].tolist()
categories = list(set(self.all_products[self.all_products['product_fk'].isin(product_fks)]['category_fk']))
categories_dict = dict.fromkeys(categories, (0, 0))
# sku level distribution
for sku in product_fks:
# 2 for distributed and 1 for oos
category_fk = self.all_products[self.all_products['product_fk'] == sku]['category_fk'].values[0]
product_df = self.scif[self.scif['product_fk'] == sku]
if product_df.empty:
categories_dict[category_fk] = map(sum, zip(categories_dict[category_fk], [0, 1]))
result = 1
facings = 0
# Saving OOS only if product wasn't in store
self.common.write_to_db_result(fk=oos_sku_kpi_fk, numerator_id=sku, denominator_id=category_fk,
result=result, numerator_result=result, denominator_result=result,
score=facings, identifier_parent=assortment_type + "_OOS",
should_enter=True)
else:
categories_dict[category_fk] = map(sum, zip(categories_dict[category_fk], [1, 1]))
result = 2
facings = product_df['facings'].values[0]
dis_numerator += 1
total_facings += facings
self.common.write_to_db_result(fk=dis_sku_kpi_fk, numerator_id=sku, denominator_id=category_fk,
result=result, numerator_result=result, denominator_result=result,
score=facings, should_enter=True,
identifier_parent=assortment_type + "_DIS_CAT_{}".format(str(category_fk)))
# category level distribution
for category_fk in categories_dict.keys():
cat_numerator, cat_denominator = categories_dict[category_fk]
cat_result = self.get_result(cat_numerator, cat_denominator)
self.common.write_to_db_result(fk=dis_cat_kpi_fk, numerator_id=category_fk,
denominator_id=self.store_id, result=cat_result, should_enter=True,
numerator_result=cat_numerator, denominator_result=cat_denominator,
score=cat_result, identifier_parent=assortment_type + "_DIS",
identifier_result=assortment_type + "_DIS_CAT_{}".format(str(category_fk)))
# store level oos and distribution
denominator = len(product_fks)
dis_result = self.get_result(dis_numerator, denominator)
oos_result = 1 - dis_result
oos_numerator = denominator - dis_numerator
self.common.write_to_db_result(fk=oos_store_kpi_fk, numerator_id=self.own_manufacturer_fk,
denominator_id=self.store_id, result=oos_result, numerator_result=oos_numerator,
denominator_result=denominator, score=total_facings,
identifier_result=assortment_type + "_OOS")
self.common.write_to_db_result(fk=dis_store_kpi_fk, numerator_id=self.own_manufacturer_fk,
denominator_id=self.store_id, result=dis_result, numerator_result=dis_numerator,
denominator_result=denominator, score=total_facings,
identifier_result=assortment_type + "_DIS")
def get_kpi_fks(self, kpis_list):
for kpi in kpis_list:
self.common.get_kpi_fk_by_kpi_type(kpi_type=kpi)
@staticmethod
def calculate_sos_res(numerator, denominator):
if denominator == 0:
return 0, 0, 0
result = round(numerator / float(denominator), 3)
return result, numerator, denominator
@kpi_runtime()
def calculate_score_sos(self):
relevant_template = self.kpi_external_targets[self.kpi_external_targets[ExternalTargetsConsts.OPERATION_TYPE]
== Consts.SOS_KPIS]
relevant_rows = relevant_template.copy()
lsos_score_kpi_fk = self.common.get_kpi_fk_by_kpi_type(Consts.LSOS_SCORE_KPI)
store_denominator = len(relevant_rows)
store_numerator = 0
for i, kpi_row in relevant_template.iterrows():
kpi_fk, num_type, num_value, deno_type, deno_value, target, target_range = kpi_row[Consts.RELEVANT_FIELDS]
numerator_filters, denominator_filters = self.get_num_and_den_filters(num_type, num_value, deno_type,
deno_value)
# Only straussil SKUs
numerator_filters['manufacturer_fk'] = self.own_manufacturer_fk
denominator_filters['manufacturer_fk'] = self.own_manufacturer_fk
numerator_df = self.parser.filter_df(conditions=numerator_filters, data_frame_to_filter=self.scif)
denominator_df = self.parser.filter_df(conditions=denominator_filters, data_frame_to_filter=self.scif)
numerator_result = numerator_df['gross_len_ign_stack'].sum()
denominator_result = denominator_df['gross_len_ign_stack'].sum()
lsos_result = self.get_result(numerator_result, denominator_result)
score = 1 if ((target - target_range) <= lsos_result <= (target + target_range)) else 0
store_numerator += score
self.common.write_to_db_result(fk=kpi_fk, numerator_id=self.own_manufacturer_fk,
denominator_id=self.store_id, should_enter=True, target=target,
numerator_result=numerator_result, denominator_result=denominator_result,
result=lsos_result, score=score, identifier_parent='LSOS_SCORE',
weight=target_range)
store_result = self.get_result(store_numerator, store_denominator)
self.common.write_to_db_result(fk=lsos_score_kpi_fk, numerator_id=self.own_manufacturer_fk,
denominator_id=self.store_id, should_enter=True, target=store_denominator,
numerator_result=store_numerator, denominator_result=store_denominator,
result=store_numerator, score=store_result, identifier_result='LSOS_SCORE')
@staticmethod
def get_num_and_den_filters(numerator_type, numerator_value, denominator_type, denominator_value):
if type(numerator_value) != list:
numerator_value = [numerator_value]
if type(denominator_value) != list:
denominator_value = [denominator_value]
numerator_filters = {numerator_type: numerator_value}
denominator_filters = {denominator_type: denominator_value}
return numerator_filters, denominator_filters
@staticmethod
def get_result(numerator, denominator):
if denominator == 0:
return 0
else:
return round(numerator / float(denominator), 4)
def calculate_hierarchy_sos(self, calculation_type):
brand_kpi_fk = self.common.get_kpi_fk_by_kpi_type(kpi_type=(calculation_type + Consts.SOS_BY_BRAND))
brand_category_kpi_fk = self.common.get_kpi_fk_by_kpi_type(kpi_type=(calculation_type +
Consts.SOS_BY_CAT_BRAND))
sku_kpi_fk = self.common.get_kpi_fk_by_kpi_type(kpi_type=(calculation_type + Consts.SOS_BY_CAT_BRAND_SKU))
calculation_param = "facings_ign_stack" if calculation_type == 'FACINGS' else "gross_len_ign_stack"
sos_df = self.parser.filter_df(conditions={'rlv_sos_sc': 1, 'product_type': ['SKU', 'Empty']},
data_frame_to_filter=self.scif)
# brand level sos
session_brands = set(sos_df['brand_fk'])
brand_den = sos_df[calculation_param].sum()
for brand_fk in session_brands:
filters = {'brand_fk': brand_fk}
brand_df = self.parser.filter_df(conditions=filters, data_frame_to_filter=sos_df)
if brand_df.empty:
continue
manufacturer_fk = brand_df['manufacturer_fk'].values[0]
brand_num = brand_df[calculation_param].sum()
if brand_num == 0:
continue
brand_res, brand_num, brand_den = self.calculate_sos_res(brand_num, brand_den)
self.common.write_to_db_result(fk=brand_kpi_fk, numerator_id=brand_fk,
denominator_id=manufacturer_fk,
result=brand_res, numerator_result=brand_num, denominator_result=brand_den,
score=brand_res,
identifier_result="{}_SOS_brand_{}".format(calculation_type, str(brand_fk)))
# brand-category level sos
brand_categories = set(self.parser.filter_df(conditions=filters,
data_frame_to_filter=sos_df)['category_fk'])
for category_fk in brand_categories:
cat_den = self.parser.filter_df(conditions={'category_fk': category_fk},
data_frame_to_filter=sos_df)[calculation_param].sum()
filters['category_fk'] = category_fk
category_df = self.parser.filter_df(conditions=filters, data_frame_to_filter=sos_df)
cat_num = category_df[calculation_param].sum()
if cat_num == 0:
continue
cat_res, cat_num, cat_den = self.calculate_sos_res(cat_num, cat_den)
self.common.write_to_db_result(fk=brand_category_kpi_fk, numerator_id=brand_fk,
context_id=manufacturer_fk,
denominator_id=category_fk, result=cat_res, numerator_result=cat_num,
should_enter=True, denominator_result=cat_den, score=cat_res,
identifier_parent="{}_SOS_brand_{}".format(calculation_type,
str(brand_fk)),
identifier_result="{}_SOS_cat_{}_brand_{}".format(calculation_type,
str(category_fk),
str(brand_fk)))
product_fks = set(self.parser.filter_df(conditions=filters, data_frame_to_filter=sos_df)['product_fk'])
for sku in product_fks:
filters['product_fk'] = sku
product_df = self.parser.filter_df(conditions=filters, data_frame_to_filter=sos_df)
sku_num = product_df[calculation_param].sum()
if sku_num == 0:
continue
sku_result, sku_num, sku_den = self.calculate_sos_res(sku_num, cat_num)
self.common.write_to_db_result(fk=sku_kpi_fk, numerator_id=sku, denominator_id=brand_fk,
result=sku_result, numerator_result=sku_num, should_enter=True,
denominator_result=cat_num, score=sku_num,
context_id=category_fk, weight=manufacturer_fk,
identifier_parent="{}_SOS_cat_{}_brand_{}".format(calculation_type,
str(category_fk),
str(brand_fk)))
del filters['product_fk']
del filters['category_fk']
class CCKHToolBox(CCKHConsts):
LEVEL1 = 1
LEVEL2 = 2
LEVEL3 = 3
def __init__(self, data_provider, output):
self.k_engine = BaseCalculationsScript(data_provider, output)
self.output = output
self.data_provider = data_provider
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.store_info = self.data_provider[Data.STORE_INFO]
self.store_type = self.store_info['store_type'].iloc[0]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.rds_conn = PSProjectConnector(self.project_name, DbUsers.CalculationEng)
self.general_tools = CCKHGENERALToolBox(self.data_provider, self.output)
self.template = CCKHTemplateConsts()
self.kpi_static_data = self.get_kpi_static_data()
self.kpi_results_queries = []
self.commonV2 = CommonV2(self.data_provider)
self.kpi_new_static_data = self.commonV2.get_new_kpi_static_data()
self.manufacturer = int(self.data_provider.own_manufacturer.param_value.values[0])
self.ps_data_provider = PsDataProvider(self.data_provider, self.output)
self.external_targets = self.ps_data_provider.get_kpi_external_targets()
self.assortment = Assortment(self.data_provider, self.output)
self.templates_info = self.external_targets[self.external_targets[CCKHTemplateConsts.TEMPLATE_OPERATION] ==
CCKHTemplateConsts.BASIC_SHEET]
self.visibility_info = self.external_targets[self.external_targets[CCKHTemplateConsts.TEMPLATE_OPERATION]
== CCKHTemplateConsts.VISIBILITY_SHEET]
self.cooler_info = self.external_targets[self.external_targets[CCKHTemplateConsts.TEMPLATE_OPERATION]
== CCKHTemplateConsts.COOLER_SHEET]
def get_kpi_static_data(self):
"""
This function extracts the static KPI data and saves it into one global data frame.
The data is taken from static.kpi / static.atomic_kpi / static.kpi_set.
"""
query = CCKHQueries.get_all_kpi_data()
kpi_static_data = pd.read_sql_query(query, self.rds_conn.db)
return kpi_static_data
def calculate_red_score(self):
"""
This function calculates the KPI results.
"""
scores_dict = {}
results_list_new_db = []
# assortments based calculations for availability
availability_kpi_dict, availability_score_dict = self.get_availability_kpi_data()
results_list_new_db.extend(availability_kpi_dict)
scores_dict.update(availability_score_dict)
# external target based calculations
final_main_child = self.templates_info[self.templates_info['Tested KPI Group'] == self.RED_SCORE].iloc[0]
all_kpi_dict, all_score_dict = self.get_all_kpi_data()
results_list_new_db.extend(all_kpi_dict)
scores_dict.update(all_score_dict)
# aggregation to calculate red score
max_points = sum([score[0] for score in scores_dict.values()])
actual_points = sum([score[1] for score in scores_dict.values()])
red_score = 0 if max_points == 0 else round((actual_points / float(max_points)) * 100, 2)
set_fk = self.kpi_static_data['kpi_set_fk'].values[0]
self.write_to_db_result(set_fk, (actual_points, max_points, red_score), level=self.LEVEL1)
results_list_new_db.append(self.get_new_kpi_dict(self.get_new_kpi_fk(final_main_child), red_score,
red_score, actual_points, max_points,
target=max_points,
weight=actual_points,
identifier_result=self.RED_SCORE,
identifier_parent=CCKHConsts.WEB_HIERARCHY))
results_list_new_db.append(self.get_new_kpi_dict(self.get_new_kpi_by_name(self.RED_SCORE), red_score,
red_score, actual_points, max_points,
target=max_points, weight=actual_points,
identifier_result=CCKHConsts.WEB_HIERARCHY))
self.commonV2.save_json_to_new_tables(results_list_new_db)
self.commonV2.commit_results_data()
def get_availability_kpi_data(self):
availability_results_list = []
scores_dict = {}
availability_assortment_df = self.assortment.calculate_lvl3_assortment()
if availability_assortment_df.empty:
Log.info("Availability KPI: session: {} does not have relevant assortments.".format(self.session_uid))
return [], {}
availability_kpi = self.kpi_new_static_data[self.kpi_new_static_data['type'].str.encode(
HelperConsts.UTF8) == self.template.AVAILABILITY_KPI_TYPE.encode(HelperConsts.UTF8)].iloc[0]
availability_new_kpi_fk = availability_kpi.pk
scores = []
# no need to validate_kpi_run because Availability is 1; seems it was added for the other KPIs
kpi_availability_group = availability_assortment_df.groupby('kpi_fk_lvl2')
for kpi_fk_lvl2, availability_kpi_df in kpi_availability_group:
score, result, threshold = self.calculate_availability(availability_kpi_df)
numerator, denominator, result_new_db = result, threshold, result
if score is not False:
if score is None:
points = 0
else:
points = 1
scores.append((points, score))
atomic_kpi_name = self.kpi_new_static_data[self.kpi_new_static_data['pk'] ==
kpi_fk_lvl2].iloc[0].type
Log.info('Save availability atomic kpi: {}'.format(atomic_kpi_name))
atomic_kpi = self.kpi_static_data[(self.kpi_static_data['kpi_name'].str.encode(HelperConsts.UTF8) ==
self.template.AVAILABILITY_KPI_TYPE.encode(HelperConsts.UTF8)) &
(self.kpi_static_data['atomic_kpi_name'] == atomic_kpi_name)]
atomic_fk = atomic_kpi.iloc[0].atomic_kpi_fk
self.write_to_db_result(atomic_fk, (score, result, threshold, points), level=self.LEVEL3)
child_name = atomic_kpi.atomic_kpi_name.iloc[0]
child_kpi_fk = self.get_new_kpi_by_name(child_name) # kpi fk from new tables
Log.info('Save availability for {} ID: {}'.format(child_name, child_kpi_fk))
availability_results_list.append(self.get_new_kpi_dict(child_kpi_fk, result_new_db, score,
numerator, denominator,
weight=points, target=denominator,
identifier_parent={
'kpi_fk': availability_new_kpi_fk},
))
max_points = sum([score[0] for score in scores])
actual_points = sum([score[0] * score[1] for score in scores])
percentage = 0 if max_points == 0 else round(
(actual_points / float(max_points)) * 100, 2)
kpi_fk = self.kpi_static_data[self.kpi_static_data['kpi_name'].str.encode(HelperConsts.UTF8) ==
self.template.AVAILABILITY_KPI_TYPE.encode(HelperConsts.UTF8)][
'kpi_fk'].values[0]
self.write_to_db_result(kpi_fk, (actual_points, max_points,
percentage), level=self.LEVEL2)
scores_dict[self.template.AVAILABILITY_KPI_TYPE] = (max_points, actual_points)
availability_results_list.append(self.get_new_kpi_dict(availability_new_kpi_fk, percentage, percentage,
actual_points, max_points,
target=max_points,
weight=actual_points,
identifier_result={
'kpi_fk': availability_new_kpi_fk},
identifier_parent=self.RED_SCORE))
return availability_results_list, scores_dict
def get_all_kpi_data(self):
results_list_new_db = []
scores_dict = {}
if self.templates_info.empty:
Log.info("All KPI: session: {} doesnt have relevant external targets".format(self.session_uid))
return [], {}
main_children = self.templates_info[self.templates_info[self.template.KPI_GROUP] == self.RED_SCORE]
for c in xrange(0, len(main_children)):
main_child = main_children.iloc[c]
main_child_kpi_fk = self.get_new_kpi_fk(main_child) # kpi fk from new tables
main_kpi_identifier = self.commonV2.get_dictionary(kpi_fk=main_child_kpi_fk)
if self.validate_store_type(main_child):
children = self.templates_info[self.templates_info
[self.template.KPI_GROUP].str.encode(HelperConsts.UTF8) ==
main_child[self.template.KPI_NAME].encode(HelperConsts.UTF8)]
scores = []
for i in xrange(len(children)):
child = children.iloc[i]
numerator, denominator, result_new_db, numerator_id = 0, 0, 0, None
kpi_weight = self.validate_kpi_run(child)
if kpi_weight is not False:
kpi_type = child[self.template.KPI_TYPE]
result = threshold = None
if kpi_type == self.SURVEY:
score, result, threshold, survey_answer_fk = self.check_survey(child)
threshold = None
numerator, denominator, result_new_db = 1, 1, score * 100
numerator_id = survey_answer_fk
elif kpi_type == self.SHARE_OF_SHELF:
score, result, threshold, result_new_db, numerator, denominator = \
self.calculate_share_of_shelf(child)
elif kpi_type == self.NUMBER_OF_SCENES:
scene_types = self.get_scene_types(child)
result = self.general_tools.calculate_number_of_scenes(
**{SCENE_TYPE_FIELD: scene_types})
numerator, denominator, result_new_db = result, 1, result
score = 1 if result >= 1 else 0
else:
Log.warning("KPI of type '{}' is not supported via assortments".format(kpi_type))
continue
if score is not False:
if score is None:
points = 0
else:
points = float(child[self.template.WEIGHT]
) if kpi_weight is True else kpi_weight
scores.append((points, score))
atomic_fk = self.get_atomic_fk(main_child, child)
self.write_to_db_result(
atomic_fk, (score, result, threshold, points), level=self.LEVEL3)
identifier_parent = main_kpi_identifier
child_name = '{}-{}'.format(child[self.template.TRANSLATION], 'Atomic') \
if main_child[self.template.KPI_NAME] == child[self.template.KPI_NAME] else child[
self.template.TRANSLATION]
child.set_value(self.template.TRANSLATION, child_name)
child_kpi_fk = self.get_new_kpi_fk(child) # kpi fk from new tables
results_list_new_db.append(self.get_new_kpi_dict(child_kpi_fk, result_new_db, score,
numerator, denominator,
weight=points, target=denominator,
identifier_parent=identifier_parent,
numerator_id=numerator_id))
max_points = sum([score[0] for score in scores])
actual_points = sum([score[0] * score[1] for score in scores])
percentage = 0 if max_points == 0 else round(
(actual_points / float(max_points)) * 100, 2)
kpi_name = main_child[self.template.TRANSLATION]
kpi_fk = self.kpi_static_data[self.kpi_static_data['kpi_name'].str.encode(HelperConsts.UTF8) ==
kpi_name.encode(HelperConsts.UTF8)]['kpi_fk'].values[0]
self.write_to_db_result(kpi_fk, (actual_points, max_points,
percentage), level=self.LEVEL2)
scores_dict[kpi_name] = (max_points, actual_points)
results_list_new_db.append(self.get_new_kpi_dict(main_child_kpi_fk, percentage, percentage,
actual_points, max_points,
target=max_points,
weight=actual_points,
identifier_result=main_kpi_identifier,
identifier_parent=self.RED_SCORE))
return results_list_new_db, scores_dict
def validate_store_type(self, params):
"""
This function checks whether or not a KPI is relevant for calculation, by the session's store type.
"""
validation = False
stores = params[self.template.STORE_TYPE]
if not stores:
validation = True
elif isinstance(stores, (str, unicode)):
if stores.upper() == self.template.ALL or self.store_type in stores.split(self.template.SEPARATOR):
validation = True
elif isinstance(stores, list):
if self.store_type in stores:
validation = True
return validation
def validate_kpi_run(self, params):
"""
This function checks whether or not a KPI Atomic needs to be calculated, based on a customized template.
"""
weight = params[self.template.WEIGHT]
if str(weight).isdigit():
validation = True
else:
kpi_group = params[self.template.KPI_GROUP]
if kpi_group == 'Visibility':
custom_template = self.visibility_info
elif kpi_group in ('Ambient Space', 'Cooler Space'):
custom_template = self.cooler_info
else:
return False
condition = (custom_template[self.template.KPI_NAME] == params[self.template.KPI_NAME])
if self.template.KPI_GROUP in custom_template.keys() and kpi_group != 'Visibility':
condition &= (custom_template[self.template.KPI_GROUP]
== params[self.template.KPI_GROUP])
kpi_data = custom_template[condition]
if kpi_data.empty:
return False
try:
weight = \
kpi_data[
kpi_data['store_type'].str.encode(HelperConsts.UTF8) == self.store_type.encode(
HelperConsts.UTF8)][
'Target'].values[0]
validation = float(weight)
except ValueError:
validation = False
except IndexError:
Log.warning("{kpi}: No matching external targets for this session: {sess}".format(
kpi=kpi_group,
sess=self.session_uid))
validation = False
return validation
def get_atomic_fk(self, pillar, params):
"""
This function gets an Atomic KPI's FK out of the template data.
"""
atomic_name = params[self.template.TRANSLATION]
kpi_name = pillar[self.template.TRANSLATION]
atomic_fk = self.kpi_static_data[(self.kpi_static_data['kpi_name'].str.encode(HelperConsts.UTF8) ==
kpi_name.encode(HelperConsts.UTF8)) & (
self.kpi_static_data['atomic_kpi_name'].str.encode(
HelperConsts.UTF8) == atomic_name.encode(HelperConsts.UTF8))][
'atomic_kpi_fk']
if atomic_fk.empty:
return None
return atomic_fk.values[0]
def get_new_kpi_fk(self, params):
"""
This function gets an KPI's FK from new kpi table 'static.kpi_level_2' out of the template data .
"""
kpi_name = params[self.template.TRANSLATION]
return self.get_new_kpi_by_name(kpi_name)
def get_new_kpi_by_name(self, kpi_name):
kpi_fk = self.kpi_new_static_data[self.kpi_new_static_data['type'].str.encode(HelperConsts.UTF8) ==
kpi_name.encode(HelperConsts.UTF8)]['pk']
if kpi_fk.empty:
return None
return kpi_fk.values[0]
def get_scene_types(self, params):
"""
This function extracts the relevant scene types (==additional_attribute_1) from the template.
"""
scene_types = params[self.template.SCENE_TYPE]
if not scene_types or (isinstance(scene_types, (str, unicode)) and scene_types.upper() == self.template.ALL):
return None
return scene_types
def calculate_availability(self, availability_kpi_df):
"""
This function calculates Availability typed Atomics from a customized template, and saves the results to the DB.
"""
all_targets = availability_kpi_df.target.unique()
if not all_targets:
return False, False, False
target = float(all_targets[0])
total_facings_count = availability_kpi_df.facings.sum()
score = 1 if total_facings_count >= target else 0
return score, total_facings_count, target
def check_survey(self, params):
"""
This function calculates Survey typed Atomics, and saves the results to the DB.
"""
survey_id = int(float(params[self.template.SURVEY_ID]))
target_answer = params[self.template.SURVEY_ANSWER]
survey_answer, survey_answer_fk = self.general_tools.get_survey_answer(survey_data=('question_fk', survey_id))
score = 1 if survey_answer == target_answer else 0
return score, survey_answer, target_answer, survey_answer_fk
def calculate_share_of_shelf(self, params):
"""
This function calculates Facings Share of Shelf typed Atomics, and saves the results to the DB.
"""
if params[self.template.SOS_NUMERATOR].startswith('~'):
sos_filters = {params[self.template.SOS_ENTITY]: (params[self.template.SOS_NUMERATOR][1:],
self.general_tools.EXCLUDE_FILTER)}
else:
sos_filters = {params[self.template.SOS_ENTITY]: params[self.template.SOS_NUMERATOR]}
general_filters = {}
scene_types = self.get_scene_types(params)
if isinstance(scene_types, (str, unicode)):
scene_types = scene_types.split(self.template.SEPARATOR)
if scene_types:
general_filters[SCENE_TYPE_FIELD] = scene_types
products_to_exclude = params[self.template.PRODUCT_TYPES_TO_EXCLUDE]
if products_to_exclude:
general_filters['product_type'] = (products_to_exclude.split(self.template.SEPARATOR),
self.general_tools.EXCLUDE_FILTER)
numerator_result = self.general_tools.calculate_availability(
**dict(sos_filters, **general_filters))
denominator_result = self.general_tools.calculate_availability(**general_filters)
if denominator_result == 0:
result = 0
else:
result = round((numerator_result / float(denominator_result)) * 100, 2)
if params[self.template.TARGET]:
target = float(params[self.template.TARGET]) * 100
score = 1 if result >= target else 0
else:
score = target = None
result_string = '{0}% ({1}/{2})'.format(result, int(numerator_result), int(denominator_result))
return score, result_string, target, result, numerator_result, denominator_result
def write_to_db_result(self, fk, score, level):
"""
This function creates the result data frame of every KPI (atomic KPI/KPI/KPI set),
and appends the insert SQL query into the queries' list, later to be written to the DB.
"""
attributes = self.create_attributes_dict(fk, score, level)
if level == self.LEVEL1:
table = KPS_RESULT
elif level == self.LEVEL2:
table = KPK_RESULT
elif level == self.LEVEL3:
table = KPI_RESULT
else:
return
query = insert(attributes, table)
self.kpi_results_queries.append(query)
def create_attributes_dict(self, fk, score, level):
"""
This function creates a data frame with all attributes needed for saving in KPI results tables.
"""
if level == self.LEVEL1:
score_2, score_3, score_1 = score
kpi_set_name = self.kpi_static_data[self.kpi_static_data['kpi_set_fk']
== fk]['kpi_set_name'].values[0]
attributes = pd.DataFrame([(kpi_set_name, self.session_uid, self.store_id, self.visit_date.isoformat(),
format(score_1, '.2f'), score_2, score_3, fk)],
columns=['kps_name', 'session_uid', 'store_fk', 'visit_date', 'score_1',
'score_2', 'score_3', 'kpi_set_fk'])
elif level == self.LEVEL2:
score_2, score_3, score = score
kpi_name = self.kpi_static_data[self.kpi_static_data['kpi_fk']
== fk]['kpi_name'].values[0]
attributes = pd.DataFrame([(self.session_uid, self.store_id, self.visit_date.isoformat(),
fk, kpi_name, score, score_2, score_3)],
columns=['session_uid', 'store_fk', 'visit_date', 'kpi_fk', 'kpk_name',
'score', 'score_2', 'score_3'])
elif level == self.LEVEL3:
score, result, threshold, weight = score
data = self.kpi_static_data[self.kpi_static_data['atomic_kpi_fk'] == fk]
atomic_kpi_name = data['atomic_kpi_name'].values[0]
kpi_fk = data['kpi_fk'].values[0]
kpi_set_name = self.kpi_static_data[self.kpi_static_data['atomic_kpi_fk']
== fk]['kpi_set_name'].values[0]
attributes = pd.DataFrame([(atomic_kpi_name, self.session_uid, kpi_set_name, self.store_id,
self.visit_date.isoformat(), datetime.utcnow().isoformat(),
score, kpi_fk, fk, threshold, result, weight)],
columns=['display_text', 'session_uid', 'kps_name', 'store_fk', 'visit_date',
'calculation_time', 'score', 'kpi_fk', 'atomic_kpi_fk', 'threshold',
'result', 'kpi_weight'])
else:
attributes = pd.DataFrame()
return attributes.to_dict()
@log_runtime('Saving to DB')
def commit_results_data(self):
"""
This function writes all KPI results to the DB, and commits the changes.
"""
cur = self.rds_conn.db.cursor()
delete_queries = CCKHQueries.get_delete_session_results_query(self.session_uid)
for query in delete_queries:
cur.execute(query)
for query in self.kpi_results_queries:
cur.execute(query)
self.rds_conn.db.commit()
def get_new_kpi_dict(self, kpi_fk, result, score, numerator_result, denominator_result,
score_after_action=0, weight=None, target=None, identifier_parent=None,
identifier_result=None, numerator_id=None):
"""
This function gets all kpi info and add the relevant numerator_id and denominator_id and return a dictionary
with the passed data.
:param kpi_fk: pk of kpi
:param result
:param score
:param numerator_result
:param denominator_result
:param weight
:param target
:param identifier_parent
:param identifier_result
:param numerator_id
:param score_after_action
:returns dict in format of db result
"""
numerator_id = self.manufacturer if numerator_id is None else numerator_id
denominator_id = self.store_id
return {'fk': kpi_fk,
SessionResultsConsts.NUMERATOR_ID: numerator_id,
SessionResultsConsts.DENOMINATOR_ID: denominator_id,
SessionResultsConsts.DENOMINATOR_RESULT: denominator_result,
SessionResultsConsts.NUMERATOR_RESULT: numerator_result,
SessionResultsConsts.RESULT: result, SessionResultsConsts.SCORE: score,
SessionResultsConsts.TARGET: target, SessionResultsConsts.WEIGHT: weight,
'identifier_parent': identifier_parent, 'identifier_result': identifier_result,
'score_after_actions': score_after_action, 'should_enter': True,
}
def __init__(self, data_provider, output):
GlobalSessionToolBox.__init__(self, data_provider, output)
self.assortment = Assortment(self.data_provider, self.output)
class ALTRIAUS_SANDToolBox:
LEVEL1 = 1
LEVEL2 = 2
LEVEL3 = 3
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.common = Common(self.data_provider)
self.common_v2 = CommonV2(self.data_provider)
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.template_info = self.data_provider.all_templates
self.rds_conn = ProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.ps_data_provider = PsDataProvider(self.data_provider)
self.thresholds_and_results = {}
self.result_df = []
self.writing_to_db_time = datetime.timedelta(0)
self.kpi_results_queries = []
self.potential_products = {}
self.shelf_square_boundaries = {}
self.average_shelf_values = {}
self.kpi_static_data = self.common.get_kpi_static_data()
self.kpi_results_queries = []
self.all_template_data = parse_template(TEMPLATE_PATH, "KPI")
self.spacing_template_data = parse_template(TEMPLATE_PATH, "Spacing")
self.fixture_width_template = pd.read_excel(FIXTURE_WIDTH_TEMPLATE,
"Fixture Width",
dtype=pd.Int64Dtype())
self.facings_to_feet_template = pd.read_excel(FIXTURE_WIDTH_TEMPLATE,
"Conversion Table",
dtype=pd.Int64Dtype())
self.header_positions_template = pd.read_excel(FIXTURE_WIDTH_TEMPLATE,
"Header Positions")
self.flip_sign_positions_template = pd.read_excel(
FIXTURE_WIDTH_TEMPLATE, "Flip Sign Positions")
self.custom_entity_data = self.ps_data_provider.get_custom_entities(
1005)
self.ignore_stacking = False
self.facings_field = 'facings' if not self.ignore_stacking else 'facings_ign_stack'
self.INCLUDE_FILTER = 1
self.assortment = Assortment(self.data_provider,
output=self.output,
ps_data_provider=self.ps_data_provider)
self.store_assortment = self.assortment.get_lvl3_relevant_ass()
self.kpi_new_static_data = self.common.get_new_kpi_static_data()
try:
self.mpis = self.match_product_in_scene.merge(self.products, on='product_fk', suffixes=['', '_p']) \
.merge(self.scene_info, on='scene_fk', suffixes=['', '_s']) \
.merge(self.template_info, on='template_fk', suffixes=['', '_t'])
except KeyError:
Log.warning('MPIS cannot be generated!')
return
self.adp = AltriaDataProvider(self.data_provider)
def main_calculation(self, *args, **kwargs):
"""
This function calculates the KPI results.
"""
self.calculate_signage_locations_and_widths('Cigarettes')
self.calculate_signage_locations_and_widths('Smokeless')
self.calculate_register_type()
self.calculate_age_verification()
self.calculate_juul_availability()
self.calculate_assortment()
self.calculate_vapor_kpis()
kpi_set_fk = 2
set_name = \
self.kpi_static_data.loc[self.kpi_static_data['kpi_set_fk'] == kpi_set_fk]['kpi_set_name'].values[0]
template_data = self.all_template_data.loc[
self.all_template_data['KPI Level 1 Name'] == set_name]
try:
if set_name and not set(
template_data['Scene Types to Include'].values[0].encode(
).split(', ')) & set(
self.scif['template_name'].unique().tolist()):
Log.info('Category {} was not captured'.format(
template_data['category'].values[0]))
return
except Exception as e:
Log.info(
'KPI Set {} is not defined in the template'.format(set_name))
for i, row in template_data.iterrows():
try:
kpi_name = row['KPI Level 2 Name']
if kpi_name in KPI_LEVEL_2_cat_space:
# scene_type = [s for s in row['Scene_Type'].encode().split(', ')]
kpi_type = row['KPI Type']
scene_type = row['scene_type']
if row['Param1'] == 'Category' or 'sub_category':
category = row['Value1']
if kpi_type == 'category_space':
kpi_set_fk = \
self.kpi_new_static_data.loc[self.kpi_new_static_data['type'] == kpi_type]['pk'].values[0]
self.calculate_category_space(
kpi_set_fk,
kpi_name,
category,
scene_types=scene_type)
except Exception as e:
Log.info('KPI {} calculation failed due to {}'.format(
kpi_name.encode('utf-8'), e))
continue
return
def calculate_vapor_kpis(self):
category = 'Vapor'
relevant_scif = self.scif[self.scif['template_name'] ==
'JUUL Merchandising']
if relevant_scif.empty:
Log.info('No products found for {} category'.format(category))
return
relevant_scif = relevant_scif[
(relevant_scif['category'].isin([category, 'POS']))
& (relevant_scif['brand_name'] == 'Juul')]
if relevant_scif.empty:
return
relevant_product_pks = relevant_scif[
relevant_scif['product_type'] ==
'SKU']['product_fk'].unique().tolist()
relevant_scene_id = self.get_most_frequent_scene(relevant_scif)
product_mpis = self.mpis[
(self.mpis['product_fk'].isin(relevant_product_pks))
& (self.mpis['scene_fk'] == relevant_scene_id)]
if product_mpis.empty:
Log.info('No products found for {} category'.format(category))
return
self.calculate_total_shelves(product_mpis, category, product_mpis)
longest_shelf = \
product_mpis[product_mpis['shelf_number'] ==
self.get_longest_shelf_number(product_mpis,
max_shelves_from_top=999)].sort_values(by='rect_x',
ascending=True)
if longest_shelf.empty or longest_shelf.isnull().all().all():
Log.warning(
'The {} category items are in a non-standard location. The {} category will not be calculated.'
.format(category, category))
return
relevant_pos = pd.DataFrame()
self.calculate_fixture_width(relevant_pos, longest_shelf, category)
return
def calculate_assortment(self):
if self.scif.empty or self.store_assortment.empty:
Log.warning(
'Unable to calculate assortment: SCIF or store assortment is empty'
)
return
grouped_scif = self.scif.groupby('product_fk',
as_index=False)['facings'].sum()
assortment_with_facings = \
pd.merge(self.store_assortment, grouped_scif, how='left', on='product_fk')
assortment_with_facings.loc[:, 'facings'] = assortment_with_facings[
'facings'].fillna(0)
for product in assortment_with_facings.itertuples():
score = 1 if product.facings > 0 else 0
self.common_v2.write_to_db_result(
product.kpi_fk_lvl3,
numerator_id=product.product_fk,
denominator_id=product.assortment_fk,
numerator_result=product.facings,
result=product.facings,
score=score)
number_of_skus_present = len(
assortment_with_facings[assortment_with_facings['facings'] > 0])
score = 1 if number_of_skus_present > 0 else 0
kpi_fk = assortment_with_facings['kpi_fk_lvl2'].iloc[0]
assortment_group_fk = assortment_with_facings[
'assortment_group_fk'].iloc[0]
self.common_v2.write_to_db_result(
kpi_fk,
numerator_id=assortment_group_fk,
numerator_result=number_of_skus_present,
denominator_result=len(assortment_with_facings),
result=number_of_skus_present,
score=score)
def calculate_register_type(self):
relevant_scif = self.scif[
(self.scif['product_type'].isin(['POS', 'Other']))
& (self.scif['category'] == 'POS Machinery')]
if relevant_scif.empty:
result = 0
product_fk = 0
else:
result = 1
product_fk = relevant_scif['product_fk'].iloc[0]
kpi_fk = self.common_v2.get_kpi_fk_by_kpi_type('Register Type')
self.common_v2.write_to_db_result(kpi_fk,
numerator_id=product_fk,
denominator_id=self.store_id,
result=result)
def calculate_age_verification(self):
relevant_scif = self.scif[self.scif['brand_name'].isin(
['Age Verification'])]
if relevant_scif.empty:
result = 0
product_fk = 0
else:
result = 1
product_fk = relevant_scif['product_fk'].iloc[0]
kpi_fk = self.common_v2.get_kpi_fk_by_kpi_type('Age Verification')
self.common_v2.write_to_db_result(kpi_fk,
numerator_id=product_fk,
denominator_id=self.store_id,
result=result)
def calculate_juul_availability(self):
relevant_scif = self.scif[(self.scif['brand_name'].isin(['Juul']))
& (self.scif['product_type'].isin(['POS']))]
juul_pos = relevant_scif['product_fk'].unique().tolist()
kpi_fk = self.common_v2.get_kpi_fk_by_kpi_type('Juul POS Availability')
if not juul_pos:
return
result = 1
for product_fk in juul_pos:
self.common_v2.write_to_db_result(kpi_fk,
numerator_id=product_fk,
denominator_id=self.store_id,
result=result)
def calculate_category_space(self,
kpi_set_fk,
kpi_name,
category,
scene_types=None):
template = self.all_template_data.loc[
(self.all_template_data['KPI Level 2 Name'] == kpi_name)
& (self.all_template_data['Value1'] == category)]
kpi_template = template.loc[template['KPI Level 2 Name'] == kpi_name]
if kpi_template.empty:
return None
kpi_template = kpi_template.iloc[0]
values_to_check = []
filters = {
'template_name': scene_types,
'category': kpi_template['Value1']
}
if kpi_template['Value1'] in CATEGORIES:
category_att = 'category'
if kpi_template['Value1']:
values_to_check = self.all_products.loc[
self.all_products[category_att] ==
kpi_template['Value1']][category_att].unique().tolist()
for primary_filter in values_to_check:
filters[kpi_template['Param1']] = primary_filter
new_kpi_name = self.kpi_name_builder(kpi_name, **filters)
result = self.calculate_category_space_length(
new_kpi_name, **filters)
filters['Category'] = kpi_template['KPI Level 2 Name']
score = result
numerator_id = self.products['category_fk'][
self.products['category'] == kpi_template['Value1']].iloc[0]
self.common_v2.write_to_db_result(kpi_set_fk,
numerator_id=numerator_id,
numerator_result=999,
result=score,
score=score)
def calculate_category_space_length(self,
kpi_name,
threshold=0.5,
retailer=None,
exclude_pl=False,
**filters):
"""
:param threshold: The ratio for a bay to be counted as part of a category.
:param filters: These are the parameters which the data frame is filtered by.
:return: The total shelf width (in mm) the relevant facings occupy.
"""
try:
filtered_scif = self.scif[self.get_filter_condition(
self.scif, **filters)]
space_length = 0
bay_values = []
for scene in filtered_scif['scene_fk'].unique().tolist():
scene_matches = self.mpis[self.mpis['scene_fk'] == scene]
scene_filters = filters
scene_filters['scene_fk'] = scene
for bay in scene_matches['bay_number'].unique().tolist():
bay_total_linear = scene_matches.loc[
(scene_matches['bay_number'] == bay)
& (scene_matches['stacking_layer'] == 1) &
(scene_matches['status']
== 1)]['width_mm_advance'].sum()
scene_filters['bay_number'] = bay
tested_group_linear = scene_matches[
self.get_filter_condition(scene_matches,
**scene_filters)]
tested_group_linear_value = tested_group_linear[
'width_mm_advance'].sum()
if tested_group_linear_value:
bay_ratio = tested_group_linear_value / float(
bay_total_linear)
else:
bay_ratio = 0
if bay_ratio >= threshold:
category = filters['category']
max_facing = scene_matches.loc[
(scene_matches['bay_number'] == bay)
& (scene_matches['stacking_layer'] == 1
)]['facing_sequence_number'].max()
shelf_length = self.spacing_template_data.query(
'Category == "' + category + '" & Low <= "' +
str(max_facing) + '" & High >= "' +
str(max_facing) + '"')
shelf_length = int(shelf_length['Size'].iloc[-1])
bay_values.append(shelf_length)
space_length += shelf_length
except Exception as e:
Log.info('Linear Feet calculation failed due to {}'.format(e))
space_length = 0
return space_length
def get_filter_condition(self, df, **filters):
"""
:param df: The data frame to be filters.
:param filters: These are the parameters which the data frame is filtered by.
Every parameter would be a tuple of the value and an include/exclude flag.
INPUT EXAMPLE (1): manufacturer_name = ('Diageo', DIAGEOAUPNGROGENERALToolBox.INCLUDE_FILTER)
INPUT EXAMPLE (2): manufacturer_name = 'Diageo'
:return: a filtered Scene Item Facts data frame.
"""
if not filters:
return df['pk'].apply(bool)
if self.facings_field in df.keys():
filter_condition = (df[self.facings_field] > 0)
else:
filter_condition = None
for field in filters.keys():
if field in df.keys():
if isinstance(filters[field], tuple):
value, exclude_or_include = filters[field]
else:
value, exclude_or_include = filters[
field], self.INCLUDE_FILTER
if not value:
continue
if not isinstance(value, list):
value = [value]
if exclude_or_include == self.INCLUDE_FILTER:
condition = (df[field].isin(value))
elif exclude_or_include == self.EXCLUDE_FILTER:
condition = (~df[field].isin(value))
elif exclude_or_include == self.CONTAIN_FILTER:
condition = (df[field].str.contains(value[0], regex=False))
for v in value[1:]:
condition |= df[field].str.contains(v, regex=False)
else:
continue
if filter_condition is None:
filter_condition = condition
else:
filter_condition &= condition
else:
Log.warning('field {} is not in the Data Frame'.format(field))
return filter_condition
def kpi_name_builder(self, kpi_name, **filters):
"""
This function builds kpi name according to naming convention
"""
for filter in filters.keys():
if filter == 'template_name':
continue
kpi_name = kpi_name.replace('{' + filter + '}',
str(filters[filter]))
kpi_name = kpi_name.replace("'", "\'")
return kpi_name
def calculate_signage_locations_and_widths(self, category):
excluded_types = ['Other', 'Irrelevant', 'Empty']
relevant_scif = self.scif[self.scif['template_name'] ==
'Tobacco Merchandising Space']
if relevant_scif.empty:
Log.info('No products found for {} category'.format(category))
return
# need to include scene_id from previous relevant_scif
# also need to split this shit up into different categories, i.e. smokeless, cigarettes
# need to figure out how to deal with POS from smokeless being included with cigarette MPIS
# get relevant SKUs from the cigarettes category
relevant_scif = relevant_scif[relevant_scif['category'].isin(
[category, 'POS'])]
relevant_product_pks = relevant_scif[
relevant_scif['product_type'] ==
'SKU']['product_fk'].unique().tolist()
relevant_pos_pks = \
relevant_scif[(relevant_scif['product_type'] == 'POS') &
~(relevant_scif['brand_name'] == 'Age Verification') &
~(relevant_scif['product_name'] == 'General POS Other')]['product_fk'].unique().tolist()
other_product_and_pos_pks = \
relevant_scif[relevant_scif['product_type'].isin(excluded_types)]['product_fk'].tolist()
relevant_scene_id = self.get_most_frequent_scene(relevant_scif)
product_mpis = self.mpis[
(self.mpis['product_fk'].isin(relevant_product_pks))
& (self.mpis['scene_fk'] == relevant_scene_id)]
if product_mpis.empty:
Log.info('No products found for {} category'.format(category))
return
self.calculate_total_shelves(product_mpis, category)
longest_shelf = \
product_mpis[product_mpis['shelf_number'] ==
self.get_longest_shelf_number(product_mpis)].sort_values(by='rect_x', ascending=True)
if longest_shelf.empty or longest_shelf.isnull().all().all():
Log.warning(
'The {} category items are in a non-standard location. The {} category will not be calculated.'
.format(category, category))
return
# demarcation_line = longest_shelf['rect_y'].median() old method, had bugs due to longest shelf being lower
demarcation_line = product_mpis['rect_y'].min()
exclusion_line = -9999
excluded_mpis = self.mpis[
~(self.mpis['product_fk'].isin(relevant_pos_pks +
relevant_product_pks +
other_product_and_pos_pks))
& (self.mpis['rect_x'] < longest_shelf['rect_x'].max()) &
(self.mpis['rect_x'] > longest_shelf['rect_x'].min()) &
(self.mpis['scene_fk'] == relevant_scene_id) &
(self.mpis['rect_y'] < demarcation_line)]
# we need this line for when SCIF and MPIS don't match
excluded_mpis = excluded_mpis[~excluded_mpis['product_type'].
isin(excluded_types)]
if not excluded_mpis.empty:
exclusion_line = excluded_mpis['rect_y'].max()
# we need to get POS stuff that falls within the x-range of the longest shelf (which is limited by category)
# we also need to account for the fact that the images suck, so we're going to add/subtract 5% of the
# max/min values to allow for POS items that fall slightly out of the shelf length range
correction_factor = 0.05
correction_value = (longest_shelf['rect_x'].max() -
longest_shelf['rect_x'].min()) * correction_factor
pos_mpis = self.mpis[
(self.mpis['product_fk'].isin(relevant_pos_pks))
& (self.mpis['rect_x'] <
(longest_shelf['rect_x'].max() + correction_value)) &
(self.mpis['rect_x'] >
(longest_shelf['rect_x'].min() - correction_value)) &
(self.mpis['scene_fk'] == relevant_scene_id)]
# DO NOT SET TO TRUE WHEN DEPLOYING
# debug flag displays polygon_mask graph DO NOT SET TO TRUE WHEN DEPLOYING
# DO NOT SET TO TRUE WHEN DEPLOYING
relevant_pos = self.adp.get_products_contained_in_displays(
pos_mpis, y_axis_threshold=35, debug=False)
if relevant_pos.empty:
Log.warning(
'No polygon mask was generated for {} category - cannot compute KPIs'
.format(category))
# we need to attempt to calculate fixture width, even if there's no polygon mask
self.calculate_fixture_width(relevant_pos, longest_shelf, category)
return
relevant_pos = relevant_pos[[
'product_fk', 'product_name', 'left_bound', 'right_bound',
'center_x', 'center_y'
]]
relevant_pos = relevant_pos.reindex(
columns=relevant_pos.columns.tolist() +
['type', 'width', 'position'])
relevant_pos['width'] = \
relevant_pos.apply(lambda row: self.get_length_of_pos(row, longest_shelf, category), axis=1)
relevant_pos['type'] = \
relevant_pos['center_y'].apply(lambda x: 'Header' if exclusion_line < x < demarcation_line else 'Flip Sign')
relevant_pos = relevant_pos.sort_values(['center_x'], ascending=True)
relevant_pos = self.remove_duplicate_pos_tags(relevant_pos)
# generate header positions
if category == 'Cigarettes':
number_of_headers = len(
relevant_pos[relevant_pos['type'] == 'Header'])
if number_of_headers > len(
self.header_positions_template['Cigarettes Positions'].
dropna()):
Log.warning(
'Number of Headers for Cigarettes is greater than max number defined in template!'
)
elif number_of_headers > 0:
header_position_list = [
position.strip()
for position in self.header_positions_template[
self.header_positions_template['Number of Headers'] ==
number_of_headers]
['Cigarettes Positions'].iloc[0].split(',')
]
relevant_pos.loc[relevant_pos['type'] == 'Header',
['position']] = header_position_list
elif category == 'Smokeless':
relevant_pos = self.check_menu_scene_recognition(relevant_pos)
number_of_headers = len(
relevant_pos[relevant_pos['type'] == 'Header'])
if number_of_headers > len(
self.header_positions_template['Smokeless Positions'].
dropna()):
Log.warning(
'Number of Headers for Smokeless is greater than max number defined in template!'
)
elif number_of_headers > 0:
header_position_list = [
position.strip()
for position in self.header_positions_template[
self.header_positions_template['Number of Headers'] ==
number_of_headers]
['Smokeless Positions'].iloc[0].split(',')
]
relevant_pos.loc[relevant_pos['type'] == 'Header',
['position']] = header_position_list
relevant_pos = self.get_menu_board_items(relevant_pos,
longest_shelf, pos_mpis)
# generate flip-sign positions
if category == 'Cigarettes':
relevant_template = \
self.fixture_width_template.loc[(self.fixture_width_template['Fixture Width (facings)']
- len(longest_shelf)).abs().argsort()[:1]].dropna(axis=1)
locations = relevant_template.columns[2:].tolist()
right_bound = 0
longest_shelf_copy = longest_shelf.copy()
for location in locations:
if right_bound > 0:
left_bound = right_bound + 1
else:
left_bound = longest_shelf_copy.iloc[:relevant_template[
location].iloc[0]]['rect_x'].min()
right_bound = longest_shelf_copy.iloc[:relevant_template[
location].iloc[0]]['rect_x'].max()
if locations[-1] == location:
right_bound = right_bound + abs(right_bound * 0.05)
flip_sign_pos = relevant_pos[
(relevant_pos['type'] == 'Flip Sign')
& (relevant_pos['center_x'] > left_bound) &
(relevant_pos['center_x'] < right_bound)]
if flip_sign_pos.empty:
# add 'NO Flip Sign' product_fk
relevant_pos.loc[len(relevant_pos), ['position', 'product_fk', 'type']] = \
[location, NO_FLIP_SIGN_PK, 'Flip Sign']
else:
relevant_pos.loc[flip_sign_pos.index,
['position']] = location
longest_shelf_copy.drop(
longest_shelf_copy.iloc[:relevant_template[location].
iloc[0]].index,
inplace=True)
elif category == 'Smokeless':
# if there are no flip signs found, there are no positions to assign
number_of_flip_signs = len(
relevant_pos[relevant_pos['type'] == 'Flip Sign'])
if number_of_flip_signs > self.flip_sign_positions_template[
'Number of Flip Signs'].max():
Log.warning(
'Number of Flip Signs for Smokeless is greater than max number defined in template!'
)
elif number_of_flip_signs > 0:
flip_sign_position_list = [
position.strip()
for position in self.flip_sign_positions_template[
self.
flip_sign_positions_template['Number of Flip Signs'] ==
number_of_flip_signs]['Position'].iloc[0].split(',')
]
relevant_pos.loc[relevant_pos['type'] == 'Flip Sign',
['position']] = flip_sign_position_list
# store empty flip sign values
for location in ['Secondary', 'Tertiary']:
if location not in relevant_pos[
relevant_pos['type'] ==
'Flip Sign']['position'].tolist():
relevant_pos.loc[len(relevant_pos), ['position', 'product_fk', 'type']] = \
[location, NO_FLIP_SIGN_PK, 'Flip Sign']
relevant_pos = relevant_pos.reindex(
columns=relevant_pos.columns.tolist() + ['denominator_id'])
# this is a bandaid fix that should be removed -> 'F7011A7C-1BB6-4007-826D-2B674BD99DAE'
# removes POS items that were 'extra', i.e. more than max value in template
# only affects smokeless
relevant_pos.dropna(subset=['position'], inplace=True)
relevant_pos.loc[:,
['denominator_id']] = relevant_pos['position'].apply(
self.get_custom_entity_pk)
for row in relevant_pos.itertuples():
kpi_fk = self.common_v2.get_kpi_fk_by_kpi_name(row.type)
self.common_v2.write_to_db_result(
kpi_fk,
numerator_id=row.product_fk,
denominator_id=row.denominator_id,
result=row.width,
score=row.width)
self.calculate_fixture_width(relevant_pos, longest_shelf, category)
return
def calculate_total_shelves(self,
longest_shelf,
category,
product_mpis=None):
category_fk = self.get_category_fk_by_name(category)
if product_mpis is None:
product_mpis = self.mpis[
(self.mpis['rect_x'] > longest_shelf['rect_x'].min())
& (self.mpis['rect_x'] < longest_shelf['rect_x'].max()) &
(self.mpis['scene_fk']
== longest_shelf['scene_fk'].fillna(0).mode().iloc[0])]
total_shelves = len(product_mpis['shelf_number'].unique())
kpi_fk = self.common_v2.get_kpi_fk_by_kpi_name('Total Shelves')
self.common_v2.write_to_db_result(kpi_fk,
numerator_id=category_fk,
denominator_id=self.store_id,
result=total_shelves)
def calculate_fixture_width(self, relevant_pos, longest_shelf, category):
correction_factor = 1 if category == 'Smokeless' else 2
longest_shelf = longest_shelf[longest_shelf['stacking_layer'] == 1]
category_fk = self.get_category_fk_by_name(category)
# this is needed to remove intentionally duplicated 'Menu Board' POS 'Headers'
relevant_pos = relevant_pos.drop_duplicates(subset=['position'])
# try:
# width = relevant_pos[relevant_pos['type'] == 'Header']['width'].sum()
# except KeyError:
# # needed for when 'width' doesn't exist
# width = 0
# if relevant_pos.empty or width == 0:
width = round(
len(longest_shelf) + correction_factor /
float(self.facings_to_feet_template[category + ' Facings'].iloc[0])
)
kpi_fk = self.common_v2.get_kpi_fk_by_kpi_name('Fixture Width')
self.common_v2.write_to_db_result(kpi_fk,
numerator_id=category_fk,
denominator_id=self.store_id,
result=width)
def get_category_fk_by_name(self, category_name):
return self.all_products[self.all_products['category'] ==
category_name]['category_fk'].iloc[0]
def check_menu_scene_recognition(self, relevant_pos):
mdis = self.adp.get_match_display_in_scene()
mdis = mdis[mdis['display_name'] == 'Menu POS']
if mdis.empty:
return relevant_pos
dummy_sku_for_menu_pk = 9282 # 'Other (Smokeless Tobacco)'
dummy_sky_for_menu_name = 'Menu POS (Scene Recognized Item)'
location_type = 'Header'
width = 1
center_x = mdis['x'].iloc[0]
center_y = mdis['y'].iloc[0]
relevant_pos.loc[len(relevant_pos), ['product_fk', 'product_name', 'center_x', 'center_y', 'type', 'width']] = \
[dummy_sku_for_menu_pk, dummy_sky_for_menu_name, center_x, center_y, location_type, width]
relevant_pos = relevant_pos.sort_values(
['center_x'], ascending=True).reset_index(drop=True)
return relevant_pos
def get_menu_board_items(self, relevant_pos, longest_shelf, pos_mpis):
# get the placeholder item
menu_board_dummy = relevant_pos[relevant_pos['product_name'] ==
'Menu POS (Scene Recognized Item)']
# if no placeholder, this function isn't relevant
if menu_board_dummy.empty:
return relevant_pos
center_x = menu_board_dummy['center_x'].iloc[0]
center_y = menu_board_dummy['center_y'].iloc[0]
position = menu_board_dummy['position'].iloc[0]
demarcation_line = longest_shelf['rect_y'].min()
upper_demarcation_line = center_y - (demarcation_line - center_y)
distance_in_facings = 2
try:
left_bound = longest_shelf[
longest_shelf['rect_x'] < center_x].sort_values(
by=['rect_x'],
ascending=False)['rect_x'].iloc[int(distance_in_facings) -
1]
except IndexError:
# if there are no POS items found to the left of the 'Menu POS' scene recognition tag, use the tag itself
# in theory this should never happen
left_bound = center_x
try:
right_bound = longest_shelf[
longest_shelf['rect_x'] > center_x].sort_values(
by=['rect_x'],
ascending=True)['rect_x'].iloc[int(distance_in_facings) -
1]
except IndexError:
# if there are no POS items found to the right of the 'Menu POS' scene recognition tag, use the tag itself
# this is more likely to happen for the right bound than the left bound
right_bound = center_x
pos_mpis = pos_mpis[(pos_mpis['rect_x'] > left_bound)
& (pos_mpis['rect_x'] < right_bound) &
(pos_mpis['rect_y'] > upper_demarcation_line) &
(pos_mpis['rect_y'] < demarcation_line)]
if pos_mpis.empty:
return relevant_pos
# remove the placeholder item
relevant_pos = relevant_pos[~(relevant_pos['product_name'] ==
'Menu POS (Scene Recognized Item)')]
location_type = 'Header'
width = 1
for row in pos_mpis.itertuples():
relevant_pos.loc[len(relevant_pos), ['product_fk', 'product_name', 'center_x',
'center_y', 'type', 'width', 'position']] = \
[row.product_fk, row.product_name, row.rect_x, row.rect_y, location_type, width, position]
return relevant_pos
@staticmethod
def remove_duplicate_pos_tags(relevant_pos_df):
duplicate_results = \
relevant_pos_df[relevant_pos_df.duplicated(subset=['left_bound', 'right_bound'], keep=False)]
duplicate_results_without_other = duplicate_results[
~duplicate_results['product_name'].str.contains('Other')]
results_without_duplicates = \
relevant_pos_df[~relevant_pos_df.duplicated(subset=['left_bound', 'right_bound'], keep=False)]
if duplicate_results_without_other.empty:
return relevant_pos_df[~relevant_pos_df.duplicated(
subset=['left_bound', 'right_bound'], keep='first')]
else:
results = pd.concat(
[duplicate_results_without_other, results_without_duplicates])
# we need to sort_index to fix the sort order to reflect center_x values
return results.drop_duplicates(
subset=['left_bound', 'right_bound']).sort_index()
def get_custom_entity_pk(self, name):
return self.custom_entity_data[self.custom_entity_data['name'] ==
name]['pk'].iloc[0]
def get_length_of_pos(self, row, longest_shelf, category):
width_in_facings = len(
longest_shelf[(longest_shelf['rect_x'] > row['left_bound']) &
(longest_shelf['rect_x'] < row['right_bound'])]) + 2
category_facings = category + ' Facings'
return self.facings_to_feet_template.loc[(
self.facings_to_feet_template[category_facings] -
width_in_facings).abs().argsort()[:1]]['POS Width (ft)'].iloc[0]
@staticmethod
def get_longest_shelf_number(relevant_mpis, max_shelves_from_top=3):
# returns the shelf_number of the longest shelf
try:
longest_shelf = \
relevant_mpis[relevant_mpis['shelf_number'] <= max_shelves_from_top].groupby('shelf_number').agg(
{'scene_match_fk': 'count'})['scene_match_fk'].idxmax()
except ValueError:
longest_shelf = pd.DataFrame()
return longest_shelf
@staticmethod
def get_most_frequent_scene(relevant_scif):
try:
relevant_scene_id = relevant_scif['scene_id'].fillna(
0).mode().iloc[0]
except IndexError:
relevant_scene_id = 0
return relevant_scene_id
def commit(self):
self.common_v2.commit_results_data()
class PEPSICORUToolBox:
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.common = Common(self.data_provider)
self.common_v1 = CommonV1(self.data_provider)
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.store_info = self.data_provider[Data.STORE_INFO]
self.visit_type = self.store_info[
Const.ADDITIONAL_ATTRIBUTE_2].values[0]
self.all_templates = self.data_provider[Data.ALL_TEMPLATES]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.scif = self.scif.loc[~(self.scif[Const.PRODUCT_TYPE]
== Const.IRRELEVANT)] # Vitaly's request
self.rds_conn = PSProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.kpi_static_data = self.common.get_kpi_static_data()
self.kpi_results_queries = []
self.k_engine = BaseCalculationsGroup(data_provider, output)
self.toolbox = GENERALToolBox(data_provider)
self.assortment = Assortment(self.data_provider,
self.output,
common=self.common_v1)
if not self.scif.empty:
self.pepsico_fk = self.get_relevant_pk_by_name(
Const.MANUFACTURER, Const.PEPSICO)
self.categories_to_calculate = self.get_relevant_categories_for_session(
)
self.main_shelves = self.get_main_shelves()
def main_calculation(self):
"""
This function calculates the KPI results.
"""
self.calculate_share_of_shelf()
self.calculate_count_of_displays()
self.calculate_assortment()
def get_main_shelves(self):
"""
This function returns a list with the main shelves of this session
"""
main_shelves_template_groups = [
group
for group in self.scif[Const.TEMPLATE_GROUP].unique().tolist()
if Const.MAIN_SHELF in group.upper()
]
main_shelves = self.scif[self.scif[Const.TEMPLATE_GROUP].isin(
main_shelves_template_groups)][
Const.TEMPLATE_NAME].unique().tolist()
return main_shelves
def get_main_shelf_by_category(self, current_category):
"""
This function gets a category and return the relevant scene type for the SOS
:param current_category: One of the product's categories. E.g: Snacks.
:return: The relevant scene type to the current category
"""
main_shelves_for_category = []
for main_shelf in self.main_shelves:
if current_category.upper() in main_shelf.upper():
main_shelves_for_category.append(main_shelf)
return main_shelves_for_category
@staticmethod
def get_category_from_template_name(template_name):
"""
This function gets a template name (scene_type) and return it's relevant category.
:param template_name: The scene type.
:return: category name
"""
if Const.SNACKS.upper() in template_name.upper():
return Const.SNACKS
elif Const.BEVERAGES.upper() in template_name.upper():
return Const.BEVERAGES
elif Const.JUICES.upper() in template_name.upper():
return Const.JUICES
else:
Log.warning(
"Couldn't find a matching category for template name = {}".
format(template_name))
return None
def get_relevant_categories_for_session(self):
"""
This function returns a list of the relevant categories according to the store type.
The parameter additional_attribute_2 defines the visit type for each store.
We have 3 types: Visit LRB (Beverages and Juices), Visit Snack and Visit (= All of them).
The function is doing intersection between the categories in SCIF and the categories by store type.
:return: List of the relevant categories
"""
categories_in_scif = self.scif[Const.CATEGORY].unique().tolist()
if None in categories_in_scif:
categories_in_scif.remove(None)
if not categories_in_scif:
Log.warning("No categories at scene item facts!")
return []
store_type = self.store_info[Const.ADDITIONAL_ATTRIBUTE_2].values[0]
if not store_type:
Log.warning(
"Invalid additional_attribute_2 for store id = {}".format(
self.store_id))
return []
if Const.SNACKS.upper() in store_type.upper():
relevant_categories = [Const.SNACKS]
elif Const.LRB.upper() in store_type.upper():
relevant_categories = [Const.JUICES, Const.BEVERAGES]
else:
relevant_categories = [Const.SNACKS, Const.JUICES, Const.BEVERAGES]
categories_for_session = list(
set(relevant_categories).intersection(set(categories_in_scif)))
if not categories_for_session:
Log.warning(
"There aren't matching categories in scif for this store.")
return categories_for_session
def get_relevant_sub_categories_for_category(self, category):
"""
This function returns a list of the relevant categories according to the scene_types in the session
:param category: The relevant category
:return: List of the relevant sub categories for this category
"""
filtered_scif = self.scif.loc[
(self.scif[Const.CATEGORY] == category)
& (self.scif[Const.MANUFACTURER_NAME] == Const.PEPSICO) &
(self.scif[Const.TEMPLATE_NAME].isin(self.main_shelves))]
sub_categories = filtered_scif[Const.SUB_CATEGORY].unique().tolist()
if None in sub_categories:
sub_categories.remove(None)
if not sub_categories:
Log.warning("No relevant sub categories for category = {}".format(
category))
return sub_categories
def get_relevant_brands_for_sub_category(self, sub_category):
"""
This function returns a list of the relevant categories according to the scene_types in the session
:param sub_category: The relevant sub category
:return: List of the relevant brands for this category
"""
filtered_scif = self.scif.loc[
(self.scif[Const.SUB_CATEGORY] == sub_category)
& (self.scif[Const.MANUFACTURER_NAME] == Const.PEPSICO) &
(self.scif[Const.TEMPLATE_NAME].isin(self.main_shelves))]
brands_list = filtered_scif[Const.BRAND_NAME].unique().tolist()
if None in brands_list:
brands_list.remove(None)
if not brands_list:
Log.warning("No relevant brands for sub category = {}".format(
sub_category))
return brands_list
def get_relevant_pk_by_name(self, filter_by, filter_param):
"""
This function gets a filter name and returns the relevant pk.
If the filter_by is equal to category it will be the field name because in SCIF there isn't category_name
:param filter_by: filter by name E.g: 'category', 'brand'.
:param filter_param: The param to filter by. E.g: if filter_by = 'category', filter_param could be 'Snack'
:return: The relevant pk
"""
pk_field = filter_by + Const.FK
field_name = filter_by + Const.NAME if Const.CATEGORY not in filter_by else filter_by
return self.scif.loc[self.scif[field_name] ==
filter_param][pk_field].values[0]
def get_target_for_count_of_displays(self):
"""
This function reads the project's template and returns the targets for all of the levels.
It iterates over the relevant row and aggregate the result per level.
:return: target_by_store (int), target_by_category (dict), target_by_scene (dict).
Plus it return the scene_type list from the template
"""
targets = pd.read_excel(TEMPLATE_PATH).fillna(0)
store_number_1 = self.store_info['store_number_1'].values[0]
if not store_number_1:
Log.warning("No valid store number 1 for store_fk = {}".format(
self.store_id))
return
targets = targets.loc[targets[Const.STORE_NUMBER_1] == store_number_1]
has_targets = False
store_target, category_targets, scene_targets, scene_types_from_template = None, None, None, None
if not targets.empty:
has_targets = True
targets = targets.drop([Const.STORE_NAME, Const.STORE_NUMBER_1],
axis=1)
target_row = (targets.iloc[0])[(
targets.iloc[0]) > 0] # Takes only the ones with target > 0
scene_types_from_template = targets.columns.tolist()
scene_types_with_targets = target_row.keys().tolist()
# Targets by store:
store_target = target_row.sum()
# Targets by category and scenes:
category_targets = {key: 0 for key in self.categories_to_calculate}
scene_targets = {key: 0 for key in scene_types_from_template}
for scene_type in scene_types_with_targets:
for category in category_targets:
if category.upper() in scene_type.upper():
# category_targets[category] += 1
category_targets[category] += target_row[scene_type]
scene_targets[scene_type] = target_row[scene_type]
return has_targets, store_target, category_targets, scene_targets, scene_types_from_template
def get_relevant_scene_types_from_list(self, scene_types_from_template):
"""
There's a gap between the actual name and the name is the template because of the visit type.
So this function returns the a dictionary the narrows it.
:param scene_types_from_template: Scene type list from the template
:return: A dictionary where the keys are the names from the templates and values are the actual names
"""
scene_types_dict = dict.fromkeys(scene_types_from_template)
relevant_templates_for_visit_type = self.all_templates.loc[(
self.all_templates[Const.ADDITIONAL_ATTRIBUTE_2] == self.visit_type
) & (~self.all_templates[Const.TEMPLATE_NAME].isin(self.main_shelves)
)][Const.TEMPLATE_NAME].unique().tolist()
for scene_type in scene_types_from_template:
for template in relevant_templates_for_visit_type:
if scene_type.upper() in template.upper():
scene_types_dict[scene_type] = template
# Remove irrelevant scene types from the dictionary
for key in scene_types_dict.keys():
if not scene_types_dict[key]:
del scene_types_dict[key]
return scene_types_dict
def calculate_count_of_displays(self):
"""
This function will calculate the Count of # of Pepsi Displays KPI
:return:
"""
# Notice! store_target is Integer, scene_type_list is a list and the rest are dictionaries
has_target, store_target, category_targets, scene_targets, scene_type_list = self.get_target_for_count_of_displays(
)
if not store_target:
Log.warning(
"No targets were defined for this store (pk = {})".format(
self.store_id))
return
# Filtering out the main shelves
if has_target:
relevant_scenes_dict = self.get_relevant_scene_types_from_list(
scene_type_list)
relevant_template_name_list = relevant_scenes_dict.values()
filtered_scif = self.scif.loc[self.scif[Const.TEMPLATE_NAME].isin(
relevant_template_name_list)]
display_count_store_level_fk = self.common.get_kpi_fk_by_kpi_type(
Const.DISPLAY_COUNT_STORE_LEVEL)
scene_types_in_store = len(filtered_scif[Const.SCENE_FK].unique())
identifier_parent_store_level = self.common.get_dictionary(
kpi_fk=display_count_store_level_fk)
count_store_level = 0
# Calculate count of display - category_level
display_count_category_level_fk = self.common.get_kpi_fk_by_kpi_type(
Const.DISPLAY_COUNT_CATEGORY_LEVEL)
for category in self.categories_to_calculate:
current_category_target = category_targets[category]
if not current_category_target:
continue
category_fk = self.get_relevant_pk_by_name(
Const.CATEGORY, category)
relevant_scenes = [
scene_type for scene_type in relevant_template_name_list
if category.upper() in scene_type.upper()
]
filtered_scif_by_cat = filtered_scif.loc[filtered_scif[
Const.TEMPLATE_NAME].isin(relevant_scenes)]
display_count_category_level_identifier = self.common.get_dictionary(
kpi_fk=display_count_category_level_fk, category=category)
# scene_types_in_cate = 0
# result_cat_level = 0
# if not filtered_scif_by_cat.empty:
# scene_types_in_cate = len(filtered_scif_by_cat[Const.SCENE_FK].unique())
# result_cat_level = 1.0 if scene_types_in_cate >= current_category_target else scene_types_in_cate / float(
# current_category_target)
# self.common.write_to_db_result(fk=display_count_category_level_fk, numerator_id=self.pepsico_fk,
# numerator_result=scene_types_in_cate,
# denominator_id=category_fk, denominator_result=current_category_target,
# identifier_result=display_count_category_level_identifier,
# identifier_parent=identifier_parent_store_level,
# result=result_cat_level, should_enter=True)
scene_count_in_cate = 0
result_cat_level = 0
if not filtered_scif_by_cat.empty:
actual_scene_names_in_cate = filtered_scif_by_cat[
Const.TEMPLATE_NAME].unique().tolist()
reverse_scene_dict = {}
for scene_type, actual_scene_name in relevant_scenes_dict.iteritems(
):
for sc in actual_scene_names_in_cate:
if actual_scene_name == sc:
reverse_scene_dict[
actual_scene_name] = scene_type
df = filtered_scif_by_cat[[
Const.TEMPLATE_NAME, 'scene_id'
]].drop_duplicates()
df['scene_type'] = df[Const.TEMPLATE_NAME].apply(
lambda x: reverse_scene_dict.get(x))
by_scene_count_in_cat = df.groupby(['scene_type']).count()
for i, row in by_scene_count_in_cat.iterrows():
scene_count_in_cate += scene_targets[i] if row[Const.TEMPLATE_NAME]>=scene_targets[i] \
else row[Const.TEMPLATE_NAME]
result_cat_level = 1.0 if scene_count_in_cate >= current_category_target else scene_count_in_cate / float(
current_category_target)
self.common.write_to_db_result(
fk=display_count_category_level_fk,
numerator_id=self.pepsico_fk,
numerator_result=scene_count_in_cate,
denominator_id=category_fk,
denominator_result=current_category_target,
identifier_result=display_count_category_level_identifier,
identifier_parent=identifier_parent_store_level,
result=result_cat_level,
should_enter=True)
# Calculate count of display - scene_level
display_count_scene_level_fk = self.common.get_kpi_fk_by_kpi_type(
Const.DISPLAY_COUNT_SCENE_LEVEL)
for scene_type in relevant_scenes_dict.keys():
scene_type_target = scene_targets[scene_type]
if not scene_type_target:
continue
actual_scene_name = relevant_scenes_dict[scene_type]
relevant_category = self.get_category_from_template_name(
actual_scene_name)
relevant_category_fk = self.get_relevant_pk_by_name(
Const.CATEGORY, relevant_category)
scene_type_score = len(
filtered_scif[filtered_scif[Const.TEMPLATE_NAME] ==
actual_scene_name][Const.SCENE_FK].unique())
result_scene_level = 1.0 if scene_type_score >= scene_type_target else scene_type_score / float(
scene_type_target)
scene_type_fk = self.all_templates.loc[self.all_templates[
Const.TEMPLATE_NAME] == actual_scene_name][
Const.TEMPLATE_FK].values[0]
parent_identifier = self.common.get_dictionary(
kpi_fk=display_count_category_level_fk,
category=relevant_category)
self.common.write_to_db_result(
fk=display_count_scene_level_fk,
numerator_id=self.pepsico_fk,
numerator_result=scene_type_score,
denominator_id=relevant_category_fk,
denominator_result=scene_type_target,
identifier_parent=parent_identifier,
context_id=scene_type_fk,
result=result_scene_level,
should_enter=True)
count_store_level += scene_type_target if scene_type_score >= scene_type_target else scene_type_score
# Calculate count of display - store_level
result_store_level = 1.0 if count_store_level >= store_target else count_store_level / float(
store_target)
self.common.write_to_db_result(
fk=display_count_store_level_fk,
numerator_id=self.pepsico_fk,
numerator_result=count_store_level,
denominator_id=self.store_id,
denominator_result=store_target,
identifier_result=identifier_parent_store_level,
result=result_store_level,
should_enter=True)
# def calculate_count_of_displays_old(self):
# """
# This function will calculate the Count of # of Pepsi Displays KPI
# :return:
# """
# # Notice! store_target is Integer, scene_type_list is a list and the rest are dictionaries
# store_target, category_targets, scene_targets, scene_type_list = self.get_target_for_count_of_displays()
# if not store_target:
# Log.warning("No targets were defined for this store (pk = {})".format(self.store_id))
# return
# # Filtering out the main shelves
# relevant_scenes_dict = self.get_relevant_scene_types_from_list(scene_type_list)
# relevant_template_name_list = relevant_scenes_dict.values()
# filtered_scif = self.scif.loc[self.scif[Const.TEMPLATE_NAME].isin(relevant_template_name_list)]
#
# # Calculate count of display - store_level
# display_count_store_level_fk = self.common.get_kpi_fk_by_kpi_type(Const.DISPLAY_COUNT_STORE_LEVEL)
# scene_types_in_store = len(filtered_scif[Const.SCENE_FK].unique())
# result_store_level = 100 if scene_types_in_store >= store_target else scene_types_in_store / float(store_target)
# # self.common.write_to_db_result(fk=display_count_store_level_fk, numerator_id=self.pepsico_fk,
# # numerator_result=scene_types_in_store,
# # denominator_id=self.store_id, denominator_result=store_target,
# # identifier_result=display_count_store_level_fk,
# # result=result_store_level, should_enter=True)
#
# identifier_parent_store_level = self.common.get_dictionary(kpi_fk=display_count_store_level_fk)
# self.common.write_to_db_result(fk=display_count_store_level_fk, numerator_id=self.pepsico_fk,
# numerator_result=scene_types_in_store,
# denominator_id=self.store_id, denominator_result=store_target,
# identifier_result=identifier_parent_store_level,
# result=result_store_level, should_enter=True)
#
# # Calculate count of display - category_level
# display_count_category_level_fk = self.common.get_kpi_fk_by_kpi_type(Const.DISPLAY_COUNT_CATEGORY_LEVEL)
# for category in self.categories_to_calculate:
# current_category_target = category_targets[category]
# if not current_category_target:
# continue
# category_fk = self.get_relevant_pk_by_name(Const.CATEGORY, category)
# relevant_scenes = [scene_type for scene_type in relevant_template_name_list if
# category.upper() in scene_type.upper()]
# filtered_scif_by_cat = filtered_scif.loc[filtered_scif[Const.TEMPLATE_NAME].isin(relevant_scenes)]
# if filtered_scif_by_cat.empty:
# continue
# scene_types_in_cate = len(filtered_scif_by_cat[Const.SCENE_FK].unique())
# result_cat_level = 100 if scene_types_in_cate >= current_category_target else scene_types_in_cate / float(
# current_category_target)
# display_count_category_level_identifier = self.common.get_dictionary(kpi_fk=display_count_category_level_fk,
# category=category)
# # self.common.write_to_db_result(fk=display_count_store_level_fk, numerator_id=self.pepsico_fk,
# # numerator_result=scene_types_in_cate,
# # denominator_id=category_fk, denominator_result=current_category_target,
# # identifier_result=display_count_category_level_identifier,
# # identifier_parent=display_count_category_level_fk,
# # result=result_cat_level, should_enter=True)
# self.common.write_to_db_result(fk=display_count_store_level_fk, numerator_id=self.pepsico_fk,
# numerator_result=scene_types_in_cate,
# denominator_id=category_fk, denominator_result=current_category_target,
# identifier_result=display_count_category_level_identifier,
# identifier_parent=identifier_parent_store_level,
# result=result_cat_level, should_enter=True)
#
#
# # Calculate count of display - scene_level
# display_count_scene_level_fk = self.common.get_kpi_fk_by_kpi_type(Const.DISPLAY_COUNT_SCENE_LEVEL)
# for scene_type in relevant_scenes_dict.keys():
# scene_type_target = scene_targets[scene_type]
# if not scene_type_target:
# continue
# actual_scene_name = relevant_scenes_dict[scene_type]
# relevant_category = self.get_category_from_template_name(actual_scene_name)
# relevant_category_fk = self.get_relevant_pk_by_name(Const.CATEGORY, relevant_category)
# scene_type_score = len(
# filtered_scif[filtered_scif[Const.TEMPLATE_NAME] == actual_scene_name][Const.SCENE_FK].unique())
#
# result_scene_level = 100 if scene_type_score >= scene_type_target else scene_types_in_store / float(
# scene_type_target)
# scene_type_fk = self.all_templates.loc[self.all_templates[Const.TEMPLATE_NAME] == actual_scene_name][
# Const.TEMPLATE_FK].values[0]
# display_count_scene_level_identifier = self.common.get_dictionary(kpi_fk=display_count_category_level_fk,
# category=relevant_category)
# # parent_identifier = self.common.get_dictionary(kpi_fk=display_count_category_level_fk,
# # category=relevant_category)
# # self.common.write_to_db_result(fk=display_count_scene_level_fk, numerator_id=self.pepsico_fk,
# # numerator_result=scene_types_in_store,
# # denominator_id=relevant_category_fk, denominator_result=scene_type_target,
# # identifier_result=display_count_scene_level_identifier,
# # identifier_parent=parent_identifier, context_id=scene_type_fk,
# # result=result_scene_level, should_enter=True)
#
# self.common.write_to_db_result(fk=display_count_scene_level_fk, numerator_id=self.pepsico_fk,
# numerator_result=scene_types_in_store,
# denominator_id=relevant_category_fk, denominator_result=scene_type_target,
# identifier_result=display_count_scene_level_identifier,
# identifier_parent=identifier_parent_store_level, context_id=scene_type_fk,
# result=result_scene_level, should_enter=True)
def calculate_assortment(self):
lvl3_result = self.assortment.calculate_lvl3_assortment()
# lvl3_result = self.get_lvl3_assortment_result_main_shelf()
self.category_assortment_calculation(lvl3_result)
self.store_assortment_calculation(lvl3_result)
def get_lvl3_assortment_result_main_shelf(self):
assortment_result = self.assortment.get_lvl3_relevant_ass()
if not self.main_shelves and not assortment_result.empty:
assortment_result.drop(assortment_result.index[0:], inplace=True)
if assortment_result.empty:
return assortment_result
filters = {Const.TEMPLATE_NAME: self.main_shelves}
filtered_scif = self.scif[self.toolbox.get_filter_condition(
self.scif, **filters)]
products_in_session = filtered_scif.loc[
filtered_scif['facings'] > 0]['product_fk'].values
assortment_result.loc[
assortment_result['product_fk'].isin(products_in_session),
'in_store'] = 1
return assortment_result
@log_runtime('Share of shelf pepsicoRU')
def calculate_share_of_shelf(self):
"""
The function filters only the relevant scene (type = Main Shelf in category) and calculates the linear SOS and
the facing SOS for each level (Manufacturer, Category, Sub-Category, Brand).
The identifier for every kpi will be the current kpi_fk and the relevant attribute according to the level
E.g sub_category_fk for level 3 or brand_fk for level 4.
:return:
"""
# Get all of the KPI fk in advance
facings_stores_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
Const.FACINGS_MANUFACTURER_SOS)
facings_cat_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
Const.FACINGS_CATEGORY_SOS)
facings_sub_cat_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
Const.FACINGS_SUB_CATEGORY_SOS)
facings_brand_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
Const.FACINGS_BRAND_SOS)
linear_store_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
Const.LINEAR_MANUFACTURER_SOS)
linear_cat_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
Const.LINEAR_CATEGORY_SOS)
linear_sub_cat_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
Const.LINEAR_SUB_CATEGORY_SOS)
linear_brand_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
Const.LINEAR_BRAND_SOS)
filter_man_param = {Const.MANUFACTURER_NAME: Const.PEPSICO}
general_filters = {Const.TEMPLATE_NAME: self.main_shelves}
facings_level_1_identifier = self.common.get_dictionary(
kpi_fk=facings_stores_kpi_fk)
linear_level_1_identifier = self.common.get_dictionary(
kpi_fk=linear_store_kpi_fk)
num_facings = denom_facings = num_linear = denom_linear = result_facings = result_linear = 0
if self.main_shelves:
num_facings, denom_facings, num_linear, denom_linear = self.calculate_sos(
sos_filters=filter_man_param, **general_filters)
result_facings = num_facings / float(
denom_facings) if denom_facings else 0
result_linear = num_linear / float(
denom_linear) if denom_linear else 0
# Facings level 1
self.common.write_to_db_result(
fk=facings_stores_kpi_fk,
numerator_id=self.pepsico_fk,
identifier_result=facings_level_1_identifier,
numerator_result=num_facings,
denominator_id=self.store_id,
denominator_result=denom_facings,
result=result_facings,
should_enter=True)
# Linear level 1
self.common.write_to_db_result(
fk=linear_store_kpi_fk,
numerator_id=self.pepsico_fk,
identifier_result=linear_level_1_identifier,
numerator_result=num_linear * 100,
denominator_id=self.store_id,
denominator_result=denom_linear * 100,
result=result_linear,
should_enter=True)
for category in self.categories_to_calculate:
current_category_fk = self.get_relevant_pk_by_name(
Const.CATEGORY, category)
main_shelves_for_category = self.get_main_shelf_by_category(
category)
if main_shelves_for_category:
filter_params = {
Const.CATEGORY: category,
Const.TEMPLATE_NAME: main_shelves_for_category
}
facings_cat_identifier = self.common.get_dictionary(
kpi_fk=facings_cat_kpi_fk, category_fk=current_category_fk)
linear_cat_identifier = self.common.get_dictionary(
kpi_fk=linear_cat_kpi_fk, category_fk=current_category_fk)
num_facings, denom_facings, num_linear, denom_linear = self.calculate_sos(
sos_filters=filter_man_param, **filter_params)
result_facings = num_facings / float(
denom_facings) if denom_facings else 0
result_linear = num_linear / float(
denom_linear) if denom_linear else 0
# Facings level 2
self.common.write_to_db_result(
fk=facings_cat_kpi_fk,
numerator_id=self.pepsico_fk,
numerator_result=num_facings,
denominator_id=current_category_fk,
denominator_result=denom_facings,
identifier_result=facings_cat_identifier,
identifier_parent=facings_level_1_identifier,
result=result_facings,
should_enter=True)
# Linear level 2
self.common.write_to_db_result(
fk=linear_cat_kpi_fk,
numerator_id=self.pepsico_fk,
numerator_result=num_linear * 100,
denominator_id=current_category_fk,
denominator_result=denom_linear * 100,
identifier_result=linear_cat_identifier,
identifier_parent=linear_level_1_identifier,
result=result_linear,
should_enter=True)
for sub_cat in self.get_relevant_sub_categories_for_category(
category):
current_sub_category_fk = self.get_relevant_pk_by_name(
Const.SUB_CATEGORY, sub_cat)
filter_sub_cat_param = {
Const.SUB_CATEGORY: sub_cat,
Const.CATEGORY: category,
Const.TEMPLATE_NAME: main_shelves_for_category
}
facings_sub_cat_identifier = self.common.get_dictionary(
kpi_fk=facings_sub_cat_kpi_fk,
sub_category_fk=current_sub_category_fk)
linear_sub_cat_identifier = self.common.get_dictionary(
kpi_fk=linear_sub_cat_kpi_fk,
sub_category_fk=current_sub_category_fk)
num_facings, denom_facings, num_linear, denom_linear = self.calculate_sos(
sos_filters=filter_man_param, **filter_sub_cat_param)
if denom_facings and denom_linear:
# Facings level 3
self.common.write_to_db_result(
fk=facings_sub_cat_kpi_fk,
numerator_id=self.pepsico_fk,
numerator_result=num_facings,
denominator_id=current_sub_category_fk,
denominator_result=denom_facings,
identifier_result=facings_sub_cat_identifier,
identifier_parent=facings_cat_identifier,
result=num_facings / float(denom_facings),
should_enter=True)
# Linear level 3
self.common.write_to_db_result(
fk=linear_sub_cat_kpi_fk,
numerator_id=self.pepsico_fk,
numerator_result=num_linear * 100,
denominator_id=current_sub_category_fk,
denominator_result=denom_linear * 100,
identifier_result=linear_sub_cat_identifier,
identifier_parent=linear_cat_identifier,
result=num_linear / float(denom_linear),
should_enter=True)
for brand_name in self.get_relevant_brands_for_sub_category(
sub_cat):
current_brand_fk = self.get_relevant_pk_by_name(
Const.BRAND, brand_name)
filter_sos_brand = {
Const.BRAND_NAME: brand_name,
Const.SUB_CATEGORY: sub_cat,
Const.MANUFACTURER_NAME: Const.PEPSICO
}
filter_general_brand_param = {
Const.SUB_CATEGORY: sub_cat,
Const.CATEGORY: category,
Const.TEMPLATE_NAME: main_shelves_for_category
}
facings_brand_identifier = self.common.get_dictionary(
kpi_fk=facings_brand_kpi_fk,
brand_fk=current_brand_fk)
linear_brand_identifier = self.common.get_dictionary(
kpi_fk=linear_brand_kpi_fk,
brand_fk=current_brand_fk)
num_facings, denom_facings, num_linear, denom_linear = self.calculate_sos(
sos_filters=filter_sos_brand,
**filter_general_brand_param)
if denom_facings and denom_linear:
# Facings level 4
self.common.write_to_db_result(
fk=facings_brand_kpi_fk,
numerator_id=current_brand_fk,
numerator_result=num_facings,
denominator_id=current_sub_category_fk,
denominator_result=denom_facings,
identifier_result=facings_brand_identifier,
identifier_parent=
facings_sub_cat_identifier,
result=num_facings / float(denom_facings),
should_enter=True)
# Linear level 4
self.common.write_to_db_result(
fk=linear_brand_kpi_fk,
numerator_id=current_brand_fk,
numerator_result=num_linear * 100,
denominator_id=current_sub_category_fk,
denominator_result=denom_linear * 100,
identifier_result=linear_brand_identifier,
identifier_parent=linear_sub_cat_identifier,
result=num_linear / float(denom_linear),
should_enter=True)
# Utils functions with a slight change from the SDK factory:
def calculate_sos(self,
sos_filters,
include_empty=Const.EXCLUDE_EMPTY,
**general_filters):
"""
:param sos_filters: These are the parameters on which ths SOS is calculated (out of the general DF).
:param include_empty: This dictates whether Empty-typed SKUs are included in the calculation.
:param general_filters: These are the parameters which the general data frame is filtered by.
:return: The numerator facings, denominator facings, numerator linear and denominator linear.
"""
if include_empty == Const.EXCLUDE_EMPTY:
general_filters[Const.PRODUCT_TYPE] = (Const.EMPTY,
Const.EXCLUDE_FILTER)
numerator_facings, numerator_linear = self.calculate_share_space(
**dict(sos_filters, **general_filters))
denominator_facings, denominator_linear = self.calculate_share_space(
**general_filters)
return numerator_facings, denominator_facings, numerator_linear / 1000.0, denominator_linear / 1000.0
def calculate_share_space(self, **filters):
"""
:param filters: These are the parameters which the data frame is filtered by.
:return: The total number of facings and the shelf width (in mm) according to the filters.
"""
filtered_scif = self.scif[self.toolbox.get_filter_condition(
self.scif, **filters)]
sum_of_facings = filtered_scif['facings'].sum()
space_length = filtered_scif['net_len_ign_stack'].sum()
return sum_of_facings, space_length
def category_assortment_calculation(self, lvl3_result):
"""
This function calculates 3 levels of assortment :
level3 is assortment SKU
level2 is assortment groups
"""
osa_product_level_fk = self.common.get_kpi_fk_by_kpi_type(
Const.OSA_SKU_LEVEL)
oos_product_level_fk = self.common.get_kpi_fk_by_kpi_type(
Const.OOS_SKU_LEVEL)
osa_category_level_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
Const.OSA_CATEGORY_LEVEL)
oos_category_level_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
Const.OOS_CATEGORY_LEVEL)
if not lvl3_result.empty:
cat_df = self.all_products[['product_fk', 'category_fk']]
lvl3_with_cat = lvl3_result.merge(cat_df,
on='product_fk',
how='left')
lvl3_with_cat = lvl3_with_cat[
lvl3_with_cat['category_fk'].notnull()]
for result in lvl3_with_cat.itertuples():
if result.in_store == 1:
score = Const.DISTRIBUTION
else:
score = Const.OOS
# Distribution
self.common_v1.write_to_db_result_new_tables(
fk=osa_product_level_fk,
numerator_id=result.product_fk,
numerator_result=score,
result=score,
denominator_id=result.category_fk,
denominator_result=1,
score=score,
score_after_actions=score)
if score == Const.OOS:
# OOS
self.common_v1.write_to_db_result_new_tables(
oos_product_level_fk,
numerator_id=result.product_fk,
numerator_result=score,
result=score,
denominator_id=result.category_fk,
denominator_result=1,
score=score,
score_after_actions=score)
category_fk_list = lvl3_with_cat['category_fk'].unique()
for cat in category_fk_list:
lvl3_result_cat = lvl3_with_cat[lvl3_with_cat["category_fk"] ==
cat]
lvl2_result = self.assortment.calculate_lvl2_assortment(
lvl3_result_cat)
for result in lvl2_result.itertuples():
denominator_res = result.total
res = np.divide(float(result.passes),
float(denominator_res))
# Distribution
self.common_v1.write_to_db_result_new_tables(
fk=osa_category_level_kpi_fk,
numerator_id=self.pepsico_fk,
numerator_result=result.passes,
denominator_id=cat,
denominator_result=denominator_res,
result=res,
score=res,
score_after_actions=res)
# OOS
self.common_v1.write_to_db_result_new_tables(
fk=oos_category_level_kpi_fk,
numerator_id=self.pepsico_fk,
numerator_result=denominator_res - result.passes,
denominator_id=cat,
denominator_result=denominator_res,
result=1 - res,
score=(1 - res),
score_after_actions=1 - res)
self.assortment.LVL2_HEADERS.extend(['passes', 'total'])
return
def store_assortment_calculation(self, lvl3_result):
"""
This function calculates the KPI results.
"""
dist_store_level_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
Const.OSA_STORE_LEVEL)
oos_store_level_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
Const.OOS_STORE_LEVEL)
# for result in lvl3_result.itertuples():
# if result.in_store == 1:
# score = Const.DISTRIBUTION
# else:
# score = Const.OOS
# # Distribution
# self.common_v1.write_to_db_result_new_tables(fk=?????, numerator_id=result.product_fk,
# numerator_result=score,
# result=score, denominator_id=self.store_id,
# denominator_result=1, score=score)
# if score == Const.OOS:
# # OOS
# self.common_v1.write_to_db_result_new_tables(fk=?????, numerator_id=result.product_fk,
# numerator_result=score,
# result=score, denominator_id=self.store_id,
# denominator_result=1, score=score,
# score_after_actions=score)
if not lvl3_result.empty:
lvl2_result = self.assortment.calculate_lvl2_assortment(
lvl3_result)
for result in lvl2_result.itertuples():
denominator_res = result.total
if not pd.isnull(result.target) and not pd.isnull(
result.group_target_date
) and result.group_target_date <= self.visit_date:
denominator_res = result.target
res = np.divide(float(result.passes), float(denominator_res))
# Distribution
self.common_v1.write_to_db_result_new_tables(
fk=dist_store_level_kpi_fk,
numerator_id=self.pepsico_fk,
denominator_id=self.store_id,
numerator_result=result.passes,
denominator_result=denominator_res,
result=res,
score=res,
score_after_actions=res)
# OOS
self.common_v1.write_to_db_result_new_tables(
fk=oos_store_level_kpi_fk,
numerator_id=self.pepsico_fk,
numerator_result=denominator_res - result.passes,
denominator_id=self.store_id,
denominator_result=denominator_res,
result=1 - res,
score=1 - res,
score_after_actions=1 - res)
return
class PERFETTICNToolBox:
LEVEL1 = 1
LEVEL2 = 2
LEVEL3 = 3
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.common = Common(self.data_provider)
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.rds_conn = PSProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.kpi_static_data = self.common.get_new_kpi_static_data()
self.kpi_results_queries = []
self.store_info = self.data_provider[Data.STORE_INFO]
self.assortment = Assortment(self.data_provider, self.output)
self.template = self.data_provider.all_templates
def main_calculation(self, *args, **kwargs):
self.display_count()
self.assortment_calculation()
self.common.commit_results_data_to_new_tables()
def display_count(self):
"""This function calculates how many displays find from types secondary shelf
"""
num_brands = {}
display_info = self.scif['template_fk']
display_fks = display_info.unique()
template_seco = self.template[
self.template['included_in_secondary_shelf_report'] ==
'Y']['template_fk']
display_fks = list(
filter(lambda x: x in template_seco.values, display_fks))
count_fk = self.kpi_static_data[self.kpi_static_data['client_name'] ==
'COUNT OF DISPLAY']['pk'].iloc[0]
for value in display_fks:
num_brands[value] = display_info[display_info == value].count()
score = num_brands[value]
self.common.write_to_db_result_new_tables(count_fk, value, None,
score, score, score,
score)
return
def assortment_calculation(self):
"""
This function calculates 3 levels of assortment :
level3 is assortment SKU
level2 is assortment groups
level1 how many groups passed out of all
"""
lvl3_result = self.assortment.calculate_lvl3_assortment()
for result in lvl3_result.itertuples():
score = result.in_store
if score >= 1:
score = 100
self.common.write_to_db_result_new_tables(
result.kpi_fk_lvl3, result.product_fk, result.in_store, score,
result.assortment_group_fk, 1, score)
if not lvl3_result.empty:
lvl2_result = self.assortment.calculate_lvl2_assortment(
lvl3_result)
for result in lvl2_result.itertuples():
denominator_res = result.total
res = np.divide(float(result.passes), float(denominator_res))
if result.passes >= 1:
score = 100
else:
score = 0
self.common.write_to_db_result_new_tables(
result.kpi_fk_lvl2, result.assortment_group_fk,
result.passes, (res * 100),
result.assortment_super_group_fk, denominator_res, score)
if not lvl2_result.empty:
lvl1_result = self.assortment.calculate_lvl1_assortment(
lvl2_result)
for result in lvl1_result.itertuples():
denominator_res = result.total
res = np.divide(float(result.passes),
float(denominator_res))
if res >= 0:
score = 100
else:
score = 0
self.common.write_to_db_result_new_tables(
fk=result.kpi_fk_lvl1,
numerator_id=result.assortment_super_group_fk,
numerator_result=result.passes,
denominator_result=denominator_res,
result=(res * 100),
score=score)
return
class MOLSONCOORSHR_SANDToolBox:
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.common = Common(self.data_provider)
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.current_date = datetime.now()
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_info = self.data_provider[Data.STORE_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.own_manufacturer_id = int(self.data_provider[Data.OWN_MANUFACTURER][self.data_provider[Data.OWN_MANUFACTURER]['param_name'] == 'manufacturer_id']['param_value'].tolist()[0])
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.rds_conn = PSProjectConnector(self.project_name, DbUsers.CalculationEng)
self.toolbox = GENERALToolBox(data_provider)
self.assortment = Assortment(self.data_provider, self.output, common=self.common)
self.kpi_static_data = self.common.get_kpi_static_data()
self.kpi_results_queries = []
self.template_path = self.get_template_path()
self.template_data = self.get_template_data()
self.sos_store_policies = self.get_sos_store_policies(self.visit_date.strftime('%Y-%m-%d'))
self.result_values = self.get_result_values()
self.scores = pd.DataFrame()
def get_sos_store_policies(self, visit_date):
query = MOLSONCOORSHR_SANDQueries.get_sos_store_policies(visit_date)
store_policies = pd.read_sql_query(query, self.rds_conn.db)
return store_policies
def get_result_values(self):
query = MOLSONCOORSHR_SANDQueries.get_result_values()
result_values = pd.read_sql_query(query, self.rds_conn.db)
return result_values
def main_calculation(self):
"""
This function starts the KPI results calculation.
"""
if not self.template_data or KPIS_TEMPLATE_SHEET not in self.template_data:
Log.error('KPIs template sheet is empty or not found')
return
self.kpis_calculation()
self.common.commit_results_data()
def kpis_calculation(self, kpi_group=''):
"""
This is a recursive function.
The function calculates each level KPI cascading from the highest level to the lowest.
"""
total_score = total_potential_score = total_calculated = 0
kpis = self.template_data['KPIs'][self.template_data['KPIs']['KPI Group'] == kpi_group]
for index, kpi in kpis.iterrows():
child_kpi_group = kpi['Child KPI Group']
kpi_type = kpi['KPI Type'].lower()
score_function = kpi['Score Function'].lower()
if not child_kpi_group:
if kpi_type in [LINEAR_SOS_VS_TARGET, FACINGS_SOS_VS_TARGET]:
score, potential_score, calculated = self.calculate_sos_vs_target(kpi)
elif kpi_type in [FACINGS_VS_TARGET, DISTRIBUTION]:
score, potential_score, calculated = self.calculate_assortment_vs_target(kpi)
else:
Log.error("KPI of type '{}' is not supported".format(kpi_type))
score = potential_score = calculated = 0
else:
score, potential_score, calculated = self.kpis_calculation(child_kpi_group)
if score_function in [WEIGHTED_SCORE, SUM_OF_SCORES]:
total_score += score
total_potential_score += potential_score
total_calculated += calculated
else:
total_score += 0
total_potential_score += 0
total_calculated += 0
if child_kpi_group and calculated:
if kpi['KPI name Eng'] == 'Store Score':
kpi_fk = self.common.get_kpi_fk_by_kpi_type(kpi['KPI name Eng'])
parent_fk = self.common.get_kpi_fk_by_kpi_type(kpi['KPI Group']) if kpi['KPI Group'] else 0
numerator_id = self.own_manufacturer_id
denominator_id = self.store_id
identifier_result = self.common.get_dictionary(kpi_fk=kpi_fk)
identifier_parent = self.common.get_dictionary(kpi_fk=parent_fk)
self.common.write_to_db_result(fk=kpi_fk,
numerator_id=numerator_id,
numerator_result=0,
denominator_id=denominator_id,
denominator_result=0,
result=score,
score=score,
weight=potential_score,
target=potential_score,
identifier_result=identifier_result,
identifier_parent=identifier_parent,
should_enter=True
)
else:
kpi_fk = self.common.get_kpi_fk_by_kpi_type(kpi['KPI name Eng'])
parent_fk = self.common.get_kpi_fk_by_kpi_type(kpi['KPI Group']) if kpi['KPI Group'] else 0
numerator_id = self.own_manufacturer_id
denominator_id = self.store_id
identifier_result = self.common.get_dictionary(kpi_fk=kpi_fk)
identifier_parent = self.common.get_dictionary(kpi_fk=parent_fk)
self.common.write_to_db_result(fk=kpi_fk,
numerator_id=numerator_id,
numerator_result=0,
denominator_id=denominator_id,
denominator_result=0,
result=score,
score=score,
weight=potential_score,
target=potential_score,
identifier_result=identifier_result,
identifier_parent=identifier_parent,
should_enter=True
)
return total_score, total_potential_score, total_calculated
@kpi_runtime()
def calculate_assortment_vs_target(self, kpi):
"""
The function filters only the relevant scenes by Location Type and calculates the Assortment scores
according to rules set in the target.
:return:
"""
lvl3_result = self.calculate_assortment_vs_target_lvl3(kpi)
for row in lvl3_result.itertuples():
numerator_id = row.product_fk
numerator_result = row.distributed if kpi['KPI Type'] == 'Distribution' else row.facings
denominator_id = self.store_id
denominator_result = row.target
# denominator_result_after_actions = 0 if row.target < row.facings else row.target - row.facings
if kpi['KPI Type'] == 'Distribution':
if row.result_distributed:
result = self.result_values[(self.result_values['result_type'] == 'PRESENCE') &
(self.result_values['result_value'] == 'DISTRIBUTED')]['result_value_fk'].tolist()[0]
score = 100
else:
result = self.result_values[(self.result_values['result_type'] == 'PRESENCE') &
(self.result_values['result_value'] == 'OOS')]['result_value_fk'].tolist()[0]
score = 0
else:
result = row.result_facings
score = round(result*100, 0)
identifier_details = self.common.get_dictionary(kpi_fk=row.kpi_fk_lvl3)
identifier_kpi = self.common.get_dictionary(kpi_fk=row.kpi_fk_lvl2)
self.common.write_to_db_result(fk=row.kpi_fk_lvl3,
numerator_id=numerator_id,
numerator_result=numerator_result,
denominator_id=denominator_id,
denominator_result=denominator_result,
# denominator_result_after_actions=denominator_result_after_actions,
result=result,
score=score,
identifier_result=identifier_details,
identifier_parent=identifier_kpi,
should_enter=True
)
score = potential_score = 0
if not lvl3_result.empty:
lvl2_result = self.calculate_assortment_vs_target_lvl2(lvl3_result)
for row in lvl2_result.itertuples():
numerator_id = self.own_manufacturer_id
numerator_result = row.distributed if kpi['KPI Type'] == 'Distribution' else row.facings
denominator_id = self.store_id
denominator_result = row.target
result = row.result_distributed if kpi['KPI Type'] == 'Distribution' else row.result_facings
score += self.score_function(result*100, kpi)
potential_score += round(float(kpi['Weight'])*100, 0)
identifier_kpi = self.common.get_dictionary(kpi_fk=row.kpi_fk_lvl2)
identifier_parent = self.common.get_dictionary(kpi_fk=self.common.get_kpi_fk_by_kpi_type(kpi['KPI Group']))
self.common.write_to_db_result(fk=row.kpi_fk_lvl2,
numerator_id=numerator_id,
numerator_result=numerator_result,
denominator_id=denominator_id,
denominator_result=denominator_result,
result=score,
score=score,
weight=potential_score,
target=potential_score,
identifier_result=identifier_kpi,
identifier_parent=identifier_parent,
should_enter=True
)
if len(lvl3_result) > 0:
calculated = 1
else:
calculated = 0
return score, potential_score, calculated
def calculate_assortment_vs_target_lvl3(self, kpi):
location_types = kpi['Location Type'].split(', ')
kpi_fk_lvl3 = self.common.get_kpi_fk_by_kpi_type(kpi['KPI name Eng'] + ' - SKU')
kpi_fk_lvl2 = self.common.get_kpi_fk_by_kpi_type(kpi['KPI name Eng'])
assortment_result = self.assortment.get_lvl3_relevant_ass()
if assortment_result.empty:
return assortment_result
assortment_result = assortment_result[(assortment_result['kpi_fk_lvl3'] == kpi_fk_lvl3) &
(assortment_result['kpi_fk_lvl2'] == kpi_fk_lvl2)]
if assortment_result.empty:
return assortment_result
assortment_result['target'] = assortment_result.apply(lambda x: json.loads(x['additional_attributes']).get('Target'), axis=1)
assortment_result['target'] = assortment_result['target'].fillna(0)
assortment_result = assortment_result[assortment_result['target'] > 0]
assortment_result['weight'] = assortment_result.apply(lambda x: json.loads(x['additional_attributes']).get('Weight'), axis=1)
assortment_result['weight'] = assortment_result['weight'].fillna(0)
assortment_total_weights = assortment_result[['assortment_fk', 'weight']].groupby('assortment_fk').agg({'weight': 'sum'}).reset_index()
assortment_result = assortment_result.merge(assortment_total_weights, how='left', left_on='assortment_fk', right_on='assortment_fk', suffixes=['', '_total'])
facings = 'facings_ign_stack' if kpi['Ignore Stacking'] else 'facings'
products_in_session = self.scif[(self.scif[facings] > 0) & (self.scif['location_type'].isin(location_types))][['product_fk', 'facings']]\
.groupby('product_fk').agg({'facings': 'sum'}).reset_index()
lvl3_result = assortment_result.merge(products_in_session, how='left', left_on='product_fk', right_on='product_fk')
lvl3_result['facings'] = lvl3_result['facings'].fillna(0)
lvl3_result['distributed'] = lvl3_result.apply(lambda x: 1 if x['facings'] else 0, axis=1)
lvl3_result['result_facings'] = lvl3_result.apply(lambda x: self.assortment_vs_target_result(x, 'facings'), axis=1)
lvl3_result['result_distributed'] = lvl3_result.apply(lambda x: self.assortment_vs_target_result(x, 'distributed'), axis=1)
return lvl3_result
def calculate_assortment_vs_target_lvl2(self, lvl3_result):
lvl2_result = lvl3_result.groupby(['kpi_fk_lvl2', self.assortment.ASSORTMENT_FK, self.assortment.ASSORTMENT_GROUP_FK])\
.agg({'facings': 'sum', 'distributed': 'sum', 'target': 'sum', 'result_facings': 'sum', 'result_distributed': 'sum'}).reset_index()
return lvl2_result
@staticmethod
def assortment_vs_target_result(x, y):
if x[y] < x['target']:
return round(x[y] / float(x['target']) * float(x['weight']) / x['weight_total'], 5)
else:
return round(1 * float(x['weight']) / x['weight_total'], 5)
@kpi_runtime()
def calculate_sos_vs_target(self, kpi):
"""
The function filters only the relevant scenes by Location Type and calculates the linear SOS and
the facing SOS according to Manufacturer and Category set in the target.
:return:
"""
location_type = kpi['Location Type']
kpi_fk = self.common.get_kpi_fk_by_kpi_type(SOS_MANUFACTURER_CATEGORY + ('_' + location_type if location_type else ''))
sos_store_policies = self.sos_store_policies[self.sos_store_policies['kpi_fk'] == str(kpi_fk)]
sos_store_policy = None
store_policy_passed = 0
for index, policy in sos_store_policies.iterrows():
sos_store_policy = policy
store_policy = json.loads(policy['store_policy'])
store_policy_passed = 1
for key in store_policy.keys():
if key in self.store_info.columns.tolist():
if self.store_info[key][0] in store_policy[key]:
continue
else:
store_policy_passed = 0
break
else:
Log.error("Store Policy attribute is not found: '{}'").format(key)
store_policy_passed = 0
break
if store_policy_passed:
break
score = potential_score = 0
if store_policy_passed:
general_filters = {LOCATION_TYPE: location_type}
sos_policy = json.loads(sos_store_policy['sos_policy'])
numerator_sos_filters = {MANUFACTURER_NAME: sos_policy[NUMERATOR][MANUFACTURER], CATEGORY: sos_policy[DENOMINATOR][CATEGORY]}
denominator_sos_filters = {CATEGORY: sos_policy[DENOMINATOR][CATEGORY]}
numerator_id = self.all_products.loc[self.all_products[MANUFACTURER_NAME] == sos_policy[NUMERATOR][MANUFACTURER]][MANUFACTURER + '_fk'].values[0]
denominator_id = self.all_products.loc[self.all_products[CATEGORY] == sos_policy[DENOMINATOR][CATEGORY]][CATEGORY + '_fk'].values[0]
ignore_stacking = kpi['Ignore Stacking'] if kpi['Ignore Stacking'] else 0
numer_facings, numer_linear = self.calculate_share_space(ignore_stacking=ignore_stacking, **dict(numerator_sos_filters, **general_filters))
denom_facings, denom_linear = self.calculate_share_space(ignore_stacking=ignore_stacking, **dict(denominator_sos_filters, **general_filters))
if kpi['KPI Type'].lower() == LINEAR_SOS_VS_TARGET:
numerator_result = round(numer_linear, 0)
denominator_result = round(denom_linear, 0)
result = numer_linear / float(denom_linear) if denom_linear else 0
elif kpi['KPI Type'].lower() == FACINGS_SOS_VS_TARGET:
numerator_result = numer_facings
denominator_result = denom_facings
result = numer_facings / float(denom_facings) if denom_facings else 0
else:
Log.error("KPI Type is invalid: '{}'").format(kpi['KPI Type'])
numerator_result = denominator_result = result = 0
if sos_store_policy['target']:
sos_target = round(float(sos_store_policy['target'])*100, 0)
else:
Log.error("SOS target is not set for Store ID {}").format(self.store_id)
sos_target = 0
result_vs_target = result/(float(sos_target)/100)*100 if sos_target else 0
score = self.score_function(result_vs_target, kpi)
potential_score = round(float(kpi['Weight'])*100, 0)
identifier_kpi = self.common.get_dictionary(kpi_fk=kpi_fk)
identifier_parent = self.common.get_dictionary(kpi_fk=self.common.get_kpi_fk_by_kpi_type(kpi['KPI Group']))
self.common.write_to_db_result(fk=kpi_fk,
numerator_id=numerator_id,
numerator_result=numerator_result,
denominator_id=denominator_id,
denominator_result=denominator_result,
result=result,
score=score,
weight=potential_score,
target=sos_target,
identifier_result=identifier_kpi,
identifier_parent=identifier_parent,
should_enter=True
)
else:
Log.warning("Store Policy is not found for Store ID {}".format(self.store_id))
return score, potential_score, store_policy_passed
def calculate_share_space(self, ignore_stacking=1, **filters):
"""
:param filters: These are the parameters which the data frame is filtered by.
:param ignore_stacking: 1 is to ignore stacking.
:return: The total number of facings and the shelf width (in mm) according to the filters.
"""
filtered_scif = self.scif[self.toolbox.get_filter_condition(self.scif, **filters)]
if ignore_stacking:
sum_of_facings = filtered_scif['facings_ign_stack'].sum()
space_length = filtered_scif['net_len_ign_stack'].sum()
else:
sum_of_facings = filtered_scif['facings'].sum()
space_length = filtered_scif['net_len_ign_stack'].sum()
return sum_of_facings, space_length
@staticmethod
def get_template_path():
return os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', 'Data', KPIS_TEMPLATE_NAME)
def get_template_data(self):
template_data = {}
try:
sheet_names = pd.ExcelFile(self.template_path).sheet_names
for sheet in sheet_names:
template_data[sheet] = parse_template(self.template_path, sheet, lower_headers_row_index=0)
except IOError as e:
Log.error('Template {} does not exist. {}'.format(KPIS_TEMPLATE_NAME, repr(e)))
return template_data
@staticmethod
def score_function(score, kpi):
weight = float(kpi['Weight']) if kpi['Weight'] else 1
score_function = kpi['Score Function'].lower()
l_threshold = float(kpi['Lower Threshold'])*100 if kpi['Lower Threshold'] else 0
h_threshold = float(kpi['Higher Threshold'])*100 if kpi['Higher Threshold'] else 100
if score < l_threshold:
score = 0
elif score >= h_threshold:
score = 100
if score_function in [WEIGHTED_SCORE]:
score = round(score*weight, 0)
else:
score = round(score, 0)
return score
class PepsicoUtil(UnifiedKPISingleton):
LEVEL1 = 1
LEVEL2 = 2
LEVEL3 = 3
EXCLUSION_TEMPLATE_PATH = os.path.join(
os.path.dirname(os.path.realpath(__file__)), '..', 'Data',
'Inclusion_Exclusion_Template_Rollout.xlsx')
ADDITIONAL_DISPLAY = 'additional display'
INCLUDE_EMPTY = True
EXCLUDE_EMPTY = False
OPERATION_TYPES = []
SOS_VS_TARGET = 'SOS vs Target'
HERO_SKU_SPACE_TO_SALES_INDEX = 'Hero SKU Space to Sales Index'
HERO_SKU_SOS_VS_TARGET = 'Hero SKU SOS vs Target'
LINEAR_SOS_INDEX = 'Linear SOS Index'
PEPSICO = 'PEPSICO'
SHELF_PLACEMENT = 'Shelf Placement'
HERO_SKU_PLACEMENT_TOP = 'Hero SKU Placement by shelf numbers_Top'
HERO_PLACEMENT = 'Hero Placement'
HERO_SKU_STACKING = 'Hero SKU Stacking'
HERO_SKU_PRICE = 'Hero SKU Price'
HERO_SKU_PROMO_PRICE = 'Hero SKU Promo Price'
BRAND_FULL_BAY_KPIS = ['Brand Full Bay_90', 'Brand Full Bay']
BRAND_FULL_BAY = 'Brand Full Bay'
HERO_PREFIX = 'Hero SKU'
ALL = 'ALL'
HERO_SKU_OOS_SKU = 'Hero SKU OOS - SKU'
HERO_SKU_OOS = 'Hero SKU OOS'
HERO_SKU_AVAILABILITY = 'Hero SKU Availability'
BRAND_SPACE_TO_SALES_INDEX = 'Brand Space to Sales Index'
BRAND_SPACE_SOS_VS_TARGET = 'Brand Space SOS vs Target'
SUB_BRAND_SPACE_TO_SALES_INDEX = 'Sub Brand Space to Sales Index'
SUB_BRAND_SPACE_SOS_VS_TARGET = 'Sub Brand Space SOS vs Target'
PEPSICO_SEGMENT_SPACE_TO_SALES_INDEX = 'PepsiCo Segment Space to Sales Index'
PEPSICO_SEGMENT_SOS_VS_TARGET = 'PepsiCo Segment SOS vs Target'
PEPSICO_SUB_SEGMENT_SPACE_TO_SALES_INDEX = 'PepsiCo Sub Segment Space to Sales Index'
PEPSICO_SUB_SEGMENT_SOS_VS_TARGET = 'PepsiCo Sub Segment SOS vs Target'
PLACEMENT_BY_SHELF_NUMBERS_TOP = 'Placement by shelf numbers_Top'
TOTAL_LINEAR_SPACE = 'Total Linear Space'
NUMBER_OF_FACINGS = 'Number of Facings'
NUMBER_OF_BAYS = 'Number of bays'
NUMBER_OF_SHELVES = 'Number of shelves'
PRODUCT_BLOCKING = 'Product Blocking'
PRODUCT_BLOCKING_ADJACENCY = 'Product Blocking Adjacency'
SHELF_PLACEMENT_VERTICAL_LEFT = 'Shelf Placement Vertical_Left'
SHELF_PLACEMENT_VERTICAL_CENTER = 'Shelf Placement Vertical_Center'
SHELF_PLACEMENT_VERTICAL_RIGHT = 'Shelf Placement Vertical_Right'
NUMBER_OF_SHELVES_TEMPL_COLUMN = 'No of Shelves in Fixture (per bay) (key)'
RELEVANT_SHELVES_TEMPL_COLUMN = 'Shelves From Bottom To Include (data)'
SHELF_PLC_TARGETS_COLUMNS = [
'kpi_operation_type_fk', 'operation_type', 'kpi_level_2_fk', 'type',
NUMBER_OF_SHELVES_TEMPL_COLUMN, RELEVANT_SHELVES_TEMPL_COLUMN,
'KPI Parent'
]
SHELF_PLC_TARGET_COL_RENAME = {
'kpi_operation_type_fk_x': 'kpi_operation_type_fk',
'operation_type_x': 'operation_type',
'kpi_level_2_fk_x': 'kpi_level_2_fk',
'type_x': 'type',
NUMBER_OF_SHELVES_TEMPL_COLUMN + '_x': NUMBER_OF_SHELVES_TEMPL_COLUMN,
RELEVANT_SHELVES_TEMPL_COLUMN + '_x': RELEVANT_SHELVES_TEMPL_COLUMN,
'KPI Parent_x': 'KPI Parent'
}
HERO_SKU_AVAILABILITY_SKU = 'Hero SKU Availability - SKU'
HERO_SKU_PLACEMENT_BY_SHELF_NUMBERS = 'Hero SKU Placement by shelf numbers'
HERO_SKU_AVAILABILITY_BY_HERO_TYPE = 'Hero SKU Availability by Hero Type'
SHARE_OF_ASSORTMENT_BY_HERO_TYPE = 'Share of Assortment by Hero Type'
HERO_SKU_LABEL = 'Hero SKU'
HERO_TYPE = 'hero_type'
HERO_SKU_SOS_OF_CAT_BY_HERO_TYPE = 'Hero SKU SOS of Category by Hero Type'
CATEGORY_FULL_BAY = 'Category Full Bay'
CSN = 'CSN'
PRICE = 'Price'
PROMO_PRICE = 'Promo Price'
LINEAR_SPACE_PER_PRODUCT = 'Linear Space Per Product'
FACINGS_PER_PRODUCT = 'Facings per Product'
PRICE_SCENE = 'Price Scene'
PROMO_PRICE_SCENE = 'Promo Price Scene'
HERO_SKU_SOS = 'Hero SKU SOS'
BRAND_SOS = 'Brand SOS'
SUB_BRAND_SOS = 'Sub Brand SOS'
PEPSICO_SEGMENT_SOS = 'PepsiCo Segment SOS'
BRAND_SOS_OF_SEGMENT = 'Brand SOS of Segment'
BINS_NOT_RECOGNIZED = 'Bins_not_recognized'
def __init__(self, output, data_provider):
super(PepsicoUtil, self).__init__(data_provider)
self.output = output
self.common = Common(self.data_provider)
# self.common_v1 = CommonV1(self.data_provider)
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK] if self.data_provider[Data.STORE_FK] is not None \
else self.session_info['store_fk'].values[0]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.rds_conn = PSProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.display_scene = self.get_match_display_in_scene()
self.kpi_static_data = self.common.get_kpi_static_data()
self.kpi_results_queries = []
self.probe_groups = self.get_probe_group()
self.match_product_in_scene = self.match_product_in_scene.merge(
self.probe_groups, on='probe_match_fk', how='left')
self.toolbox = GENERALToolBox(self.data_provider)
self.commontools = PEPSICOUKCommonToolBox(self.data_provider,
self.rds_conn)
self.all_templates = self.commontools.all_templates
self.custom_entities = self.commontools.custom_entities
self.on_display_products = self.commontools.on_display_products
self.exclusion_template = self.commontools.exclusion_template
self.filtered_scif = self.commontools.filtered_scif.copy()
self.filtered_matches = self.commontools.filtered_matches.copy()
self.filtered_matches = self.filtered_matches.merge(
self.probe_groups, on='probe_match_fk', how='left')
self.filtered_scif_secondary = self.commontools.filtered_scif_secondary.copy(
)
self.filtered_matches_secondary = self.commontools.filtered_matches_secondary.copy(
)
self.scene_bay_shelf_product = self.commontools.scene_bay_shelf_product
self.ps_data = PsDataProvider(self.data_provider, self.output)
self.full_store_info = self.commontools.full_store_info.copy()
self.external_targets = self.commontools.external_targets
self.assortment = Assortment(self.commontools.data_provider,
self.output)
self.lvl3_ass_result = self.get_lvl3_relevant_assortment_result()
self.own_manuf_fk = self.all_products[
self.all_products['manufacturer_name'] ==
self.PEPSICO]['manufacturer_fk'].values[0]
self.scene_kpi_results = self.get_results_of_scene_level_kpis()
self.kpi_results_check = pd.DataFrame(columns=[
'kpi_fk', 'numerator', 'denominator', 'result', 'score', 'context'
])
self.sos_vs_target_targets = self.construct_sos_vs_target_base_df()
self.all_targets_unpacked = self.commontools.all_targets_unpacked.copy(
)
self.block_results = pd.DataFrame(columns=['Group Name', 'Score'])
self.hero_type_custom_entity_df = self.get_hero_type_custom_entity_df()
def get_match_display_in_scene(self):
query = PEPSICOUK_Queries.get_match_display(self.session_uid)
match_display = pd.read_sql_query(query, self.rds_conn.db)
return match_display
def get_probe_group(self):
query = PEPSICOUK_Queries.get_probe_group(self.session_uid)
probe_group = pd.read_sql_query(query, self.rds_conn.db)
return probe_group
@staticmethod
def get_full_bay_and_positional_filters(parameters):
filters = {parameters['Parameter 1']: parameters['Value 1']}
if parameters['Parameter 2']:
filters.update({parameters['Parameter 2']: parameters['Value 2']})
if parameters['Parameter 3']:
filters.update({parameters['Parameter 3']: parameters['Value 3']})
return filters
# @staticmethod
# def get_stack_data(row):
# is_stack = False
# sequences_list = row['all_sequences'][0:-1].split(',')
# count_sequences = collections.Counter(sequences_list)
# repeating_items = [c > 1 for c in count_sequences.values()]
# if repeating_items:
# if any(repeating_items):
# is_stack = True
# return is_stack
@staticmethod
def split_and_strip(value):
return map(lambda x: x.strip(), str(value).split(','))
def construct_sos_vs_target_base_df(self):
sos_targets = self.get_relevant_sos_vs_target_kpi_targets()
sos_targets = sos_targets.drop_duplicates(subset=[
'kpi_operation_type_fk', 'kpi_level_2_fk', 'numerator_value',
'denominator_value', 'type'
],
keep='first')
sos_targets = sos_targets.drop(
['key_json', 'data_json', 'start_date', 'end_date'], axis=1)
if not sos_targets.empty:
sos_targets['numerator_id'] = sos_targets.apply(
self.retrieve_relevant_item_pks,
axis=1,
args=('numerator_type', 'numerator_value'))
sos_targets['denominator_id'] = sos_targets.apply(
self.retrieve_relevant_item_pks,
axis=1,
args=('denominator_type', 'denominator_value'))
sos_targets['identifier_parent'] = sos_targets['KPI Parent'].apply(
lambda x: self.common.get_dictionary(kpi_fk=int(float(x))))
return sos_targets
def get_relevant_sos_vs_target_kpi_targets(self, brand_vs_brand=False):
sos_vs_target_kpis = self.external_targets[
self.external_targets['operation_type'] == self.SOS_VS_TARGET]
sos_vs_target_kpis = sos_vs_target_kpis.drop_duplicates(subset=[
'operation_type', 'kpi_level_2_fk', 'key_json', 'data_json'
])
relevant_targets_df = pd.DataFrame(
columns=sos_vs_target_kpis.columns.values.tolist())
if not sos_vs_target_kpis.empty:
policies_df = self.commontools.unpack_external_targets_json_fields_to_df(
sos_vs_target_kpis, field_name='key_json')
policy_columns = policies_df.columns.values.tolist()
del policy_columns[policy_columns.index('pk')]
store_dict = self.full_store_info.to_dict('records')[0]
for column in policy_columns:
store_att_value = store_dict.get(column)
policies_df = policies_df[policies_df[column].isin(
[store_att_value, self.ALL])]
kpi_targets_pks = policies_df['pk'].values.tolist()
relevant_targets_df = sos_vs_target_kpis[
sos_vs_target_kpis['pk'].isin(kpi_targets_pks)]
# relevant_targets_df = relevant_targets_df.merge(policies_df, on='pk', how='left')
data_json_df = self.commontools.unpack_external_targets_json_fields_to_df(
relevant_targets_df, 'data_json')
relevant_targets_df = relevant_targets_df.merge(data_json_df,
on='pk',
how='left')
kpi_data = self.kpi_static_data[['pk', 'type']].drop_duplicates()
kpi_data.rename(columns={'pk': 'kpi_level_2_fk'}, inplace=True)
relevant_targets_df = relevant_targets_df.merge(
kpi_data,
left_on='kpi_level_2_fk',
right_on='kpi_level_2_fk',
how='left')
linear_sos_fk = self.common.get_kpi_fk_by_kpi_type(
self.LINEAR_SOS_INDEX)
if brand_vs_brand:
relevant_targets_df = relevant_targets_df[
relevant_targets_df['KPI Parent'] == linear_sos_fk]
else:
relevant_targets_df = relevant_targets_df[~(
relevant_targets_df['KPI Parent'] == linear_sos_fk)]
return relevant_targets_df
def retrieve_relevant_item_pks(self, row, type_field_name,
value_field_name):
try:
if row[type_field_name].endswith("_fk"):
item_id = row[value_field_name]
else:
# print row[type_field_name], ' :', row[value_field_name]
item_id = self.custom_entities[
self.custom_entities['name'] ==
row[value_field_name]]['pk'].values[0]
except KeyError as e:
Log.error('No id found for field {}. Error: {}'.format(
row[type_field_name], e))
item_id = None
return item_id
def calculate_sos(self, sos_filters, **general_filters):
numerator_linear = self.calculate_share_space(
**dict(sos_filters, **general_filters))
denominator_linear = self.calculate_share_space(**general_filters)
return float(numerator_linear), float(denominator_linear)
def calculate_share_space(self, **filters):
filtered_scif = self.filtered_scif[self.toolbox.get_filter_condition(
self.filtered_scif, **filters)]
space_length = filtered_scif['updated_gross_length'].sum()
return space_length
def add_kpi_result_to_kpi_results_df(self, result_list):
self.kpi_results_check.loc[len(self.kpi_results_check)] = result_list
def get_results_of_scene_level_kpis(self):
scene_kpi_results = pd.DataFrame()
if not self.scene_info.empty:
scene_kpi_results = self.ps_data.get_scene_results(
self.scene_info['scene_fk'].drop_duplicates().values)
return scene_kpi_results
def get_store_data_by_store_id(self):
store_id = self.store_id if self.store_id else self.session_info[
'store_fk'].values[0]
query = PEPSICOUK_Queries.get_store_data_by_store_id(store_id)
query_result = pd.read_sql_query(query, self.rds_conn.db)
return query_result
def get_facings_scene_bay_shelf_product(self):
self.filtered_matches['count'] = 1
aggregate_df = self.filtered_matches.groupby(
['scene_fk', 'bay_number', 'shelf_number', 'product_fk'],
as_index=False).agg({'count': np.sum})
return aggregate_df
def get_lvl3_relevant_assortment_result(self):
assortment_result = self.assortment.get_lvl3_relevant_ass()
# if assortment_result.empty:
# return assortment_result
# products_in_session = self.filtered_scif.loc[self.filtered_scif['facings'] > 0]['product_fk'].values
# assortment_result.loc[assortment_result['product_fk'].isin(products_in_session), 'in_store'] = 1
return assortment_result
@staticmethod
def get_block_and_adjacency_filters(target_series):
filters = {target_series['Parameter 1']: target_series['Value 1']}
if target_series['Parameter 2']:
filters.update(
{target_series['Parameter 2']: target_series['Value 2']})
if target_series['Parameter 3']:
filters.update(
{target_series['Parameter 3']: target_series['Value 3']})
return filters
@staticmethod
def get_block_filters(target_series):
if isinstance(target_series['Value 1'], list):
filters = {target_series['Parameter 1']: target_series['Value 1']}
else:
filters = {
target_series['Parameter 1']: [target_series['Value 1']]
}
if target_series['Parameter 2']:
if isinstance(target_series['Value 2'], list):
filters.update(
{target_series['Parameter 2']: target_series['Value 2']})
else:
filters.update(
{target_series['Parameter 2']: [target_series['Value 2']]})
if target_series['Parameter 3']:
if isinstance(target_series['Value 2'], list):
filters.update(
{target_series['Parameter 3']: target_series['Value 3']})
else:
filters.update(
{target_series['Parameter 3']: [target_series['Value 3']]})
return filters
def reset_filtered_scif_and_matches_to_exclusion_all_state(self):
self.filtered_scif = self.commontools.filtered_scif.copy()
self.filtered_matches = self.commontools.filtered_matches.copy()
def reset_secondary_filtered_scif_and_matches_to_exclusion_all_state(self):
self.filtered_scif_secondary = self.commontools.filtered_scif_secondary.copy(
)
self.filtered_matches_secondary = self.commontools.filtered_matches_secondary.copy(
)
def get_available_hero_sku_list(self, dependencies_df):
hero_list = dependencies_df[
(dependencies_df['kpi_type'] == self.HERO_SKU_AVAILABILITY_SKU)
& (dependencies_df['numerator_result'] == 1
)]['numerator_id'].unique().tolist()
return hero_list
def get_unavailable_hero_sku_list(self, dependencies_df):
hero_list = dependencies_df[
(dependencies_df['kpi_type'] == self.HERO_SKU_AVAILABILITY_SKU)
& (dependencies_df['numerator_result'] == 0
)]['numerator_id'].unique().tolist()
return hero_list
def get_hero_type_custom_entity_df(self):
hero_type_df = self.custom_entities[self.custom_entities['entity_type']
== self.HERO_TYPE]
hero_type_df.rename(columns={'pk': 'entity_fk'}, inplace=True)
return hero_type_df
class HEINEKENTWToolBox:
LEVEL1 = 1
LEVEL2 = 2
LEVEL3 = 3
DIST_STORE_LVL1 = 1014
OOS_STORE_LVL1 = 1015
DIST_STORE_LVL2 = 1016
OOS_STORE_LVL2 = 1017
DIST_CATEGORY_LVL1 = 1018
OOS_CATEGORY_LVL1 = 1019
DIST_CATEGORY_LVL2 = 1020
OOS_CATEGORY_LVL2 = 1021
DISTRIBUTION = 4
OOS = 5
MANUFACTURER_FK = 175 # heinken manfucturer
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.common = Common(self.data_provider)
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.rds_conn = PSProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.kpi_static_data = self.common.get_new_kpi_static_data()
self.kpi_results_queries = []
self.assortment = Assortment(self.data_provider, self.output)
def main_calculation(self, *args, **kwargs):
"""
This function calculates the KPI results.
"""
lvl3_result = self.assortment.calculate_lvl3_assortment()
self.category_assortment_calculation(lvl3_result)
self.store_assortment_calculation(lvl3_result)
self.common.commit_results_data_to_new_tables()
# self.common.commit_results_data_to_new_tables()
def category_assortment_calculation(self, lvl3_result):
"""
This function calculates 3 levels of assortment :
level3 is assortment SKU
level2 is assortment groups
"""
if not lvl3_result.empty:
# cat_df = self.scif[['product_fk', 'category_fk']]
cat_df = self.all_products[['product_fk', 'category_fk']]
lvl3_with_cat = lvl3_result.merge(cat_df,
on='product_fk',
how='left')
lvl3_with_cat = lvl3_with_cat[
lvl3_with_cat['category_fk'].notnull()]
for result in lvl3_with_cat.itertuples():
if result.in_store == 1:
score = self.DISTRIBUTION
else:
score = self.OOS
# Distrubtion
self.common.write_to_db_result_new_tables(
fk=self.DIST_CATEGORY_LVL1,
numerator_id=result.product_fk,
numerator_result=score,
result=score,
denominator_id=result.category_fk,
denominator_result=1,
score=score,
score_after_actions=score)
if score == self.OOS:
# OOS
self.common.write_to_db_result_new_tables(
fk=self.OOS_CATEGORY_LVL1,
numerator_id=result.product_fk,
numerator_result=score,
result=score,
denominator_id=result.category_fk,
denominator_result=1,
score=score,
score_after_actions=score)
category_list = lvl3_with_cat['category_fk'].unique()
for cat in category_list:
lvl3_result_cat = lvl3_with_cat[lvl3_with_cat["category_fk"] ==
cat]
lvl2_result = self.assortment.calculate_lvl2_assortment(
lvl3_result_cat)
for result in lvl2_result.itertuples():
denominator_res = result.total
res = np.divide(float(result.passes),
float(denominator_res))
# Distrubtion
self.common.write_to_db_result_new_tables(
fk=self.DIST_CATEGORY_LVL2,
numerator_id=self.MANUFACTURER_FK,
numerator_result=result.passes,
denominator_id=cat,
denominator_result=denominator_res,
result=res,
score=res,
score_after_actions=res)
# OOS
self.common.write_to_db_result_new_tables(
fk=self.OOS_CATEGORY_LVL2,
numerator_id=self.MANUFACTURER_FK,
numerator_result=denominator_res - result.passes,
denominator_id=cat,
denominator_result=denominator_res,
result=1 - res,
score=(1 - res),
score_after_actions=1 - res)
return
def store_assortment_calculation(self, lvl3_result):
"""
This function calculates the KPI results.
"""
for result in lvl3_result.itertuples():
if result.in_store == 1:
score = self.DISTRIBUTION
else:
score = self.OOS
# Distrubtion
self.common.write_to_db_result_new_tables(
fk=self.DIST_STORE_LVL1,
numerator_id=result.product_fk,
numerator_result=score,
result=score,
denominator_id=self.store_id,
denominator_result=1,
score=score)
if score == self.OOS:
# OOS
self.common.write_to_db_result_new_tables(
fk=self.OOS_STORE_LVL1,
numerator_id=result.product_fk,
numerator_result=score,
result=score,
denominator_id=self.store_id,
denominator_result=1,
score=score,
score_after_actions=score)
if not lvl3_result.empty:
lvl2_result = self.assortment.calculate_lvl2_assortment(
lvl3_result)
for result in lvl2_result.itertuples():
denominator_res = result.total
if not pd.isnull(result.target) and not pd.isnull(
result.group_target_date
) and result.group_target_date <= self.assortment.current_date:
denominator_res = result.target
res = np.divide(float(result.passes), float(denominator_res))
# Distrubtion
self.common.write_to_db_result_new_tables(
fk=self.DIST_STORE_LVL2,
numerator_id=self.MANUFACTURER_FK,
denominator_id=self.store_id,
numerator_result=result.passes,
denominator_result=denominator_res,
result=res,
score=res,
score_after_actions=res)
# OOS
self.common.write_to_db_result_new_tables(
fk=self.OOS_STORE_LVL2,
numerator_id=self.MANUFACTURER_FK,
numerator_result=denominator_res - result.passes,
denominator_id=self.store_id,
denominator_result=denominator_res,
result=1 - res,
score=1 - res,
score_after_actions=1 - res)
return
