def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.project_name = self.data_provider.project_name
self.common = Common(self.data_provider)
self.old_common = oldCommon(self.data_provider)
self.rds_conn = PSProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.session_fk = self.data_provider.session_id
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.store_info = self.data_provider[Data.STORE_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.survey = Survey(self.data_provider)
self.block = Block(self.data_provider)
self.general_toolbox = GENERALToolBox(self.data_provider)
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.template_path = self.get_relevant_template()
self.gap_data = self.get_gap_data()
self.kpi_weights = parse_template(self.template_path,
Consts.KPI_WEIGHT,
lower_headers_row_index=0)
self.template_data = self.parse_template_data()
self.kpis_gaps = list()
self.passed_availability = list()
self.kpi_static_data = self.old_common.get_kpi_static_data()
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]
def __init__(self, data_provider, output):
GlobalSessionToolBox.__init__(self, data_provider, output)
self.adjacency = Adjancency(data_provider)
self.block = Block(data_provider)
self.kpi_static_data = self.common.get_kpi_static_data()
self.ps_data_provider = PsDataProvider(data_provider)
self._scene_types = None
self.external_targets = self.ps_data_provider.get_kpi_external_targets(
)
def __init__(self, scene_toolbox_obj):
self.__dict__.update(scene_toolbox_obj.__dict__)
self.current_scene_fk = self.scene_info.iloc[0].scene_fk
self.store_banner_name = self.store_info.iloc[
0].additional_attribute_20
self.template_name = self.templates.iloc[0].template_name
self.custom_entity_data = self.get_relevant_custom_entity_data()
self.match_product_data = self.match_product_in_scene.merge(
self.products, on='product_fk', how='left')
self.block = Block(self.data_provider, self.output)
def calculate(self):
if not self.util.filtered_matches.empty:
self.util.filtered_scif, self.util.filtered_matches = \
self.util.commontools.set_filtered_scif_and_matches_for_specific_kpi(self.util.filtered_scif,
self.util.filtered_matches,
self.util.PRODUCT_BLOCKING)
filtered_matches = self.util.filtered_matches.copy()
if 'sub_category_fk' in filtered_matches.columns:
filtered_matches = filtered_matches.drop(columns=['sub_category_fk'])
self.block = Block(self.data_provider, custom_scif=self.util.filtered_scif,
custom_matches=filtered_matches)
if not self.util.filtered_matches.empty:
self.calculate_product_blocking()
self.util.reset_filtered_scif_and_matches_to_exclusion_all_state()
class ProductBlockingKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(ProductBlockingKpi, self).__init__(data_provider, config_params=config_params, **kwargs)
self.util = PepsicoUtil(None, data_provider)
self.block = None
def kpi_type(self):
pass
def calculate(self):
if not self.util.filtered_matches.empty:
self.util.filtered_scif, self.util.filtered_matches = \
self.util.commontools.set_filtered_scif_and_matches_for_specific_kpi(self.util.filtered_scif,
self.util.filtered_matches,
self.util.PRODUCT_BLOCKING)
filtered_matches = self.util.filtered_matches.copy()
if 'sub_category_fk' in filtered_matches.columns:
filtered_matches = filtered_matches.drop(columns=['sub_category_fk'])
self.block = Block(self.data_provider, custom_scif=self.util.filtered_scif,
custom_matches=filtered_matches)
if not self.util.filtered_matches.empty:
self.calculate_product_blocking()
self.util.reset_filtered_scif_and_matches_to_exclusion_all_state()
def calculate_product_blocking(self):
external_targets = self.util.all_targets_unpacked[self.util.all_targets_unpacked['type'] == self.util.PRODUCT_BLOCKING]
additional_block_params = {'check_vertical_horizontal': True, 'minimum_facing_for_block': 3,
'include_stacking': True,
'allowed_products_filters': {'product_type': ['Empty']}}
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.util.PRODUCT_BLOCKING)
for i, row in external_targets.iterrows():
# print row['Group Name']
group_fk = self.util.custom_entities[self.util.custom_entities['name'] == row['Group Name']]['pk'].values[0]
# filters = self.util.get_block_and_adjacency_filters(row)
filters = self.util.get_block_filters(row)
target = row['Target']
additional_block_params.update({'minimum_block_ratio': float(target)/100})
result_df = self.block.network_x_block_together(filters, additional=additional_block_params)
score = max_ratio = 0
result = self.util.commontools.get_yes_no_result(0)
if not result_df.empty:
max_ratio = result_df['facing_percentage'].max()
result_df = result_df[result_df['is_block']==True]
if not result_df.empty:
max_ratio = result_df['facing_percentage'].max()
result_df = result_df[result_df['facing_percentage'] == max_ratio]
result = self.util.commontools.get_yes_no_result(1)
orientation = result_df['orientation'].values[0]
score = self.util.commontools.get_kpi_result_value_pk_by_value(orientation.upper())
# print score
self.write_to_db_result(fk=kpi_fk, numerator_id=group_fk, denominator_id=self.util.store_id,
numerator_result=max_ratio * 100,
score=score, result=result, target=target, by_scene=True)
self.util.block_results = self.util.block_results.append(pd.DataFrame([{'Group Name': row['Group Name'],
'Score':
result_df['is_block'].values[
0] if not result_df.empty else False}]))
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.ps_data_provider = PsDataProvider(self.data_provider, self.output)
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.block = Block(data_provider)
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 = self.ps_data_provider.rds_conn
self.kpi_static_data = self.common.get_kpi_static_data()
self.session_info = self.data_provider[Data.SESSION_INFO]
self.session_fk = self.session_info['pk'].values[0]
self.kpi_results_queries = []
self.kpi_static_queries = []
self.own_manufacturer_fk = int(
self.data_provider.own_manufacturer.param_value.values[0])
self.adjacency = BlockAdjacency(self.data_provider,
ps_data_provider=self.ps_data_provider,
common=self.common,
rds_conn=self.rds_conn)
self.eyelight = Eyelight(self.data_provider, self.common,
self.ps_data_provider)
self.merged_scif_mpis = self.match_product_in_scene.merge(
self.scif,
how='left',
left_on=['scene_fk', 'product_fk'],
right_on=['scene_fk', 'product_fk'])
self.targets = self.ps_data_provider.get_kpi_external_targets(
key_fields=[
"KPI Type", "Location: JSON", "Config Params: JSON",
"Dataset 1: JSON", "Dataset 2: JSON"
])
self.results_df = pd.DataFrame(columns=[
'kpi_name', 'kpi_fk', 'numerator_id', 'numerator_result',
'context_id', 'denominator_id', 'denominator_result', 'result',
'score'
])
self.custom_entity_table = self.get_kpi_custom_entity_table()
def __init__(self, data_provider, output):
self.data_provider = data_provider
self.common = Common(data_provider)
self.output = output
self.ps_data_provider = PsDataProvider(data_provider, output)
self.session_info = self.data_provider[Data.SESSION_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.manufacturer_id = self.get_manufacturer_id_from_manufacturer_name(Const.MANUFACTURER)
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.template_info = self.data_provider[Data.TEMPLATES]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.block = Block(self.data_provider, self.output, common=self.common)
self.mpis = self.match_product_in_scene \
.merge(self.products, on='product_fk', suffixes=['', '_p']) \
.merge(self.scene_info, on='scene_fk', suffixes=['', '_s'])[COLUMNS]
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_kpi_static_data()
self.kpi_results_queries = []
self.set_up_template = pd.read_excel(os.path.join(
os.path.dirname(os.path.realpath(__file__)), '..', 'Data',
'gsk_set_up.xlsx'),
sheet_name='Functional KPIs',
keep_default_na=False)
self.gsk_generator = GSKGenerator(self.data_provider, self.output,
self.common, self.set_up_template)
self.blocking_generator = Block(self.data_provider)
self.assortment = self.gsk_generator.get_assortment_data_provider()
self.store_info = self.data_provider['store_info']
self.store_fk = self.data_provider[StoreInfoConsts.STORE_FK]
self.ps_data_provider = PsDataProvider(self.data_provider, self.output)
self.targets = self.ps_data_provider.get_kpi_external_targets(
key_fields=Consts.KEY_FIELDS, data_fields=Consts.DATA_FIELDS)
self.own_manufacturer = self.get_manufacturer
self.set_up_data = {
(Consts.PLN_BLOCK, Const.KPI_TYPE_COLUMN): Const.NO_INFO,
(Consts.POSITION_SCORE, Const.KPI_TYPE_COLUMN): Const.NO_INFO,
(Consts.ECAPS_FILTER_IDENT, Const.KPI_TYPE_COLUMN): Const.NO_INFO,
(Consts.PLN_MSL, Const.KPI_TYPE_COLUMN): Const.NO_INFO,
("GSK_PLN_LSOS_SCORE", Const.KPI_TYPE_COLUMN): Const.NO_INFO,
(Consts.POSM, Const.KPI_TYPE_COLUMN): Const.NO_INFO
}
def __init__(self, data_provider, output):
GlobalSessionToolBox.__init__(self, data_provider, output)
self.templates = {}
self.parse_template()
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.match_scene_item_facts = pd.merge(
self.scif,
self.match_product_in_scene,
how='right',
left_on=['item_id', 'scene_id'],
right_on=['product_fk',
'scene_fk']) # Merges scif with mpis on product_fk
self.block = Block(data_provider)
self.own_manufacturer_fk = int(
self.data_provider.own_manufacturer.param_value.values[0])
self.results_df = pd.DataFrame(columns=[
'kpi_name', 'kpi_fk', 'numerator_id', 'numerator_result',
'context_id', 'denominator_id', 'denominator_result', 'result',
'score'
])
def __init__(self, output, data_provider):
super(PNGJP_SAND2BlockGoldenUtil, self).__init__(data_provider)
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.templates = self.data_provider[Data.TEMPLATES]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.scif = self.data_provider.scene_item_facts
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_info = self.data_provider[Data.STORE_INFO]
self.store_id = self.store_info.iloc[0].store_fk
self.store_type = self.data_provider.store_type
self.rds_conn = PSProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.kpi_static_data = self.common.get_kpi_static_data()
self.ps_data_provider = PsDataProvider(self.data_provider, self.output)
self.template_parser = PNGJPTemplateParser(self.data_provider,
self.rds_conn)
self.targets_from_template = self.template_parser.get_targets()
self.custom_entity_data = self.template_parser.get_custom_entity()
self.external_targets = self.template_parser.get_external_targets()
self.match_display_in_scene = self.data_provider.match_display_in_scene
self.current_scene_fk = self.scene_info.iloc[0].scene_fk
self.template_name = self.templates.iloc[0].template_name
self.match_product_data = self.match_product_in_scene.merge(
self.products, on='product_fk', how='left')
self.block = Block(self.data_provider, self.output)
class ColdCutToolBox:
LEVEL1 = 1
LEVEL2 = 2
LEVEL3 = 3
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.ps_data_provider = PsDataProvider(self.data_provider, self.output)
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.block = Block(data_provider)
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 = self.ps_data_provider.rds_conn
self.kpi_static_data = self.common.get_kpi_static_data()
self.session_info = self.data_provider[Data.SESSION_INFO]
self.session_fk = self.session_info['pk'].values[0]
self.kpi_results_queries = []
self.kpi_static_queries = []
self.own_manufacturer_fk = int(
self.data_provider.own_manufacturer.param_value.values[0])
self.adjacency = BlockAdjacency(self.data_provider,
ps_data_provider=self.ps_data_provider,
common=self.common,
rds_conn=self.rds_conn)
self.eyelight = Eyelight(self.data_provider, self.common,
self.ps_data_provider)
self.merged_scif_mpis = self.match_product_in_scene.merge(
self.scif,
how='left',
left_on=['scene_fk', 'product_fk'],
right_on=['scene_fk', 'product_fk'])
self.targets = self.ps_data_provider.get_kpi_external_targets(
key_fields=[
"KPI Type", "Location: JSON", "Config Params: JSON",
"Dataset 1: JSON", "Dataset 2: JSON"
])
self.results_df = pd.DataFrame(columns=[
'kpi_name', 'kpi_fk', 'numerator_id', 'numerator_result',
'context_id', 'denominator_id', 'denominator_result', 'result',
'score'
])
self.custom_entity_table = self.get_kpi_custom_entity_table()
def main_calculation(self):
"""
This function calculates the KPI results.
"""
relevant_kpi_types = [
Consts.SOS, Consts.HORIZONTAL_SHELF_POSITION,
Consts.VERTICAL_SHELF_POSITION, Consts.BLOCKING, Consts.BLOCK_ADJ,
Consts.BLOCKING_ORIENTATION
]
targets = self.targets[self.targets[Consts.ACTUAL_TYPE].isin(
relevant_kpi_types)]
self._calculate_kpis_from_template(targets)
self.save_results_to_db()
return
def calculate_blocking(self, row, df):
if df.empty:
return None
additional_data = row['Config Params: JSON']
location_data = row['Location: JSON']
kpi_fk = row['kpi_fk']
population_data = row['Dataset 1: JSON']['include'][0]
result_dict_list = self._logic_for_blocking(kpi_fk, population_data,
location_data,
additional_data)
return result_dict_list
def calculate_blocking_adj(self, row, df):
result_dict_list = []
additional_data = row['Config Params: JSON']
location_data = row['Location: JSON']
kpi_fk = row['kpi_fk']
anchor_data = row['Dataset 1: JSON']['include'][0]
target_data = row['Dataset 2: JSON']['include'][0]
context_type = additional_data.get('context_type')
if context_type:
target_df = ParseInputKPI.filter_df(target_data, self.scif)
target_values = target_df[context_type].unique().tolist()
context_values = [
v for v in df[context_type].unique().tolist()
if v and pd.notna(v) and v in target_values
]
for context_value in context_values:
anchor_data.update({context_type: [context_value]})
target_data.update({context_type: [context_value]})
result_dict = self._logic_for_adj(
kpi_fk,
anchor_data,
target_data,
location_data,
additional_data,
eyelight_prefix='{}-'.format(context_value),
custom_entity=context_value)
result_dict_list.append(result_dict)
else:
result_dict = self._logic_for_adj(kpi_fk, anchor_data, target_data,
location_data, additional_data)
result_dict_list.append(result_dict)
return result_dict_list
def _logic_for_adj(self,
kpi_fk,
anchor_data,
target_data,
location_data,
additional_data,
custom_entity=None,
eyelight_prefix=None):
result = self.adjacency.evaluate_block_adjacency(
anchor_data,
target_data,
location=location_data,
additional=additional_data,
kpi_fk=kpi_fk,
eyelight_prefix=eyelight_prefix)
result_type_fk = Consts.CUSTOM_RESULT['Yes'] if result and pd.notna(
result) else Consts.CUSTOM_RESULT['No']
result_dict = {
'kpi_fk': kpi_fk,
'numerator_id': self.own_manufacturer_fk,
'denominator_id': self.store_id,
'numerator_result': 1 if result else 0,
'denominator_result': 1,
'result': result_type_fk
}
if custom_entity:
result_dict.update(
{'context_id': self.get_custom_entity_value(custom_entity)})
return result_dict
def _logic_for_blocking(self, kpi_fk, population_data, location_data,
additional_data):
result_dict_list = []
additional_data.update({'use_masking_only': True})
block = self.block.network_x_block_together(population=population_data,
location=location_data,
additional=additional_data)
for row in block.itertuples():
scene_match_fks = list(row.cluster.nodes[list(
row.cluster.nodes())[0]]['scene_match_fk'])
self.eyelight.write_eyelight_result(scene_match_fks, kpi_fk)
passed_block = block[block['is_block']]
if passed_block.empty:
numerator_result = 0
result_value = "No"
else:
numerator_result = 1
result_value = "Yes"
result_type_fk = Consts.CUSTOM_RESULT[result_value]
# numerator_id = df.custom_entity_fk.iloc[0]
result_dict = {
'kpi_fk': kpi_fk,
'numerator_id': self.own_manufacturer_fk,
'numerator_result': numerator_result,
'denominator_id': self.store_id,
'denominator_result': 1,
'result': result_type_fk
}
result_dict_list.append(result_dict)
return result_dict_list
def calculate_blocking_orientation(self, row, df):
if df.empty:
return
result_dict_list = []
additional_data = row['Config Params: JSON']
location_data = row['Location: JSON']
kpi_fk = row['kpi_fk']
population_data = row['Dataset 1: JSON']
if population_data:
population_data = population_data['include'][0]
else:
population_data = {}
additional_data.update({
'vertical_horizontal_methodology':
['bucketing', 'percentage_of_shelves'],
'shelves_required_for_vertical':
.8,
'check_vertical_horizontal':
True
})
numerator_type = additional_data.get('numerator_type')
if numerator_type:
numerator_values = [
v for v in df[numerator_type].unique().tolist()
if v and pd.notna(v)
]
for numerator_value in numerator_values:
population_data.update({numerator_type: [numerator_value]})
result_dict = self._logic_for_blocking_orientation(
kpi_fk, population_data, location_data, additional_data,
numerator_value)
result_dict_list.append(result_dict)
else:
result_dict = self._logic_for_blocking_orientation(
kpi_fk, population_data, location_data, additional_data)
result_dict_list.append(result_dict)
return result_dict_list
def _logic_for_blocking_orientation(self,
kpi_fk,
population_data,
location_data,
additional_data,
custom_entity=None):
additional_data.update({'use_masking_only': True})
block = self.block.network_x_block_together(population=population_data,
location=location_data,
additional=additional_data)
if custom_entity:
prefix = '{}-'.format(custom_entity)
numerator_id = self.get_custom_entity_value(custom_entity)
else:
prefix = None
numerator_id = self.own_manufacturer_fk
for row in block.itertuples():
scene_match_fks = list(row.cluster.nodes[list(
row.cluster.nodes())[0]]['scene_match_fk'])
self.eyelight.write_eyelight_result(scene_match_fks,
kpi_fk,
prefix=prefix)
passed_block = block[block['is_block']]
if passed_block.empty:
result_value = "Not Blocked"
else:
result_value = passed_block.orientation.iloc[0]
result = Consts.CUSTOM_RESULT[result_value]
result_dict = {
'kpi_fk': kpi_fk,
'numerator_id': numerator_id,
'numerator_result': 1 if result_value != 'Not Blocked' else 0,
'denominator_id': self.store_id,
'denominator_result': 1,
'result': result
}
return result_dict
def calculate_vertical_position(self, row, df):
result_dict_list = []
mpis = df # get this from the external target filter_df method thingy
scene_facings_df = mpis.groupby(['scene_fk', 'product_fk'],
as_index=False)['facings'].max()
scene_facings_df.rename(columns={'facings': 'scene_facings'},
inplace=True)
shelf_df = self.merged_scif_mpis.groupby(
['scene_fk', 'bay_number'],
as_index=False)['shelf_number_from_bottom'].max()
shelf_df.rename(columns={'shelf_number_from_bottom': 'shelf_count'},
inplace=True)
pre_sort_mpis = pd.merge(mpis,
scene_facings_df,
how='left',
on=['scene_fk', 'product_fk'])
scene_facings_df_sorted = pre_sort_mpis.sort_values('scene_facings')
mpis = scene_facings_df_sorted.drop_duplicates(
['scene_fk', 'product_fk'], keep="last")
mpis = pd.merge(mpis,
shelf_df,
how='left',
on=['scene_fk', 'bay_number'])
mpis['position'] = mpis.apply(self._calculate_vertical_position,
axis=1)
mpis['result_type_fk'] = mpis['position'].apply(
lambda x: Consts.CUSTOM_RESULT.get(x, 0))
mpis = mpis.groupby(['product_fk'],
as_index=False)['result_type_fk'].agg(
lambda x: pd.Series.mode(x).iat[0])
for result in mpis.itertuples():
custom_fk_result = result.result_type_fk
if type(custom_fk_result) == numpy.ndarray:
custom_fk_result = result.result_type_fk[0]
result_item = {
'kpi_fk': row.kpi_fk,
'numerator_id': result.product_fk,
'numerator_result': 1,
'denominator_id': self.store_id,
'denominator_result': 1,
'result': custom_fk_result,
'score': 0
}
result_dict_list.append(result_item)
return result_dict_list
def calculate_horizontal_position(self, row, df):
result_dict_list = []
mpis = df # get this from the external target filter_df method thingy
scene_facings_df = mpis.groupby(['scene_fk', 'product_fk'],
as_index=False)['facings'].max()
scene_facings_df.rename(columns={'facings': 'scene_facings'},
inplace=True)
pre_sort_mpis = pd.merge(mpis,
scene_facings_df,
how='left',
on=['scene_fk', 'product_fk'])
bay_df = pre_sort_mpis.groupby('scene_fk',
as_index=False)['bay_number'].max()
bay_df.rename(columns={'bay_number': 'bay_count'}, inplace=True)
mpis = pd.merge(pre_sort_mpis, bay_df, how='left', on='scene_fk')
mpis['position'] = mpis.apply(self._calculate_horizontal_position,
axis=1)
mpis['result_type_fk'] = mpis['position'].apply(
lambda x: Consts.CUSTOM_RESULT.get(x, 0))
mpis = mpis.groupby(['product_fk'],
as_index=False)['result_type_fk'].agg(
lambda x: pd.Series.mode(x).iat[0])
for result in mpis.itertuples():
custom_fk_result = result.result_type_fk
if type(custom_fk_result) == numpy.ndarray:
custom_fk_result = result.result_type_fk[0]
result_item = {
'kpi_fk': row.kpi_fk,
'numerator_id': result.product_fk,
'numerator_result': 1,
'denominator_id': self.store_id,
'denominator_result': 1,
'result': custom_fk_result,
'score': 0
}
result_dict_list.append(result_item)
return result_dict_list
@staticmethod
def _calculate_horizontal_position(row):
bay_count = row.bay_count
if bay_count == 1:
return 'Center'
factor = round(bay_count / float(3))
if row.bay_number <= factor:
return 'Left'
elif row.bay_number > (bay_count - factor):
return 'Right'
return 'Center'
@staticmethod
def _calculate_vertical_position(row):
shelf_number = str(row.shelf_number_from_bottom)
shelf_count = str(row.shelf_count)
shelf_count_pos_map = Consts.shelf_map[shelf_count]
pos_value = shelf_count_pos_map[shelf_number]
return pos_value
def calculate_facings_sos(self, row, df):
data_filter = {'population': row['Dataset 2: JSON']}
if 'include' not in data_filter['population'].keys():
data_filter['population'].update(
{'include': [{
'session_id': self.session_fk
}]})
data_filter.update({'location': row['Location: JSON']})
config_json = row['Config Params: JSON']
numerator_type = config_json['numerator_type']
df = ParseInputKPI.filter_df(data_filter, self.scif)
result_dict_list = self._logic_for_sos(row, df, numerator_type)
return result_dict_list
def _logic_for_sos(self, row, df, numerator_type):
result_list = []
facing_type = 'facings'
config_json = row['Config Params: JSON']
if 'include_stacking' in config_json:
if config_json['include_stacking']:
facing_type = 'facings_ign_stack'
for num_item in df[numerator_type].unique().tolist():
if num_item:
numerator_scif = df[df[numerator_type] == num_item]
else:
numerator_scif = df[df[numerator_type].isnull()]
num_item = 'None'
numerator_result = numerator_scif[facing_type].sum()
denominator_result = df[facing_type].sum()
custom_entity_fk = self.get_custom_entity_value(num_item)
sos_value = self.calculate_percentage_from_numerator_denominator(
numerator_result, denominator_result)
result_dict = {
'kpi_fk': row.kpi_fk,
'numerator_id': custom_entity_fk,
'numerator_result': numerator_result,
'denominator_id': self.store_id,
'denominator_result': denominator_result,
'result': sos_value
}
result_list.append(result_dict)
return result_list
def _get_calculation_function_by_kpi_type(self, kpi_type):
if kpi_type == Consts.SOS:
return self.calculate_facings_sos
elif kpi_type == Consts.HORIZONTAL_SHELF_POSITION:
return self.calculate_horizontal_position
elif kpi_type == Consts.VERTICAL_SHELF_POSITION:
return self.calculate_vertical_position
elif kpi_type == Consts.BLOCKING:
return self.calculate_blocking
elif kpi_type == Consts.BLOCK_ADJ:
return self.calculate_blocking_adj
elif kpi_type == Consts.BLOCKING_ORIENTATION:
return self.calculate_blocking_orientation
def _calculate_kpis_from_template(self, template_df):
for i, row in template_df.iterrows():
try:
calculation_function = self._get_calculation_function_by_kpi_type(
row[Consts.ACTUAL_TYPE])
row = self.apply_json_parser(row)
merged_scif_mpis = self._parse_json_filters_to_df(row)
result_data = calculation_function(row, merged_scif_mpis)
if result_data and isinstance(result_data, list):
for result in result_data:
self.results_df.loc[len(self.results_df),
result.keys()] = result
elif result_data and isinstance(result_data, dict):
self.results_df.loc[len(self.results_df),
result_data.keys()] = result_data
except Exception as e:
Log.error('Unable to calculate {}: {}'.format(
row[Consts.KPI_NAME], e))
def _parse_json_filters_to_df(self, row):
jsonv = row[(row.index.str.contains('JSON'))
& (~row.index.str.contains('Config Params')) &
(~row.index.str.contains('Dataset 2'))]
filter_json = jsonv[~jsonv.isnull()]
filtered_scif_mpis = self.merged_scif_mpis
for each_json in filter_json:
final_json = {
'population': each_json
} if ('include' or 'exclude') in each_json else each_json
filtered_scif_mpis = ParseInputKPI.filter_df(
final_json, filtered_scif_mpis)
if 'include_stacking' in row['Config Params: JSON'].keys():
including_stacking = row['Config Params: JSON'][
'include_stacking'][0]
filtered_scif_mpis[Consts.FINAL_FACINGS] = \
filtered_scif_mpis.facings if including_stacking == 'True' else filtered_scif_mpis.facings_ign_stack
filtered_scif_mpis = filtered_scif_mpis[
filtered_scif_mpis.stacking_layer == 1]
return filtered_scif_mpis
def apply_json_parser(self, row):
json_relevent_rows_with_parse_logic = row[
(row.index.str.contains('JSON')) & (row.notnull())].apply(
self.parse_json_row)
row = row[~row.index.isin(json_relevent_rows_with_parse_logic.index
)].append(
json_relevent_rows_with_parse_logic)
return row
def parse_json_row(self, item):
'''
:param item: improper json value (formatted incorrectly)
:return: properly formatted json dictionary
The function will be in conjunction with apply. The function will applied on the row(pandas series). This is
meant to convert the json comprised of improper format of strings and lists to a proper dictionary value.
'''
if item:
try:
container = self.prereq_parse_json_row(item)
except Exception as e:
container = None
Log.warning('{}: Unable to parse json for: {}'.format(e, item))
else:
container = None
return container
def save_results_to_db(self):
self.results_df.drop(columns=['kpi_name'], inplace=True)
self.results_df.rename(columns={'kpi_fk': 'fk'}, inplace=True)
self.results_df['result'].fillna(0, inplace=True)
self.results_df['score'].fillna(0, inplace=True)
results = self.results_df.to_dict('records')
for result in results:
result = simplejson.loads(simplejson.dumps(result,
ignore_nan=True))
self.common.write_to_db_result(**result)
@staticmethod
def prereq_parse_json_row(item):
'''
primarly logic for formatting the value of the json
'''
container = dict()
try:
container = ast.literal_eval(item)
except:
json_str = ",".join(item)
json_str_fixed = json_str.replace("'", '"')
container = json.loads(json_str_fixed)
return container
@staticmethod
def _get_numerator_and_denominator_type(config_param,
context_relevant=False):
numerator_type = config_param['numerator_type'][0]
denominator_type = config_param['denominator_type'][0]
if context_relevant:
context_type = config_param['context_type'][0]
return numerator_type, denominator_type, context_type
return numerator_type, denominator_type
@staticmethod
def calculate_percentage_from_numerator_denominator(
numerator_result, denominator_result):
try:
ratio = numerator_result / denominator_result
except Exception as e:
Log.error(e.message)
ratio = 0
if not isinstance(ratio, (float, int)):
ratio = 0
return round(ratio * 100, 2)
def get_kpi_custom_entity_table(self):
"""
:param entity_type: pk of entity from static.entity_type
:return: the DF of the static.custom_entity of this entity_type
"""
query = "SELECT pk, name, entity_type_fk FROM static.custom_entity;"
df = pd.read_sql_query(query, self.rds_conn.db)
return df
def get_custom_entity_value(self, value):
try:
custom_fk = self.custom_entity_table['pk'][
self.custom_entity_table['name'] == value].iloc[0]
return custom_fk
except IndexError:
Log.error('No custom entity found for: {}'.format(value))
return None
def commit_results(self):
self.common.commit_results_data()
class SceneLayoutComplianceCalc(object):
def __init__(self, scene_toolbox_obj):
self.__dict__.update(scene_toolbox_obj.__dict__)
self.current_scene_fk = self.scene_info.iloc[0].scene_fk
self.store_banner_name = self.store_info.iloc[
0].additional_attribute_20
self.template_name = self.templates.iloc[0].template_name
self.custom_entity_data = self.get_relevant_custom_entity_data()
self.match_product_data = self.match_product_in_scene.merge(
self.products, on='product_fk', how='left')
self.block = Block(self.data_provider, self.output)
def parse_include_config_from_setup(self, kpi_details):
include_empty = False
include_other = False
relev_setup_templtate = self.set_up_template[
self.set_up_template['KPI Type'] == kpi_details.iloc[0]
[KPI_TYPE_COL]]
if not relev_setup_templtate.empty:
Log.info("Missing KPI Config in setup template: "
"KPI {kpi} for session: {sess} and scene: {scene}".format(
kpi=kpi_details.iloc[0][KPI_TYPE_COL],
sess=self.session_uid,
scene=self.current_scene_fk,
))
empty_value = relev_setup_templtate['Include Empty'].iloc[0]
if not pd.isnull(empty_value) and empty_value.lower().strip(
) == "include":
include_empty = True
others_value = relev_setup_templtate["Include Others"].iloc[0]
if not pd.isnull(others_value) and others_value.lower().strip(
) == "include":
include_other = True
return include_empty, include_other
def ensure_as_list(self, template_fks):
if isinstance(template_fks, list):
ext_target_template_fks = template_fks
else:
ext_target_template_fks = list([template_fks])
return ext_target_template_fks
def check_if_all_kpis_available(self):
layout_compliance_kpis = [
GSK_LAYOUT_COMPLIANCE_BLOCK, GSK_LAYOUT_COMPLIANCE_BRAND_FSOS,
GSK_LAYOUT_COMPLIANCE_POSITION, GSK_LAYOUT_COMPLIANCE_SEQUENCE,
GSK_LAYOUT_COMPLIANCE_SBRAND_FSOS
]
kpis_not_found = []
for kpi_name in layout_compliance_kpis:
res_df = self.kpi_static_data[self.kpi_static_data[KPI_TYPE_COL] ==
kpi_name]
if res_df.empty:
kpis_not_found.append(kpi_name)
Log.warning(
"Error: KPI {} not found in static.kpi_level_2 table.".
format(kpi_name))
status = True if len(kpis_not_found) == 0 else False
return status
def get_relevant_custom_entity_data(self):
Log.info(
"Getting custom entity data for the present super brands and store banner..."
)
columns_to_check = ["store_banner_pk", "super_brand_pk"]
status = True
for column in columns_to_check:
if column not in self.targets.columns:
Log.error(
"Error: {} not found in external targets".format(column))
status = False
if not status:
return pd.DataFrame()
query = """ select * from static.custom_entity where pk in {custom_entity_pks};"""
custom_entity_data = pd.read_sql_query(
query.format(custom_entity_pks=tuple(
np.concatenate((self.targets['store_banner_pk'].dropna(
).unique().astype('int'), self.targets['super_brand_pk'].
dropna().unique().astype('int'))))),
self.rds_conn.db)
return custom_entity_data
def calculate_all(self):
if not self.check_if_all_kpis_available():
Log.warning(
'Unable to calculate GSK_LAYOUT_COMPLIANCE_KPIs: KPIs are not in kpi_level_2'
)
return
if self.targets.empty:
Log.warning(
'Unable to calculate GSK_LAYOUT_COMPLIANCE_KPIs: external targets are empty'
)
return
gsk_layout_compliance_brand_fsos = self.kpi_static_data[
(self.kpi_static_data[KPI_TYPE_COL] ==
GSK_LAYOUT_COMPLIANCE_BRAND_FSOS)
& (self.kpi_static_data['delete_time'].isnull())]
gsk_layout_compliance_position = self.kpi_static_data[
(self.kpi_static_data[KPI_TYPE_COL] ==
GSK_LAYOUT_COMPLIANCE_POSITION)
& (self.kpi_static_data['delete_time'].isnull())]
gsk_layout_compliance_sbrand_fsos = self.kpi_static_data[
(self.kpi_static_data[KPI_TYPE_COL] ==
GSK_LAYOUT_COMPLIANCE_SBRAND_FSOS)
& (self.kpi_static_data['delete_time'].isnull())]
gsk_layout_compliance_sequence = self.kpi_static_data[
(self.kpi_static_data[KPI_TYPE_COL] ==
GSK_LAYOUT_COMPLIANCE_SEQUENCE)
& (self.kpi_static_data['delete_time'].isnull())]
gsk_layout_compliance_block = self.kpi_static_data[
(self.kpi_static_data[KPI_TYPE_COL] == GSK_LAYOUT_COMPLIANCE_BLOCK)
& (self.kpi_static_data['delete_time'].isnull())]
try:
self.calculate_gsk_layout_compliance_brand_fsos(
kpi_details=gsk_layout_compliance_brand_fsos)
except Exception as e:
Log.error("Error : {}".format(e))
try:
self.calculate_gsk_layout_compliance_block(
kpi_details=gsk_layout_compliance_block)
except Exception as e:
Log.error("Error : {}".format(e))
try:
self.calculate_gsk_layout_compliance_sequence(
kpi_details=gsk_layout_compliance_sequence)
except Exception as e:
Log.error("Error : {}".format(e))
try:
self.calculate_gsk_layout_compliance_super_brand_fsos(
kpi_details=gsk_layout_compliance_sbrand_fsos)
except Exception as e:
Log.error("Error : {}".format(e))
try:
self.calculate_gsk_layout_compliance_position(
kpi_details=gsk_layout_compliance_position)
except Exception as e:
Log.error("Error : {}".format(e))
def calculate_gsk_layout_compliance_block(self, kpi_details):
Log.info(
"Calculating {kpi} for session: {sess} and scene: {scene}".format(
kpi=kpi_details.iloc[0][KPI_TYPE_COL],
sess=self.session_uid,
scene=self.current_scene_fk,
))
include_empty, include_other = self.parse_include_config_from_setup(
kpi_details)
block_targets = self.targets[self.targets['kpi_fk'] ==
kpi_details['pk'].iloc[0]]
# if no targets return
if block_targets.empty:
Log.warning('There is no target policy for calculating {}'.format(
kpi_details.iloc[0][KPI_TYPE_COL]))
return False
else:
for idx, each_target in block_targets.iterrows():
# check for banner and template match in target
target_banner_name = self.custom_entity_data[
self.custom_entity_data['pk'] ==
each_target.store_banner_pk].name.iloc[0]
if self.templates.iloc[0].template_fk not in self.ensure_as_list(each_target.template_fks) or \
target_banner_name != self.store_banner_name:
Log.info(
"""Session: {sess}; Scene:{scene}. Scene Type not matching [{k} not in {v}]
or banner of current store -> {store_b} != target banner -> {targ_b}
target for calculating {kpi}.""".format(
sess=self.session_uid,
scene=self.current_scene_fk,
kpi=kpi_details.iloc[0][KPI_TYPE_COL],
store_b=self.store_banner_name,
targ_b=target_banner_name,
k=self.templates.iloc[0].template_fk,
v=each_target.template_fks,
))
continue
else:
result = score = 0
total_facings_count = biggest_block_facings_count = 0
block_threshold_perc = each_target.block_threshold_perc
super_brand_pk = each_target.super_brand_pk
store_banner_pk = each_target.store_banner_pk
super_brand_custom_entity = self.custom_entity_data[
self.custom_entity_data['pk'] == super_brand_pk]
sub_category_pk = each_target.sub_category_fk
Log.info(
"Calculating brand blocked for super brand: {super_b} [super_id] & sub category: {scat}"
.format(
super_b=super_brand_custom_entity.name.iloc[0],
super_id=super_brand_pk,
scat=sub_category_pk,
))
stacking_include = bool(int(each_target.stacking_include))
# able to pass sub cat and super brand[?] // or get the prods and pass
population_filters = {
'sub_category_fk': [float(sub_category_pk)],
'Super Brand':
[super_brand_custom_entity.name.iloc[0]]
}
allowed_products = defaultdict(list)
if include_empty:
allowed_products['product_type'].append('Empty')
if include_other:
allowed_products['product_type'].append("Other")
allowed_products = dict(allowed_products)
location_filters = {'scene_fk': [self.current_scene_fk]}
additional_filters = {
'minimum_facing_for_block': 1,
'minimum_block_ratio': 0,
'include_stacking': stacking_include,
'check_vertical_horizontal': True,
'allowed_products_filters': allowed_products
}
block_res = self.block.network_x_block_together(
population_filters, location_filters,
additional_filters)
block_res.dropna(subset=['total_facings'], inplace=True)
block_res = block_res.query('is_block==True')
if block_res.empty:
Log.info(
"Fail: Cannot find brand blocked for super brand: {super_b} "
"[super_id] & sub category: {scat}. Save as a Fail."
.format(
super_b=super_brand_custom_entity.name.iloc[0],
super_id=super_brand_pk,
scat=sub_category_pk,
))
continue
else:
Log.info(
"Found brand blocked for super brand: {super_b} "
"[super_id] & sub category: {scat}. Check and save."
.format(
super_b=super_brand_custom_entity.name.iloc[0],
super_id=super_brand_pk,
scat=sub_category_pk,
))
biggest_cluster = block_res.sort_values(
by='block_facings', ascending=False).head(1)
biggest_block_facings_count = float(
biggest_cluster['block_facings'])
total_facings_count = float(
biggest_cluster['total_facings'])
if total_facings_count:
result = round((biggest_block_facings_count /
total_facings_count) * 100, 2)
if result >= block_threshold_perc:
score = 1
self.common.write_to_db_result(
fk=kpi_details.iloc[0].pk,
numerator_id=store_banner_pk,
denominator_id=super_brand_pk,
context_id=sub_category_pk,
numerator_result=biggest_block_facings_count,
denominator_result=total_facings_count,
target=block_threshold_perc,
result=result,
score=score,
by_scene=True,
)
def calculate_gsk_layout_compliance_sequence(self, kpi_details):
Log.info(
"Calculating {kpi} for session: {sess} and scene: {scene}".format(
kpi=kpi_details.iloc[0][KPI_TYPE_COL],
sess=self.session_uid,
scene=self.current_scene_fk,
))
sequence_targets = self.targets[self.targets['kpi_fk'] ==
kpi_details['pk'].iloc[0]]
# if no targets return
if sequence_targets.empty:
Log.warning('There is no target policy for calculating {}'.format(
kpi_details.iloc[0][KPI_TYPE_COL]))
return False
else:
for idx, each_target in sequence_targets.iterrows():
# check for template and banner match in target
target_banner_name = self.custom_entity_data[
self.custom_entity_data['pk'] ==
each_target.store_banner_pk].name.iloc[0]
if self.templates.iloc[0].template_fk not in self.ensure_as_list(each_target.template_fks) or \
target_banner_name != self.store_banner_name:
Log.info(
"""Session: {sess}; Scene:{scene}. Scene Type not matching [{k} not in {v}]
or banner of current store -> {store_b} != target banner -> {targ_b}
target for calculating {kpi}.""".format(
sess=self.session_uid,
scene=self.current_scene_fk,
kpi=kpi_details.iloc[0][KPI_TYPE_COL],
store_b=self.store_banner_name,
targ_b=target_banner_name,
k=self.templates.iloc[0].template_fk,
v=each_target.template_fks,
))
continue
store_banner_pk = each_target.store_banner_pk
brand_pk_to_check = each_target.brand_pk
sequence_brand_pks = each_target.sequence_brand_pks
condition = each_target.condition
sub_category_pk = each_target.sub_category_fk
stacking_include = bool(int(each_target.stacking_include))
stack_filtered_mpis = self.match_product_data
Log.info(
"Checking if brand: {br} is present {condition} as in sequence : {seq}."
.format(br=brand_pk_to_check,
condition=condition,
seq=sequence_brand_pks))
if brand_pk_to_check not in sequence_brand_pks:
Log.error(
""" KPI:{kpi}. Session: {sess}; Scene:{scene}. brand to check {brand} not in list {br_lst}."""
.format(sess=self.session_uid,
scene=self.current_scene_fk,
kpi=kpi_details.iloc[0][KPI_TYPE_COL],
brand=brand_pk_to_check,
br_lst=sequence_brand_pks))
continue
if not stacking_include:
# consider only stacking layer 1 products
stack_filtered_mpis = self.match_product_data[
self.match_product_data['stacking_layer'] == 1]
interested_brand_prod_data = stack_filtered_mpis[
(stack_filtered_mpis['brand_fk'] == brand_pk_to_check)
& (stack_filtered_mpis['sub_category_fk'] ==
sub_category_pk)]
if interested_brand_prod_data.empty:
Log.error(
""" KPI:{kpi}. Session: {sess}; Scene:{scene}. brand to check {brand} is not present."""
.format(
sess=self.session_uid,
scene=self.current_scene_fk,
kpi=kpi_details.iloc[0][KPI_TYPE_COL],
brand=brand_pk_to_check,
))
continue
# Check only in predecessor brands
predecessor_brands = sequence_brand_pks[:sequence_brand_pks.
index(brand_pk_to_check
)]
predecessor_brand_shelf_sorted = stack_filtered_mpis[
(stack_filtered_mpis['brand_fk'].isin(predecessor_brands))
& (stack_filtered_mpis['sub_category_fk'] ==
sub_category_pk)][['brand_fk', 'shelf_number'
]].sort_values(['shelf_number'])
result = 0 # initialize as fail
if predecessor_brand_shelf_sorted.empty:
# predecessor brands are not present; PASS
Log.info(
"No Predecessor Brands {} are present. PASS".format(
predecessor_brands))
result = 1
else:
Log.info(
"Starting to checking> brand: {br} is present below or same level as in sequence : {seq}."
.format(br=brand_pk_to_check, seq=sequence_brand_pks))
min_shelf_of_brand = stack_filtered_mpis[
(stack_filtered_mpis['brand_fk'] == brand_pk_to_check)
& (stack_filtered_mpis['sub_category_fk'] ==
sub_category_pk)]['shelf_number'].min()
idx_brand_start_check = sequence_brand_pks.index(
brand_pk_to_check) - 1
while idx_brand_start_check >= 0:
predecessor_brand_to_check = sequence_brand_pks[
idx_brand_start_check]
Log.info(
"Checking if predecessor brand: {} is present.".
format(predecessor_brand_to_check))
if predecessor_brand_shelf_sorted[
predecessor_brand_shelf_sorted['brand_fk'] ==
predecessor_brand_to_check].empty:
# This is the logic to reduce index and continue while loop
Log.info(
"Brand {} is not present. Check next brand in predecessor"
.format(predecessor_brand_to_check))
result = 1 # PASS unless proved otherwise.
idx_brand_start_check -= 1
continue
# check for `predecessor_brand_to_check` present below the level of every brand_pk_to_check
Log.info(
"Check if Brand to check {br_c} is present below the level of brand in sequence {seq}"
.format(br_c=brand_pk_to_check,
seq=predecessor_brand_to_check))
max_shelf_of_predecessor_brand = predecessor_brand_shelf_sorted[
predecessor_brand_shelf_sorted['brand_fk'] ==
predecessor_brand_to_check]['shelf_number'].max()
if min_shelf_of_brand >= max_shelf_of_predecessor_brand:
Log.info(
"PASS: brand: {brand} is below or same level as brand: {predecessor}."
.format(
brand=brand_pk_to_check,
predecessor=predecessor_brand_to_check))
result = 1
break
else:
Log.info(
"FAIL: brand: {brand} is above level of brand: {predecessor}."
.format(
brand=brand_pk_to_check,
predecessor=predecessor_brand_to_check))
result = 0
break
# end of check
self.common.write_to_db_result(
fk=kpi_details.iloc[0].pk,
numerator_id=store_banner_pk,
denominator_id=brand_pk_to_check,
context_id=sub_category_pk,
result=result,
numerator_result=result,
denominator_result=1,
score=result,
target=1,
by_scene=True,
)
def calculate_gsk_layout_compliance_super_brand_fsos(self, kpi_details):
Log.info(
"Calculating {kpi} for session: {sess} and scene: {scene}".format(
kpi=kpi_details.iloc[0][KPI_TYPE_COL],
sess=self.session_uid,
scene=self.current_scene_fk,
))
brand_fsos_targets = self.targets[self.targets['kpi_fk'] ==
kpi_details['pk'].iloc[0]]
# if no targets return
if brand_fsos_targets.empty:
Log.warning('There is no target policy for calculating {}'.format(
kpi_details.iloc[0][KPI_TYPE_COL]))
return False
else:
for idx, each_target in brand_fsos_targets.iterrows():
# check for template and banner match in target
target_banner_name = self.custom_entity_data[
self.custom_entity_data['pk'] ==
each_target.store_banner_pk].name.iloc[0]
if self.templates.iloc[0].template_fk not in self.ensure_as_list(each_target.template_fks) or \
target_banner_name != self.store_banner_name:
Log.info(
"""Session: {sess}; Scene:{scene}. Scene Type not matching [{k} not in {v}]
or banner of current store -> {store_b} != target banner -> {targ_b}
target for calculating {kpi}.""".format(
sess=self.session_uid,
scene=self.current_scene_fk,
kpi=kpi_details.iloc[0][KPI_TYPE_COL],
store_b=self.store_banner_name,
targ_b=target_banner_name,
k=self.templates.iloc[0].template_fk,
v=each_target.template_fks,
))
continue
numerator = result = score = 0
store_banner_pk = each_target.store_banner_pk
super_brand_pk = each_target.super_brand_pk
brand_pk = each_target.brand_pk
sub_category_pk = each_target.sub_category_fk
stacking_include = bool(int(each_target.stacking_include))
facings_field = 'facings'
if not stacking_include:
# consider only stacking layer 1 products
facings_field = 'facings_ign_stack'
scif_with_products = self.scif.merge(self.products,
on='product_fk',
how='left',
suffixes=('', '_prod'))
super_brand_custom_entity = self.custom_entity_data[
self.custom_entity_data['pk'] == super_brand_pk]
if super_brand_custom_entity.empty:
# should never happen
Log.error(
'Super Brand not found. Custom Entity Not loaded with a recent template update.'
)
continue
denominator = scif_with_products[(
scif_with_products['sub_category_fk'] == sub_category_pk
)][facings_field].sum()
if denominator:
super_brand_name = super_brand_custom_entity.name.iloc[0]
numerator = scif_with_products[
(scif_with_products['Super Brand'] == super_brand_name)
& (scif_with_products['sub_category_fk'] ==
sub_category_pk)][facings_field].sum()
result = round((numerator / float(denominator)) * 100, 2)
else:
Log.info(
"{kpi}: No products with sub cat: {scat} found. Save zero."
.format(kpi=kpi_details.iloc[0][KPI_TYPE_COL],
scat=sub_category_pk))
continue
if result >= each_target.threshold:
score = 1
self.common.write_to_db_result(
fk=kpi_details.iloc[0].pk,
numerator_id=store_banner_pk,
denominator_id=super_brand_pk,
context_id=sub_category_pk,
numerator_result=numerator,
denominator_result=denominator,
result=result,
score=score,
target=each_target.threshold,
by_scene=True,
)
def calculate_gsk_layout_compliance_position(self, kpi_details):
Log.info(
"Calculating {kpi} for session: {sess} and scene: {scene}".format(
kpi=kpi_details.iloc[0][KPI_TYPE_COL],
sess=self.session_uid,
scene=self.current_scene_fk,
))
position_targets = self.targets[self.targets['kpi_fk'] ==
kpi_details['pk'].iloc[0]]
def _get_shelf_range(sh):
"""Input => string ~ '1_2_shelf
Output => xrange ~ xrange(1,3)
'"""
split_sh = sh.split('_')[:2]
int_sh = map(int, split_sh)
int_sh[1] = int_sh[1] + 1
return xrange(*int_sh)
# if no targets return
if position_targets.empty:
Log.warning('There is no target policy for calculating {}'.format(
kpi_details.iloc[0][KPI_TYPE_COL]))
return False
else:
# target_shelf_config_keys => '1_5_shelf, 6_7_shelf, above_12_shelf etc'
for idx, each_target in position_targets.iterrows():
result = score = 0
target_shelf_config_keys = each_target[each_target.keys().map(
lambda x: x.endswith('_shelf'))]
store_banner_pk = each_target.store_banner_pk
sub_category_pk = each_target.sub_category_fk
brand_pk = each_target.brand_pk
stacking_include = bool(int(each_target.stacking_include))
# numerator - Cumulative no of Facings "of the brand and sub category" available at desired shelf
numerator = 0 # number of facings available in desired shelf
# check for banner and template match in target
target_banner_name = self.custom_entity_data[
self.custom_entity_data['pk'] ==
each_target.store_banner_pk].name.iloc[0]
if self.templates.iloc[0].template_fk not in self.ensure_as_list(each_target.template_fks) or \
target_banner_name != self.store_banner_name:
Log.info(
"""Session: {sess}; Scene:{scene}. Scene Type not matching [{k} not in {v}]
or banner of current store -> {store_b} != target banner -> {targ_b}
target for calculating {kpi}.""".
format(
sess=self.session_uid,
scene=self.current_scene_fk,
kpi=kpi_details.iloc[0][KPI_TYPE_COL],
store_b=self.store_banner_name,
targ_b=target_banner_name,
k=self.templates.iloc[0].template_fk,
v=each_target.template_fks,
))
continue
stack_filtered_mpis = self.match_product_data
if not stacking_include:
# consider only stacking layer 1 products
stack_filtered_mpis = self.match_product_data[
self.match_product_data['stacking_layer'] == 1]
# denominator - total number of facings "of the brand and sub category" available in whole scene
denominator = len(stack_filtered_mpis[
(stack_filtered_mpis['brand_fk'] == brand_pk)
& (stack_filtered_mpis['sub_category_fk'] ==
sub_category_pk)])
if denominator:
for bay_number, grouped_bay_data in stack_filtered_mpis.groupby(
'bay_number'):
Log.info("Running {kpi} for bay {bay}".format(
kpi=kpi_details.iloc[0][KPI_TYPE_COL],
bay=bay_number))
# min_shelf = self.match_product_data['shelf_number'].unique().min()
max_shelf = grouped_bay_data['shelf_number'].unique(
).max()
shelf_config_key = None
for each_shelf_conf in target_shelf_config_keys.keys():
if each_shelf_conf.startswith('above'):
above_shelf = int(
each_shelf_conf.split('_')[1])
if max_shelf > above_shelf:
# if strictly greater ~ above_12_shelf
# satisfies when shelf is 13 and above
shelf_config_key = each_shelf_conf
break
elif max_shelf in _get_shelf_range(
each_shelf_conf):
shelf_config_key = each_shelf_conf
break
if not shelf_config_key:
Log.error(
""" Session: {sess}; Scene:{scene}.
There is no shelf policy for calculating {kpi}."""
.format(sess=self.session_uid,
scene=self.current_scene_fk,
kpi=kpi_details.iloc[0][KPI_TYPE_COL]))
return False
# find the brand products in the shelf_config_key
interested_shelves = each_target[shelf_config_key]
Log.info(
"Using {shelf_config} => shelves to check {shelves} for bay: {bay}"
.format(shelf_config=shelf_config_key,
shelves=interested_shelves,
bay=bay_number))
per_bay_numerator = len(grouped_bay_data[
(grouped_bay_data['shelf_number'].isin(
interested_shelves))
& (grouped_bay_data['brand_fk'] == brand_pk) &
(grouped_bay_data['sub_category_fk']
== sub_category_pk)])
numerator = numerator + per_bay_numerator
if denominator:
result = round((numerator / float(denominator)) * 100,
2)
if result >= each_target.target_perc:
score = 1
else:
Log.info(
"{kpi}: No products with sub cat: {scat} brand: {brand} found. Save zero."
.format(
kpi=kpi_details.iloc[0][KPI_TYPE_COL],
scat=sub_category_pk,
brand=brand_pk,
))
continue
self.common.write_to_db_result(
fk=kpi_details.iloc[0].pk,
numerator_id=store_banner_pk,
denominator_id=brand_pk,
context_id=sub_category_pk,
numerator_result=numerator,
denominator_result=denominator,
result=result,
score=score,
target=each_target.target_perc,
by_scene=True,
)
def calculate_gsk_layout_compliance_brand_fsos(self, kpi_details):
Log.info(
"Calculating {kpi} for session: {sess} and scene: {scene}".format(
kpi=kpi_details.iloc[0][KPI_TYPE_COL],
sess=self.session_uid,
scene=self.current_scene_fk,
))
brand_fsos_targets = self.targets[self.targets['kpi_fk'] ==
kpi_details['pk'].iloc[0]]
# if no targets return
if brand_fsos_targets.empty:
Log.warning('There is no target policy for calculating {}'.format(
kpi_details.iloc[0][KPI_TYPE_COL]))
return False
else:
for idx, each_target in brand_fsos_targets.iterrows():
# check for banner and template match in target
target_banner_name = self.custom_entity_data[
self.custom_entity_data['pk'] ==
each_target.store_banner_pk].name.iloc[0]
if self.templates.iloc[0].template_fk not in self.ensure_as_list(each_target.template_fks) or \
target_banner_name != self.store_banner_name:
Log.info(
"""Session: {sess}; Scene:{scene}. Scene Type not matching [{k} not in {v}]
or banner of current store -> {store_b} != target banner -> {targ_b}
target for calculating {kpi}.""".
format(
sess=self.session_uid,
scene=self.current_scene_fk,
kpi=kpi_details.iloc[0][KPI_TYPE_COL],
store_b=self.store_banner_name,
targ_b=target_banner_name,
k=self.templates.iloc[0].template_fk,
v=each_target.template_fks,
))
continue
numerator = result = score = 0
store_banner_pk = each_target.store_banner_pk
super_brand_pk = each_target.super_brand_pk
brand_pk = each_target.brand_pk
sub_category_pk = each_target.sub_category_fk
stacking_include = bool(int(each_target.stacking_include))
facings_field = 'facings'
if not stacking_include:
# consider only stacking layer 1 products
facings_field = 'facings_ign_stack'
super_brand_custom_entity = self.custom_entity_data[
self.custom_entity_data['pk'] == super_brand_pk]
if super_brand_custom_entity.empty:
# should never happen
Log.error(
'Super Brand not found. Custom Entity Not loaded with a recent template update.'
)
continue
super_brand_name = super_brand_custom_entity.name.iloc[0]
scif_with_products = self.scif.merge(self.products,
on='product_fk',
how='left',
suffixes=('', '_prod'))
denominator = scif_with_products[
(scif_with_products['Super Brand'] == super_brand_name)
& (scif_with_products['sub_category_fk'] == sub_category_pk
)][facings_field].sum()
numerator = self.scif[(self.scif['brand_fk'] == brand_pk) & (
self.scif['sub_category_fk'] == sub_category_pk
)][facings_field].sum()
if denominator:
result = round((numerator / float(denominator)) * 100, 2)
else:
if numerator == 0:
Log.info(
"{kpi}: No products with sub cat: {scat} brand: {brand} found. Save zero."
.format(
kpi=kpi_details.iloc[0][KPI_TYPE_COL],
scat=sub_category_pk,
brand=brand_pk,
))
continue
if result >= each_target.threshold:
score = 1
self.common.write_to_db_result(
fk=kpi_details.iloc[0].pk,
numerator_id=store_banner_pk,
denominator_id=brand_pk,
context_id=sub_category_pk,
numerator_result=numerator,
denominator_result=denominator,
result=result,
score=score,
target=each_target.threshold,
by_scene=True,
)
class ToolBox(GlobalSessionToolBox):
def __init__(self, data_provider, output):
GlobalSessionToolBox.__init__(self, data_provider, output)
self.adjacency = Adjancency(data_provider)
self.block = Block(data_provider)
self.kpi_static_data = self.common.get_kpi_static_data()
self.ps_data_provider = PsDataProvider(data_provider)
self._scene_types = None
self.external_targets = self.ps_data_provider.get_kpi_external_targets(
)
@property
def scene_types(self):
if not self._scene_types:
self._scene_types = self.scif['template_fk'].unique().tolist()
return self._scene_types
def main_calculation(self):
custom_kpis = self.kpi_static_data[
(self.kpi_static_data['kpi_calculation_stage_fk'] == 3)
& (self.kpi_static_data['valid_from'] <= self.visit_date) &
((self.kpi_static_data['valid_until']).isnull() |
(self.kpi_static_data['valid_until'] >= self.visit_date))]
for kpi in custom_kpis.itertuples():
kpi_function = self.get_kpi_function_by_family_fk(
kpi.kpi_family_fk)
kpi_function(kpi.pk)
return
@run_for_every_scene_type
def calculate_presence(self, kpi_fk, template_fk=None):
config = self.get_external_target_data_by_kpi_fk(kpi_fk)
if config.empty or (template_fk is None):
return
result_df = self.scif[
self.scif[config.numerator_param].isin(config.numerator_value)
& (self.scif['template_fk'] == template_fk)]
numerator_id = self.get_brand_fk_from_brand_name(
config.numerator_value[0])
result = 0 if result_df.empty else 1
self.write_to_db(kpi_fk,
numerator_id=numerator_id,
denominator_id=template_fk,
result=result)
return
@run_for_every_scene_type
def calculate_shelf_location(self, kpi_fk, template_fk=None):
config = self.get_external_target_data_by_kpi_fk(kpi_fk)
shelf_location = config.shelf_location
if config.empty or (template_fk is None):
return
relevant_scene_fks = self.scif[
self.scif['template_fk'] ==
template_fk]['scene_fk'].unique().tolist()
relevant_matches = self.matches[self.matches['scene_fk'].isin(
relevant_scene_fks)]
shelves = relevant_matches.groupby(
'bay_number',
as_index=False)['shelf_number'].max()['shelf_number'].mean()
products_df = self.scif[
(self.scif[config.numerator_param].isin(config.numerator_value))
& (self.scif['template_fk'] == template_fk)]
products_list = products_df['product_fk'].unique().tolist()
if shelf_location == 'top':
shelf_matches = relevant_matches[
(relevant_matches['product_fk'].isin(products_list))
& (relevant_matches['shelf_number'] <= (shelves / 3))]
elif shelf_location == 'middle_bottom':
shelf_matches = relevant_matches[
(relevant_matches['product_fk'].isin(products_list))
& (relevant_matches['shelf_number'] > (shelves / 3))]
else:
shelf_matches = pd.DataFrame()
numerator_id = self.get_brand_fk_from_brand_name(
config.numerator_value[0])
result = 0 if shelf_matches.empty else 1
self.write_to_db(kpi_fk,
numerator_id=numerator_id,
denominator_id=template_fk,
result=result)
@run_for_every_scene_type
def calculate_blocking(self, kpi_fk, template_fk=None):
config = self.get_external_target_data_by_kpi_fk(kpi_fk)
if config.empty or (template_fk is None):
return
location = {'template_fk': template_fk}
blocks = self.block.network_x_block_together(
{config.numerator_param: config.numerator_value},
location,
additional={'check_vertical_horizontal': True})
if not blocks.empty:
blocks = blocks[blocks['is_block']]
orientation = config.orientation
if orientation and orientation is not pd.np.nan:
blocks = blocks[blocks['orientation'] == orientation]
numerator_id = self.get_brand_fk_from_brand_name(
config.numerator_value[0])
result = 0 if blocks.empty else 1
self.write_to_db(kpi_fk,
numerator_id=numerator_id,
denominator_id=template_fk,
result=result)
@run_for_every_scene_type
def calculate_adjacency(self, kpi_fk, template_fk=None):
config = self.get_external_target_data_by_kpi_fk(kpi_fk)
if config.empty or (template_fk is None):
return
location = {'template_fk': template_fk}
anchor_pks = \
self.scif[self.scif[config.anchor_param].isin(config.anchor_value)]['product_fk'].unique().tolist()
tested_pks = \
self.scif[self.scif[config.tested_param].isin(config.tested_value)]['product_fk'].unique().tolist()
# handle populations that are not mutually exclusive
tested_pks = [x for x in tested_pks if x not in anchor_pks]
population = {
'anchor_products': {
'product_fk': anchor_pks
},
'tested_products': {
'product_fk': tested_pks
}
}
# this function is only needed until the adjacency function is enhanced to not crash when an empty population
# is provided
if self.check_population_exists(population, template_fk):
try:
adj_df = self.adjacency.network_x_adjacency_calculation(
population, location, {
'minimum_facings_adjacent': 1,
'minimum_block_ratio': 0,
'minimum_facing_for_block': 1,
'include_stacking': True
})
except AttributeError:
Log.info(
"Error calculating adjacency for kpi_fk {} template_fk {}".
format(kpi_fk, template_fk))
return
if adj_df.empty:
result = 0
else:
result = 1 if not adj_df[adj_df['is_adj']].empty else 0
else:
result = 0
numerator_id = self.get_brand_fk_from_brand_name(
config.anchor_value[0])
self.write_to_db(kpi_fk,
numerator_id=numerator_id,
denominator_id=template_fk,
result=result)
return
@run_for_every_scene_type
def calculate_brand_facings(self, kpi_fk, template_fk=None):
relevant_scif = self.scif[self.scif['template_fk'] == template_fk]
denominator_results = relevant_scif.groupby(
'Customer Category', as_index=False)[[
'facings'
]].sum().rename(columns={'facings': 'denominator_result'})
numerator_result = relevant_scif.groupby(
['brand_fk', 'Customer Category'], as_index=False)[[
'facings'
]].sum().rename(columns={'facings': 'numerator_result'})
results = numerator_result.merge(denominator_results)
results['result'] = (results['numerator_result'] /
results['denominator_result'])
results['result'].fillna(0, inplace=True)
for index, row in results.iterrows():
relevant_perfetti_product_fk = self.get_product_fk_from_perfetti_category(
row['Customer Category'])
self.write_to_db(fk=kpi_fk,
numerator_id=row['brand_fk'],
denominator_id=relevant_perfetti_product_fk,
numerator_result=row['numerator_result'],
denominator_result=row['denominator_result'],
context_id=template_fk,
result=row['result'],
score=row['result'])
def get_kpi_function_by_family_fk(self, kpi_family_fk):
if kpi_family_fk == 19:
return self.calculate_presence
elif kpi_family_fk == 20:
return self.calculate_adjacency
elif kpi_family_fk == 21:
return self.calculate_blocking
elif kpi_family_fk == 22:
return self.calculate_shelf_location
elif kpi_family_fk == 23:
return self.calculate_brand_facings
def get_external_target_data_by_kpi_fk(self, kpi_fk):
return self.external_targets[self.external_targets['kpi_fk'] ==
kpi_fk].iloc[0]
def get_brand_fk_from_brand_name(self, brand_name):
return self.all_products[self.all_products['brand_name'] ==
brand_name]['brand_fk'].iloc[0]
def get_product_fk_from_perfetti_category(self, perfetti_category):
try:
return self.all_products[self.all_products['Customer Category'] ==
perfetti_category]['product_fk'].iloc[0]
except IndexError:
return None
def check_population_exists(self, population, template_fk):
relevant_scif = self.scif[self.scif['template_fk'] == template_fk]
anchor_scif = relevant_scif[relevant_scif['product_fk'].isin(
population['anchor_products']['product_fk'])]
tested_scif = relevant_scif[relevant_scif['product_fk'].isin(
population['tested_products']['product_fk'])]
if anchor_scif.empty or tested_scif.empty:
return False
else:
return True
class ToolBox(GlobalSessionToolBox):
def __init__(self, data_provider, output):
GlobalSessionToolBox.__init__(self, data_provider, output)
self.templates = {}
self.parse_template()
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.match_scene_item_facts = pd.merge(
self.scif,
self.match_product_in_scene,
how='right',
left_on=['item_id', 'scene_id'],
right_on=['product_fk',
'scene_fk']) # Merges scif with mpis on product_fk
self.block = Block(data_provider)
self.own_manufacturer_fk = int(
self.data_provider.own_manufacturer.param_value.values[0])
self.results_df = pd.DataFrame(columns=[
'kpi_name', 'kpi_fk', 'numerator_id', 'numerator_result',
'context_id', 'denominator_id', 'denominator_result', 'result',
'score'
])
def parse_template(self):
for sheet in SHEETS:
self.templates[sheet] = pd.read_excel(TEMPLATE_PATH,
sheet_name=sheet)
for shelf_postion_sheet in SHELF_POSITION_SHEET:
self.templates[shelf_postion_sheet] = pd.read_excel(
SHELF_POSITION_PATH,
sheet_name=shelf_postion_sheet,
index_col=0)
def main_calculation(self):
relevant_kpi_template = self.templates[Consts.KPIS]
# Consts.SOS, Consts.DISTRIBUTION, Consts.ADJACENCY_BRAND_WITHIN_BAY,
# Consts.ADJACENCY_CATEGORY_WITHIN_BAY, Consts.SHELF_POSITION, Consts.LEAD_ANCHOR_BY_BAY
foundation_kpi_types = [
Consts.SOS, Consts.DISTRIBUTION, Consts.ADJACENCY_BRAND_WITHIN_BAY,
Consts.ADJACENCY_CATEGORY_WITHIN_BAY, Consts.SHELF_POSITION,
Consts.LEAD_ANCHOR_BY_BAY
]
foundation_kpi_template = relevant_kpi_template[relevant_kpi_template[
Consts.KPI_TYPE].isin(foundation_kpi_types)]
self._calculate_kpis_from_template(foundation_kpi_template)
self.save_results_to_db()
return
def save_results_to_db(self):
self.results_df.drop(columns=['kpi_name'], inplace=True)
self.results_df.rename(columns={'kpi_fk': 'fk'}, inplace=True)
self.results_df[['result']].fillna(0, inplace=True)
# self.results_df.fillna(None, inplace=True)
results = self.results_df.to_dict('records')
for result in results:
# try:
# if result['denominator_result'] == 0 and result['numerator_result']:
# del result['denominator_result']
# del result['numerator_result']
result = simplejson.loads(simplejson.dumps(result,
ignore_nan=True))
self.write_to_db(**result)
# except:
# a = 1
def _calculate_kpis_from_template(self, template_df):
for i, row in template_df.iterrows():
calculation_function = self._get_calculation_function_by_kpi_type(
row[Consts.KPI_TYPE])
try:
kpi_rows = self.templates[row[Consts.KPI_TYPE]][self.templates[
row[Consts.KPI_TYPE]][Consts.KPI_NAME].str.encode(
'utf-8') == row[Consts.KPI_NAME].encode('utf-8')]
except IndexError:
pass
for index, kpi_row in kpi_rows.iterrows():
result_data = calculation_function(kpi_row)
if result_data:
for result in result_data:
# if result['result'] <= 1:
# result['result'] = result['result'] * 100
self.results_df.loc[len(self.results_df),
result.keys()] = result
def _get_calculation_function_by_kpi_type(self, kpi_type):
if kpi_type == Consts.SOS:
return self.calculate_sos
elif kpi_type == Consts.DISTRIBUTION:
return self.calculate_distribution
elif kpi_type == Consts.ADJACENCY_BRAND_WITHIN_BAY:
return self.calculate_adjacency_brand
elif kpi_type == Consts.ADJACENCY_CATEGORY_WITHIN_BAY:
return self.calculate_adjacency_category
elif kpi_type == Consts.SHELF_POSITION:
return self.calculate_shelf_position
elif kpi_type == Consts.LEAD_ANCHOR_BY_BAY:
return self.calculate_lead_anchor
def calculate_adjacency_brand(self, row):
kpi_name = row[Consts.KPI_NAME]
kpi_fk = self.get_kpi_fk_by_kpi_type(kpi_name)
relevant_brands = self._sanitize_values(
row[Consts.BRAND_NAME]) # Gets the brand name from the template
relevant_brand_fk_of_the_relevant_brand = \
self.all_products.loc[self.all_products.brand_name.isin(relevant_brands), 'brand_fk'].iat[
0] # used to save as the denominator id
direction = {'UP': 0, 'DOWN': 2, 'RIGHT': 1, 'LEFT': 3}
result_dict_list = []
relevant_match_scene_item_facts = self._filter_df(
self.match_scene_item_facts, {Consts.BRAND_NAME: relevant_brands}
) # Filter the merged data frame with the brand to get the relevant dataframe
if not relevant_match_scene_item_facts.empty:
for relevant_scene in set(
relevant_match_scene_item_facts.scene_id
): # Iterating through the unique scenes existin the merged dataframe
score = relevant_scene
mcif = relevant_match_scene_item_facts[
relevant_match_scene_item_facts.scene_id.isin(
[relevant_scene])]
unique_bay_numbers = set(
mcif.bay_number
) # Getting the unique bay numbers in the the scene
location = {Consts.SCENE_FK: relevant_scene}
for bay in unique_bay_numbers:
context_id = bay # bay number
# Consts.BRAND_FK: relevant_brands,, Consts.BAY_NUMBER:bay
# relevant_mpis = self.match_product_in_scene[self.match_product_in_scene.bay_number.isin([bay])]
relevant_filters = {
Consts.BRAND_NAME: relevant_brands,
Consts.BAY_NUMBER: [bay]
}
# block = self.block.network_x_block_together(relevant_filters,
# location=location_filters,
# additional={'minimum_facing_for_block': 1,
# 'use_masking_only': True})
block = self.block.network_x_block_together(
relevant_filters,
location=location,
additional={
'allowed_edge_type': ['encapsulated'],
'calculate_all_scenes': True,
'minimum_facing_for_block': 1,
'use_masking_only': True,
'allowed_products_filters': {
'product_type': 'Empty'
},
})
passed_block = block[block.is_block == True]
if not passed_block.empty:
passed_block = passed_block.iloc[
passed_block.block_facings.astype(int).idxmax(
)] # logic to get the block with the largest number of facings in the block
valid_cluster_for_block = passed_block.cluster.nodes.values(
)
relevant_scene_match_fks_for_block = [
scene_match_fk for item in valid_cluster_for_block
for scene_match_fk in item['scene_match_fk']
]
relevant_probe_match_fks_for_block = [
probe_match_fk for item in valid_cluster_for_block
for probe_match_fk in item['probe_match_fk']
]
# {1: 'block_relevant_brand', 2: 'adjacency_relevant_brand', 3:'block_relecant_category',4:'adjacency_relevant_category'}
self._save_in_match_product_in_probe_state_reporting_for_explorer_filter(
relevant_probe_match_fks_for_block, 1)
'''The below line is used to filter the adjacency graph by the closest edges. Specifically since
the edges are determined by masking only, there's a chance that there will be two edges that come
out from a single node.'''
adj_graph = self.block.adj_graphs_by_scene.values(
)[0].edge_subgraph(
self._filter_redundant_edges(
self.block.adj_graphs_by_scene.values()[0]))
adj_items = {
} # will contain the scene match fks that are adjacent to the block. key: scene_match_fk | values:[direction, brand_fk]
for match in relevant_scene_match_fks_for_block:
for node, node_data in adj_graph.adj[match].items(
):
if node not in relevant_scene_match_fks_for_block:
product_fk = self.match_scene_item_facts.loc[
self.match_scene_item_facts.
scene_match_fk == node,
'item_id'].iat[0]
if product_fk == 0: # general empty
# we need visibility in to seeing if the product is empty
# since we are saving the brand. If the product is empty, then it will save
# as General. That is why we are saving brand fk 3431 which is brand: empty
important_brand = 3431
elif product_fk == 23211: # irrelevant
# we need visibility in to seeing if the product is irrelevant
# since we are saving the brand. If the product is irrelevant, then it will save
# as General. That is why we are saving brand fk 3432 which is brand: irrelevant
important_brand = 3432
else:
important_brand = self.match_scene_item_facts.loc[
self.match_scene_item_facts.
scene_match_fk == node,
'brand_fk'].iat[0]
adj_items[node] = [
node_data, important_brand
]
brand_fks_for_adj_items = np.array(
[nd[1] for nd in adj_items.values()])
node_direction = np.array(
[nd[0]['direction'] for nd in adj_items.values()])
relevant_probe_match_fks_for_adjacent_products = self.match_product_in_scene[
self.match_product_in_scene.scene_match_fk.isin(
adj_items.keys())].probe_match_fk.values
# {1: 'block_relevant_brand', 2: 'adjacency_relevant_brand', 3:'block_relecant_category',4:'adjacency_relevant_category'}
self._save_in_match_product_in_probe_state_reporting_for_explorer_filter(
relevant_probe_match_fks_for_adjacent_products, 2)
for dir, dir_fk in direction.items():
if dir in node_direction:
index_of_revant_brand_fk = np.where(
node_direction == dir)
relevant_brand_fks_for_adj_items = brand_fks_for_adj_items[
index_of_revant_brand_fk]
values, counts = np.unique(
relevant_brand_fks_for_adj_items,
return_counts=True)
for j in range(len(values)):
denominator_id = relevant_brand_fk_of_the_relevant_brand # brand fk of the relevant brand
numerator_id = values[
j] # brand_fk of the adjacency product
numerator_result = dir_fk # the direction of the adjacency
result = counts[j]
result_dict = {
'kpi_fk': kpi_fk,
'numerator_id': numerator_id,
'denominator_id': denominator_id,
'context_id': context_id,
'numerator_result': numerator_result,
'result': result,
'score': score
}
result_dict_list.append(result_dict)
return result_dict_list
def calculate_adjacency_category(self, row):
kpi_name = row[Consts.KPI_NAME]
kpi_fk = self.get_kpi_fk_by_kpi_type(kpi_name)
relevant_brands = self._sanitize_values(
row[Consts.BRAND_NAME]) # Gets the brand name from the template
relevant_brand_fk_of_the_relevant_brand = \
self.all_products.loc[self.all_products.brand_name.isin(relevant_brands), 'brand_fk'].iat[
0] # used to save as the denominator id
direction = {'UP': 0, 'DOWN': 2, 'RIGHT': 1, 'LEFT': 3}
result_dict_list = []
relevant_match_scene_item_facts = self._filter_df(
self.match_scene_item_facts, {Consts.BRAND_NAME: relevant_brands}
) # Filter the merged data frame with the brand to get the relevant dataframe
if not relevant_match_scene_item_facts.empty:
for relevant_scene in set(
relevant_match_scene_item_facts.scene_id
): # Iterating through the unique scenes in the merged dataframe
mcif = relevant_match_scene_item_facts[
relevant_match_scene_item_facts.scene_id.isin(
[relevant_scene])]
unique_bay_numbers = set(
mcif.bay_number
) # Getting the unique bay numbers in the the scene
location = {Consts.SCENE_FK: relevant_scene}
for bay in unique_bay_numbers:
# Consts.BRAND_FK: relevant_brands,, Consts.BAY_NUMBER:bay
# relevant_mpis = self.match_product_in_scene[self.match_product_in_scene.bay_number.isin([bay])]
relevant_filters = {
Consts.BRAND_NAME: relevant_brands,
Consts.BAY_NUMBER: [bay]
}
# block = self.block.network_x_block_together(relevant_filters,
# location=location_filters,
# additional={'minimum_facing_for_block': 1,
# 'use_masking_only': True})
block = self.block.network_x_block_together(
relevant_filters,
location=location,
additional={
'allowed_edge_type': ['encapsulated'],
'calculate_all_scenes': True,
'minimum_facing_for_block': 1,
'use_masking_only': True,
'allowed_products_filters': {
'product_type': 'Empty'
},
})
passed_block = block[block.is_block == True]
if not passed_block.empty:
passed_block = passed_block.iloc[
passed_block.block_facings.astype(int).idxmax(
)] # logic to get the block with the largest number of facings in the block
valid_cluster_for_block = passed_block.cluster.nodes.values(
)
relevant_scene_match_fks_for_block = [
scene_match_fk for item in valid_cluster_for_block
for scene_match_fk in item['scene_match_fk']
]
relevant_probe_match_fks_for_block = [
probe_match_fk for item in valid_cluster_for_block
for probe_match_fk in item['probe_match_fk']
]
# {1: 'block_relevant_brand', 2: 'adjacency_relevant_brand', 3:'block_relecant_category',4:'adjacency_relevant_category'}
self._save_in_match_product_in_probe_state_reporting_for_explorer_filter(
relevant_probe_match_fks_for_block, 3)
'''The below line is used to filter the adjacency graph by the closest edges. Specifically since
the edges are determined by masking only, there's a chance that there will be two edges that come
out from a single node.'''
adj_graph = self.block.adj_graphs_by_scene.values(
)[0].edge_subgraph(
self._filter_redundant_edges(
self.block.adj_graphs_by_scene.values()[0]))
adj_items = {
} # will contain the scene match fks that are adjacent to the block
for match in relevant_scene_match_fks_for_block:
for node, node_data in adj_graph.adj[match].items(
):
if node not in relevant_scene_match_fks_for_block:
product_fk = self.match_scene_item_facts.loc[
self.match_scene_item_facts.
scene_match_fk == node,
'item_id'].iat[0]
if product_fk == 0: # general empty
# we need visibility in to seeing if the product is empty
# since we are saving the category. If the product is empty, then it will save
# as General. That is why we are saving category fk 26 which is category: empty
important_category = 26
elif product_fk == 23211: # irrelevant
# we need visibility in to seeing if the product is irrelevant
# since we are saving the category. If the product is irrelevant, then it will save
# as General. That is why we are saving brand fk 27 which is category: irrelevant
important_category = 27
else:
important_category = self.match_scene_item_facts.loc[
self.match_scene_item_facts.
scene_match_fk == node,
'category_fk'].iat[0]
adj_items[node] = [
node_data, important_category
]
category_fks_for_adj_items = np.array(
[nd[1] for nd in adj_items.values()])
node_direction = np.array(
[nd[0]['direction'] for nd in adj_items.values()])
relevant_probe_match_fks_for_adjacent_products = self.match_product_in_scene[
self.match_product_in_scene.scene_match_fk.isin(
adj_items.keys())].probe_match_fk.values
# {1: 'block_relevant_brand', 2: 'adjacency_relevant_brand', 3:'block_relecant_category',4:'adjacency_relevant_category'}
self._save_in_match_product_in_probe_state_reporting_for_explorer_filter(
relevant_probe_match_fks_for_adjacent_products, 4)
for dir, dir_fk in direction.items():
if dir in node_direction:
index_of_revant_brand_fk = np.where(
node_direction == dir)
relevant_category_fks_for_adj_items = category_fks_for_adj_items[
index_of_revant_brand_fk]
values, counts = np.unique(
relevant_category_fks_for_adj_items,
return_counts=True)
for j in range(len(values)):
numerator_id = values[
j] # brand_fk of the adjacency product
denominator_id = relevant_brand_fk_of_the_relevant_brand # brand fk of the relevant brand
numerator_result = dir_fk # the direction of the adjacency
context_id = bay # bay number
result = counts[
j] # grouped by category, count
score = relevant_scene
result_dict = {
'kpi_fk': kpi_fk,
'numerator_id': numerator_id,
'denominator_id': denominator_id,
'context_id': context_id,
'numerator_result': numerator_result,
'result': result,
'score': score
}
result_dict_list.append(result_dict)
return result_dict_list
def calculate_distribution(self, row):
kpi_name = row[Consts.KPI_NAME]
kpi_fk = self.get_kpi_fk_by_kpi_type(kpi_name)
relevant_product_fk = self._sanitize_values(row.product_list_pk)
bool_array_present_products_fk_in_session = pd.np.in1d(
relevant_product_fk,
self.scif.product_fk.unique().tolist())
present_products_fk_in_session_index = pd.np.flatnonzero(
bool_array_present_products_fk_in_session)
present_products_fk_in_session = pd.np.array(
relevant_product_fk).ravel()[present_products_fk_in_session_index]
absent_products_fk_in_session_index = pd.np.flatnonzero(
~bool_array_present_products_fk_in_session)
absent_products_fk_in_session = pd.np.array(
relevant_product_fk).ravel()[absent_products_fk_in_session_index]
result_dict_list = []
for present_product_fk in present_products_fk_in_session:
result = self.scif[self.scif.product_fk.isin([present_product_fk
])].facings.iat[0]
result_dict = {
'kpi_name': kpi_name,
'kpi_fk': kpi_fk,
'numerator_id': present_product_fk,
'denominator_id': self.store_id,
'result': result
}
result_dict_list.append(result_dict)
for absent_products_fk_in_session in absent_products_fk_in_session:
result = 0
result_dict = {
'kpi_name': kpi_name,
'kpi_fk': kpi_fk,
'numerator_id': absent_products_fk_in_session,
'denominator_id': self.store_id,
'result': result
}
result_dict_list.append(result_dict)
return result_dict_list
def calculate_sos(self, row):
kpi_name = row[Consts.KPI_NAME]
kpi_fk = self.get_kpi_fk_by_kpi_type(kpi_name)
iterate_by = self._sanitize_values(row.iterate_by)
# Have to save the sos by sku. So each sku will have its result (sos) saved
# The skus relevant are saved in the iterate(in the template)
sku_relevelant_scif = self.scif[self.scif.product_fk.isin(iterate_by)]
result_dict_list = []
for unique_product_fk in set(sku_relevelant_scif.product_fk):
# The logic for denominator result: The denominator scif is filter by category_fk.
# The tricky part is the category_fk is determined by the product_fk.
# So if the category_fk is 1 for product_fk 99. Then the denominator scif is filtered by the
# category fk 1.
denominator_relevant_scif = \
self.scif[self.scif.category_fk.isin(
self.scif.category_fk[self.scif.product_fk == unique_product_fk].to_numpy())]
# denominator_id = self.scif[self.scif]
denominator_id = denominator_relevant_scif.category_fk.iat[0]
denominator_result = denominator_relevant_scif[row[
Consts.OUTPUT]].sum(
) if not denominator_relevant_scif.empty else 1
relevant_numerator_scif = self.scif[self.scif.product_fk.isin(
[unique_product_fk])]
numerator_result = relevant_numerator_scif[row[Consts.OUTPUT]].sum(
) if not relevant_numerator_scif.empty else 0
numerator_id = relevant_numerator_scif.product_fk.iat[0]
result = (float(numerator_result) / denominator_result) * 100
result_dict = {
'kpi_name': kpi_name,
'kpi_fk': kpi_fk,
'numerator_id': numerator_id,
'denominator_id': denominator_id,
'numerator_result': numerator_result,
'denominator_result': denominator_result,
'result': result
}
result_dict_list.append(result_dict)
return result_dict_list
def calculate_shelf_position(self, row):
kpi_name = row[Consts.KPI_NAME]
kpi_fk = self.get_kpi_fk_by_kpi_type(kpi_name)
relevant_product_fks = self._sanitize_values(
row[Consts.NUMERATOR_VALUE_1])
result_dict_list = []
relevant_mcif = self.match_scene_item_facts[
self.match_scene_item_facts.item_id.isin(relevant_product_fks)]
if not relevant_mcif.empty:
shelf_position_dict = {
'Bottom': 22,
'Middle': 23,
'Eye': 24,
'Top': 25
}
for unique_scene in set(relevant_mcif.scene_id):
context_id = unique_scene
relevant_mcif_2 = relevant_mcif[relevant_mcif.scene_id.isin(
[unique_scene])]
for bay in set(relevant_mcif_2.bay_number):
relevant_mcif_3 = relevant_mcif_2[
relevant_mcif_2.bay_number.isin([bay])]
group_by_mcif = relevant_mcif_3.groupby(
['item_id', 'shelf_number']).first()
relevant_group_by_mcif = group_by_mcif.reset_index(
'shelf_number')[['shelf_number', 'facings']]
for unique_product_fk in set(relevant_group_by_mcif.index):
result = self._get_shelf_position_id(
unique_scene=unique_scene,
unique_bay=bay,
grouped_shelf=relevant_group_by_mcif[
relevant_group_by_mcif.index.isin(
[unique_product_fk])].shelf_number,
shelf_position_dict=shelf_position_dict
) # return the proper shelf position based on number the number of shelfs using the shelf position dict
# final_mcif_of_product_fk = relevant_group_by_mcif.max()
numerator_result = \
relevant_group_by_mcif.loc[relevant_group_by_mcif.index == unique_product_fk, 'facings'].values[
0]
result_dict = {
'kpi_fk': kpi_fk,
'numerator_id': unique_product_fk,
'denominator_id': bay,
'context_id': context_id,
'numerator_result': numerator_result,
'result': result
}
result_dict_list.append(result_dict)
return result_dict_list
def calculate_lead_anchor(self, row):
kpi_name = row[Consts.KPI_NAME]
kpi_fk = self.get_kpi_fk_by_kpi_type(kpi_name)
relevant_template_name = row[Consts.TEMPLATE_NAME]
result_dict_list = []
relevant_mcif = self.match_scene_item_facts[
self.match_scene_item_facts.template_name.isin(
[relevant_template_name])]
if not relevant_mcif.empty:
for relevant_scene in set(relevant_mcif.scene_id):
filtered_mcif = relevant_mcif[relevant_mcif.scene_id.isin(
[relevant_scene])]
for bay in set(filtered_mcif.bay_number):
bay_filtered_mcif = filtered_mcif[
filtered_mcif.bay_number.isin([bay])]
# this function gets the right(or left) most products in the bay
product_fks = self._get_the_most_right_or_most_left_product_fks_on_bay(
bay_filtered_mcif, relevant_template_name)
unique_product = Counter(product_fks).keys()
count_of_product_fks = Counter(product_fks).values()
# The logic of lead anchor is the the lead anchor (so the right most or the left most depending
# on the door handle position which is determined by the scene_type) is grouped by product fk and
# we want the count of facings of the product in right corner or left corner
for index in range(len(unique_product)):
numerator_id = unique_product[index] # product fk
denominator_id = bay # bay number
context_id = relevant_scene # scene fk
numerator_result = count_of_product_fks[
index] # count of facings of LEAD PRODUCTS on right or left depending on the scene type
denominator_result = sum(
count_of_product_fks
) # sum of the total facings of LEAD PRODUCTS
result = (float(numerator_result) /
denominator_result) * 100
result_dict = {
'kpi_fk': kpi_fk,
'numerator_id': numerator_id,
'denominator_id': denominator_id,
'context_id': context_id,
'numerator_result': numerator_result,
'denominator_result': denominator_result,
'result': result
}
result_dict_list.append(result_dict)
return result_dict_list
@staticmethod
def _sanitize_values(item):
if pd.isna(item):
return item
else:
items = [x.strip() for x in item.split(',')]
return items
@staticmethod
def _filter_df(df, filters, exclude=0):
for key, val in filters.items():
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 _filter_redundant_edges(self, adj_g):
"""Since the edges determines by the masking only, there's a chance that there will be two edges
that come out from a single node. This method filters the redundant ones (the ones who skip the
closet adjecent node)"""
edges_filter = []
for node_fk, node_data in adj_g.nodes(data=True):
for direction in ['UP', 'DOWN', 'LEFT', 'RIGHT']:
edges = [(edge[0], edge[1])
for edge in list(adj_g.edges(node_fk, data=True))
if edge[2]['direction'] == direction]
if len(edges) <= 1:
edges_filter.extend(edges)
else:
edges_filter.append(self._get_shortest_path(adj_g, edges))
return edges_filter
def _get_shortest_path(self, adj_g, edges_to_check):
""" This method gets a list of edge and returns the one with the minimum distance"""
distance_per_edge = {
edge: self._get_edge_distance(adj_g, edge)
for edge in edges_to_check
}
shortest_edge = min(distance_per_edge, key=distance_per_edge.get)
return shortest_edge
def _get_edge_distance(self, adj_g, edge):
"""
This method gets an edge and calculate it's length (the distance between it's nodes)
"""
first_node_coordinate = np.array(
self._get_node_display_coordinates(adj_g, edge[0]))
second_node_coordinate = np.array(
self._get_node_display_coordinates(adj_g, edge[1]))
distance = np.sqrt(
np.sum((first_node_coordinate - second_node_coordinate)**2))
return distance
def _get_node_display_coordinates(self, adj_g, node_fk):
"""
This method gets a node and extract the Display coordinates (x and y).
Those attributes were added to each node since this is the attributes we condensed the graph by
"""
return float(list(adj_g.nodes[node_fk]['rect_x'])[0]), float(
list(adj_g.nodes[node_fk]['rect_y'])[0])
@staticmethod
def _get_the_most_right_or_most_left_product_fks_on_bay(
bay_filtered_mcif, relevant_template_name):
'''
:param bay_filtered_mcif: the relevant merge dataframe of mpis and scif
:param relevant_template_name: the template name (Right door handle or Left door handle)
:return: If it is a right door handle, gets the product fks of all the right most product of each shelf.
If it is a left door handle, gets the product fk of all the left most product of each shelf.
'''
product_fks = []
for shelf in set(bay_filtered_mcif.shelf_number):
shelf_filtered_mcif = bay_filtered_mcif[
bay_filtered_mcif.shelf_number.isin([shelf])]
'''If the template name is 'Right Door Handle', then below loc will get the product fk that
is the most right product fk of the shelf. If the template name is Left Door Handle, then the
below loc will get the most left product fk of the shelf.'''
most_right_or_left_product_on_shelf = shelf_filtered_mcif.loc[
shelf_filtered_mcif.rect_x.idxmax(), 'item_id'] if relevant_template_name == 'Right Door Handle' else \
shelf_filtered_mcif.loc[shelf_filtered_mcif.rect_x.idxmin(), 'item_id']
product_fks.append(most_right_or_left_product_on_shelf)
return product_fks
def _get_shelf_position_id(self, unique_scene, unique_bay, grouped_shelf,
shelf_position_dict):
'''
:return: Uses the shelf position in the scene and bay and return the id of the shelf
'''
max_shelf_position = self.match_scene_item_facts[
(self.match_scene_item_facts.scene_id.isin([unique_scene]))
& (self.match_scene_item_facts.bay_number.isin([unique_bay])
)].shelf_number.max()
return shelf_position_dict[self.templates['Shelf Map'].loc[
max_shelf_position, grouped_shelf.iat[0]]]
def _save_in_match_product_in_probe_state_reporting_for_explorer_filter(
self, relevant_probe_match_fks,
match_product_in_probe_state_reporting_fk):
df_for_common = pd.DataFrame({
self.common.MATCH_PRODUCT_IN_PROBE_FK:
relevant_probe_match_fks,
self.common.MATCH_PRODUCT_IN_PROBE_STATE_REPORTING_FK:
match_product_in_probe_state_reporting_fk
}) # this is for block_relevant_brand
self.common.match_product_in_probe_state_values = \
self.common.match_product_in_probe_state_values.append(df_for_common)
class CBCDAIRYILToolBox:
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.project_name = self.data_provider.project_name
self.common = Common(self.data_provider)
self.old_common = oldCommon(self.data_provider)
self.rds_conn = PSProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.session_fk = self.data_provider.session_id
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.store_info = self.data_provider[Data.STORE_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.survey = Survey(self.data_provider)
self.block = Block(self.data_provider)
self.general_toolbox = GENERALToolBox(self.data_provider)
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.template_path = self.get_relevant_template()
self.gap_data = self.get_gap_data()
self.kpi_weights = parse_template(self.template_path,
Consts.KPI_WEIGHT,
lower_headers_row_index=0)
self.template_data = self.parse_template_data()
self.kpis_gaps = list()
self.passed_availability = list()
self.kpi_static_data = self.old_common.get_kpi_static_data()
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]
def get_relevant_template(self):
"""
This function returns the relevant template according to it's visit date.
Because of a change that was done in the logic there are 3 templates that match different dates.
:return: Full template path
"""
if self.visit_date <= datetime.date(datetime(2019, 12, 31)):
return "{}/{}/{}".format(
Consts.TEMPLATE_PATH, Consts.PREVIOUS_TEMPLATES,
Consts.PROJECT_TEMPLATE_NAME_UNTIL_2019_12_31)
else:
return "{}/{}".format(Consts.TEMPLATE_PATH,
Consts.CURRENT_TEMPLATE)
def get_gap_data(self):
"""
This function parse the gap data template and returns the gap priorities.
:return: A dict with the priorities according to kpi_names. E.g: {kpi_name1: 1, kpi_name2: 2 ...}
"""
gap_sheet = parse_template(self.template_path,
Consts.KPI_GAP,
lower_headers_row_index=0)
gap_data = zip(gap_sheet[Consts.KPI_NAME], gap_sheet[Consts.ORDER])
gap_data = {kpi_name: int(order) for kpi_name, order in gap_data}
return gap_data
def main_calculation(self):
"""
This function calculates the KPI results.
At first it fetches the relevant Sets (according to the stores attributes) and go over all of the relevant
Atomic KPIs based on the project's template.
Than, It aggregates the result per KPI using the weights and at last aggregates for the set level.
"""
self.calculate_hierarchy_sos()
self.calculate_oos()
if self.template_data.empty:
Log.warning(Consts.EMPTY_TEMPLATE_DATA_LOG.format(self.store_id))
return
kpi_set, kpis = self.get_relevant_kpis_for_calculation()
kpi_set_fk = self.common.get_kpi_fk_by_kpi_type(Consts.TOTAL_SCORE)
old_kpi_set_fk = self.get_kpi_fk_by_kpi_name(Consts.TOTAL_SCORE, 1)
total_set_scores = list()
for kpi_name in kpis:
kpi_fk = self.common.get_kpi_fk_by_kpi_type(kpi_name)
old_kpi_fk = self.get_kpi_fk_by_kpi_name(kpi_name, 2)
kpi_weight = self.get_kpi_weight(kpi_name, kpi_set)
atomics_df = self.get_atomics_to_calculate(kpi_name)
atomic_results = self.calculate_atomic_results(
kpi_fk, atomics_df) # Atomic level
kpi_results = self.calculate_kpis_and_save_to_db(
atomic_results, kpi_fk, kpi_weight, kpi_set_fk) # KPI lvl
self.old_common.old_write_to_db_result(fk=old_kpi_fk,
level=2,
score=format(
kpi_results, '.2f'))
total_set_scores.append(kpi_results)
kpi_set_score = self.calculate_kpis_and_save_to_db(
total_set_scores, kpi_set_fk) # Set level
self.old_common.write_to_db_result(fk=old_kpi_set_fk,
level=1,
score=kpi_set_score)
self.handle_gaps()
def calculate_oos(self):
numerator = total_facings = 0
store_kpi_fk = self.common.get_kpi_fk_by_kpi_type(kpi_type=Consts.OOS)
sku_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
kpi_type=Consts.OOS_SKU)
leading_skus_df = self.template_data[self.template_data[
Consts.KPI_NAME].str.encode(
"utf8") == Consts.LEADING_PRODUCTS.encode("utf8")]
skus_ean_list = leading_skus_df[Consts.PARAMS_VALUE_1].tolist()
skus_ean_set = set([
ean_code.strip() for values in skus_ean_list
for ean_code in values.split(",")
])
product_fks = self.all_products[self.all_products[
'product_ean_code'].isin(skus_ean_set)]['product_fk'].tolist()
# sku level oos
for sku in product_fks:
# 2 for distributed and 1 for oos
product_df = self.scif[self.scif['product_fk'] == sku]
if product_df.empty:
numerator += 1
self.common.write_to_db_result(fk=sku_kpi_fk,
numerator_id=sku,
denominator_id=self.store_id,
result=1,
numerator_result=1,
denominator_result=1,
score=0,
identifier_parent="OOS",
should_enter=True)
# store level oos
denominator = len(product_fks)
if denominator == 0:
numerator = result = 0
else:
result = round(numerator / float(denominator), 4)
self.common.write_to_db_result(fk=store_kpi_fk,
numerator_id=self.own_manufacturer_fk,
denominator_id=self.store_id,
result=result,
numerator_result=numerator,
denominator_result=denominator,
score=total_facings,
identifier_result="OOS")
def calculate_hierarchy_sos(self):
store_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
kpi_type=Consts.SOS_BY_OWN_MAN)
category_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
kpi_type=Consts.SOS_BY_OWN_MAN_CAT)
brand_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
kpi_type=Consts.SOS_BY_OWN_MAN_CAT_BRAND)
sku_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
kpi_type=Consts.SOS_BY_OWN_MAN_CAT_BRAND_SKU)
sos_df = self.scif[self.scif['rlv_sos_sc'] == 1]
# store level sos
store_res, store_num, store_den = self.calculate_own_manufacturer_sos(
filters={}, df=sos_df)
self.common.write_to_db_result(fk=store_kpi_fk,
numerator_id=self.own_manufacturer_fk,
denominator_id=self.store_id,
result=store_res,
numerator_result=store_num,
denominator_result=store_den,
score=store_res,
identifier_result="OWN_SOS")
# category level sos
session_categories = set(
self.parser.filter_df(
conditions={'manufacturer_fk': self.own_manufacturer_fk},
data_frame_to_filter=self.scif)['category_fk'])
for category_fk in session_categories:
filters = {'category_fk': category_fk}
cat_res, cat_num, cat_den = self.calculate_own_manufacturer_sos(
filters=filters, df=sos_df)
self.common.write_to_db_result(
fk=category_kpi_fk,
numerator_id=category_fk,
denominator_id=self.store_id,
result=cat_res,
numerator_result=cat_num,
denominator_result=cat_den,
score=cat_res,
identifier_parent="OWN_SOS",
should_enter=True,
identifier_result="OWN_SOS_cat_{}".format(str(category_fk)))
# brand-category level sos
filters['manufacturer_fk'] = self.own_manufacturer_fk
cat_brands = set(
self.parser.filter_df(conditions=filters,
data_frame_to_filter=sos_df)['brand_fk'])
for brand_fk in cat_brands:
filters['brand_fk'] = brand_fk
brand_df = self.parser.filter_df(conditions=filters,
data_frame_to_filter=sos_df)
brand_num = brand_df['facings'].sum()
brand_res, brand_num, cat_num = self.calculate_sos_res(
brand_num, cat_num)
self.common.write_to_db_result(
fk=brand_kpi_fk,
numerator_id=brand_fk,
denominator_id=category_fk,
result=brand_res,
numerator_result=brand_num,
should_enter=True,
denominator_result=cat_num,
score=brand_res,
identifier_parent="OWN_SOS_cat_{}".format(
str(category_fk)),
identifier_result="OWN_SOS_cat_{}_brand_{}".format(
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_facings = product_df['facings'].sum()
sku_result, sku_num, sku_den = self.calculate_sos_res(
sku_facings, brand_num)
self.common.write_to_db_result(
fk=sku_kpi_fk,
numerator_id=sku,
denominator_id=brand_fk,
result=sku_result,
numerator_result=sku_facings,
should_enter=True,
denominator_result=brand_num,
score=sku_facings,
identifier_parent="OWN_SOS_cat_{}_brand_{}".format(
str(category_fk), str(brand_fk)))
del filters['product_fk']
del filters['brand_fk']
def calculate_own_manufacturer_sos(self, filters, df):
filters['manufacturer_fk'] = self.own_manufacturer_fk
numerator_df = self.parser.filter_df(conditions=filters,
data_frame_to_filter=df)
del filters['manufacturer_fk']
denominator_df = self.parser.filter_df(conditions=filters,
data_frame_to_filter=df)
if denominator_df.empty:
return 0, 0, 0
denominator = denominator_df['facings'].sum()
if numerator_df.empty:
numerator = 0
else:
numerator = numerator_df['facings'].sum()
return self.calculate_sos_res(numerator, denominator)
@staticmethod
def calculate_sos_res(numerator, denominator):
if denominator == 0:
return 0, 0, 0
result = round(numerator / float(denominator), 3)
return result, numerator, denominator
def add_gap(self, atomic_kpi, score, atomic_weight):
"""
In case the score is not perfect the gap is added to the gap list.
:param atomic_weight: The Atomic KPI's weight.
:param score: Atomic KPI score.
:param atomic_kpi: A Series with data about the Atomic KPI.
"""
parent_kpi_name = atomic_kpi[Consts.KPI_NAME]
atomic_name = atomic_kpi[Consts.KPI_ATOMIC_NAME]
atomic_fk = self.common.get_kpi_fk_by_kpi_type(atomic_name)
current_gap_dict = {
Consts.ATOMIC_FK: atomic_fk,
Consts.PRIORITY: self.gap_data[parent_kpi_name],
Consts.SCORE: score,
Consts.WEIGHT: atomic_weight
}
self.kpis_gaps.append(current_gap_dict)
@staticmethod
def sort_by_priority(gap_dict):
""" This is a util function for the kpi's gaps sorting by priorities"""
return gap_dict[Consts.PRIORITY], gap_dict[Consts.SCORE]
def handle_gaps(self):
""" This function takes the top 5 gaps (by priority) and saves it to the DB (pservice.custom_gaps table) """
self.kpis_gaps.sort(key=self.sort_by_priority)
gaps_total_score = 0
gaps_per_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
Consts.GAP_PER_ATOMIC_KPI)
gaps_total_score_kpi_fk = self.common.get_kpi_fk_by_kpi_type(
Consts.GAPS_TOTAL_SCORE_KPI)
for gap in self.kpis_gaps[:5]:
current_gap_score = gap[Consts.WEIGHT] - (gap[Consts.SCORE] / 100 *
gap[Consts.WEIGHT])
gaps_total_score += current_gap_score
self.insert_gap_results(gaps_per_kpi_fk,
current_gap_score,
gap[Consts.WEIGHT],
numerator_id=gap[Consts.ATOMIC_FK],
parent_fk=gaps_total_score_kpi_fk)
total_weight = sum(
map(lambda res: res[Consts.WEIGHT], self.kpis_gaps[:5]))
self.insert_gap_results(gaps_total_score_kpi_fk, gaps_total_score,
total_weight)
def insert_gap_results(self,
gap_kpi_fk,
score,
weight,
numerator_id=Consts.CBC_MANU,
parent_fk=None):
""" This is a utility function that insert results to the DB for the GAP """
should_enter = True if parent_fk else False
score, weight = score * 100, round(weight * 100, 2)
self.common.write_to_db_result(fk=gap_kpi_fk,
numerator_id=numerator_id,
numerator_result=score,
denominator_id=self.store_id,
denominator_result=weight,
weight=weight,
identifier_result=gap_kpi_fk,
identifier_parent=parent_fk,
result=score,
score=score,
should_enter=should_enter)
def calculate_kpis_and_save_to_db(self,
kpi_results,
kpi_fk,
parent_kpi_weight=1.0,
parent_fk=None):
"""
This KPI aggregates the score by weights and saves the results to the DB.
:param kpi_results: A list of results and weights tuples: [(score1, weight1), (score2, weight2) ... ].
:param kpi_fk: The relevant KPI fk.
:param parent_kpi_weight: The parent's KPI total weight.
:param parent_fk: The KPI SET FK that the KPI "belongs" too if exist.
:return: The aggregated KPI score.
"""
should_enter = True if parent_fk else False
ignore_weight = not should_enter # Weights should be ignored only in the set level!
kpi_score = self.calculate_kpi_result_by_weight(
kpi_results, parent_kpi_weight, ignore_weights=ignore_weight)
total_weight = round(parent_kpi_weight * 100, 2)
target = None if parent_fk else round(80,
2) # Requested for visualization
self.common.write_to_db_result(fk=kpi_fk,
numerator_id=Consts.CBC_MANU,
numerator_result=kpi_score,
denominator_id=self.store_id,
denominator_result=total_weight,
target=target,
identifier_result=kpi_fk,
identifier_parent=parent_fk,
should_enter=should_enter,
weight=total_weight,
result=kpi_score,
score=kpi_score)
if not parent_fk: # required only for writing set score in anoter kpi needed for dashboard
kpi_fk = self.common.get_kpi_fk_by_kpi_type(
Consts.TOTAL_SCORE_FOR_DASHBOARD)
self.common.write_to_db_result(fk=kpi_fk,
numerator_id=Consts.CBC_MANU,
numerator_result=kpi_score,
denominator_id=self.store_id,
denominator_result=total_weight,
target=target,
identifier_result=kpi_fk,
identifier_parent=parent_fk,
should_enter=should_enter,
weight=total_weight,
result=kpi_score,
score=kpi_score)
return kpi_score
def calculate_kpi_result_by_weight(self,
kpi_results,
parent_kpi_weight,
ignore_weights=False):
"""
This function aggregates the KPI results by scores and weights.
:param ignore_weights: If True the function just sums the results.
:param parent_kpi_weight: The parent's KPI total weight.
:param kpi_results: A list of results and weights tuples: [(score1, weight1), (score2, weight2) ... ].
:return: The aggregated KPI score.
"""
if ignore_weights or len(kpi_results) == 0:
return sum(kpi_results)
weights_list = map(lambda res: res[1], kpi_results)
if None in weights_list: # Ignoring weights and dividing equally by length!
kpi_score = sum(map(lambda res: res[0], kpi_results)) / float(
len(kpi_results))
elif round(
sum(weights_list), 2
) < parent_kpi_weight: # Missing weights needs to be divided among the kpis
kpi_score = self.divide_missing_percentage(kpi_results,
parent_kpi_weight,
sum(weights_list))
else:
kpi_score = sum([score * weight for score, weight in kpi_results])
return kpi_score
@staticmethod
def divide_missing_percentage(kpi_results, parent_weight, total_weights):
"""
This function is been activated in case the total number of KPI weights doesn't equal to 100%.
It divides the missing percentage among the other KPI and calculates the score.
:param parent_weight: Parent KPI's weight.
:param total_weights: The total number of weights that were calculated earlier.
:param kpi_results: A list of results and weights tuples: [(score1, weight1), (score2, weight2) ... ].
:return: KPI aggregated score.
"""
missing_weight = parent_weight - total_weights
weight_addition = missing_weight / float(
len(kpi_results)) if kpi_results else 0
kpi_score = sum([
score * (weight + weight_addition) for score, weight in kpi_results
])
return kpi_score
def calculate_atomic_results(self, kpi_fk, atomics_df):
"""
This method calculates the result for every atomic KPI (the lowest level) that are relevant for the kpi_fk.
:param kpi_fk: The KPI FK that the atomic "belongs" too.
:param atomics_df: The relevant Atomic KPIs from the project's template.
:return: A list of results and weights tuples: [(score1, weight1), (score2, weight2) ... ].
"""
total_scores = list()
for i in atomics_df.index:
current_atomic = atomics_df.loc[i]
kpi_type, atomic_weight, general_filters = self.get_relevant_data_per_atomic(
current_atomic)
if general_filters is None:
continue
num_result, den_result, atomic_score = self.calculate_atomic_kpi_by_type(
kpi_type, **general_filters)
# Handling Atomic KPIs results
if atomic_score is None: # In cases that we need to ignore the KPI and divide it's weight
continue
elif atomic_score < 100:
self.add_gap(current_atomic, atomic_score, atomic_weight)
total_scores.append((atomic_score, atomic_weight))
atomic_fk_lvl_2 = self.common.get_kpi_fk_by_kpi_type(
current_atomic[Consts.KPI_ATOMIC_NAME].strip())
old_atomic_fk = self.get_kpi_fk_by_kpi_name(
current_atomic[Consts.KPI_ATOMIC_NAME].strip(), 3)
self.common.write_to_db_result(fk=atomic_fk_lvl_2,
numerator_id=Consts.CBC_MANU,
numerator_result=num_result,
denominator_id=self.store_id,
weight=round(
atomic_weight * 100, 2),
denominator_result=den_result,
should_enter=True,
identifier_parent=kpi_fk,
result=atomic_score,
score=atomic_score * atomic_weight)
self.old_common.old_write_to_db_result(
fk=old_atomic_fk,
level=3,
result=str(format(atomic_score * atomic_weight, '.2f')),
score=atomic_score)
return total_scores
def get_kpi_fk_by_kpi_name(self, kpi_name, kpi_level):
if kpi_level == 1:
column_key = 'kpi_set_fk'
column_value = 'kpi_set_name'
elif kpi_level == 2:
column_key = 'kpi_fk'
column_value = 'kpi_name'
elif kpi_level == 3:
column_key = 'atomic_kpi_fk'
column_value = 'atomic_kpi_name'
else:
raise ValueError('invalid level')
try:
if column_key and column_value:
return self.kpi_static_data[
self.kpi_static_data[column_value].str.encode('utf-8') ==
kpi_name.encode('utf-8')][column_key].values[0]
except IndexError:
Log.error(
'Kpi name: {}, isnt equal to any kpi name in static table'.
format(kpi_name))
return None
def get_relevant_data_per_atomic(self, atomic_series):
"""
This function return the relevant data per Atomic KPI.
:param atomic_series: The Atomic row from the Template.
:return: A tuple with data: (atomic_type, atomic_weight, general_filters)
"""
kpi_type = atomic_series.get(Consts.KPI_TYPE)
atomic_weight = float(atomic_series.get(
Consts.WEIGHT)) if atomic_series.get(Consts.WEIGHT) else None
general_filters = self.get_general_filters(atomic_series)
return kpi_type, atomic_weight, general_filters
def calculate_atomic_kpi_by_type(self, atomic_type, **general_filters):
"""
This function calculates the result according to the relevant Atomic Type.
:param atomic_type: KPI Family from the template.
:param general_filters: Relevant attributes and values to calculate by.
:return: A tuple with results: (numerator_result, denominator_result, total_score).
"""
num_result = denominator_result = 0
if atomic_type in [Consts.AVAILABILITY]:
atomic_score = self.calculate_availability(**general_filters)
elif atomic_type == Consts.AVAILABILITY_FROM_BOTTOM:
atomic_score = self.calculate_availability_from_bottom(
**general_filters)
elif atomic_type == Consts.MIN_2_AVAILABILITY:
num_result, denominator_result, atomic_score = self.calculate_min_2_availability(
**general_filters)
elif atomic_type == Consts.SURVEY:
atomic_score = self.calculate_survey(**general_filters)
elif atomic_type == Consts.BRAND_BLOCK:
atomic_score = self.calculate_brand_block(**general_filters)
elif atomic_type == Consts.EYE_LEVEL:
num_result, denominator_result, atomic_score = self.calculate_eye_level(
**general_filters)
else:
Log.warning(Consts.UNSUPPORTED_KPI_LOG.format(atomic_type))
atomic_score = None
return num_result, denominator_result, atomic_score
def get_relevant_kpis_for_calculation(self):
"""
This function retrieve the relevant KPIs to calculate from the template
:return: A tuple: (set_name, [kpi1, kpi2, kpi3...]) to calculate.
"""
kpi_set = self.template_data[Consts.KPI_SET].values[0]
kpis = self.template_data[self.template_data[
Consts.KPI_SET].str.encode('utf-8') == kpi_set.encode('utf-8')][
Consts.KPI_NAME].unique().tolist()
# Planogram KPI should be calculated last because of the MINIMUM 2 FACINGS KPI.
if Consts.PLANOGRAM_KPI in kpis and kpis.index(
Consts.PLANOGRAM_KPI) != len(kpis) - 1:
kpis.append(kpis.pop(kpis.index(Consts.PLANOGRAM_KPI)))
return kpi_set, kpis
def get_atomics_to_calculate(self, kpi_name):
"""
This method filters the KPIs data to be the relevant atomic KPIs.
:param kpi_name: The hebrew KPI name from the template.
:return: A DataFrame that contains data about the relevant Atomic KPIs.
"""
atomics = self.template_data[self.template_data[
Consts.KPI_NAME].str.encode('utf-8') == kpi_name.encode('utf-8')]
return atomics
def get_store_attributes(self, attributes_names):
"""
This function encodes and returns the relevant store attribute.
:param attributes_names: List of requested store attributes to return.
:return: A dictionary with the requested attributes, E.g: {attr_name: attr_val, ...}
"""
# Filter store attributes
store_info_dict = self.store_info.iloc[0].to_dict()
filtered_store_info = {
store_att: store_info_dict[store_att]
for store_att in attributes_names
}
return filtered_store_info
def parse_template_data(self):
"""
This function responsible to filter the relevant template data..
:return: A DataFrame with filtered Data by store attributes.
"""
kpis_template = parse_template(self.template_path,
Consts.KPI_SHEET,
lower_headers_row_index=1)
relevant_store_info = self.get_store_attributes(
Consts.STORE_ATTRIBUTES_TO_FILTER_BY)
filtered_data = self.filter_template_by_store_att(
kpis_template, relevant_store_info)
return filtered_data
@staticmethod
def filter_template_by_store_att(kpis_template, store_attributes):
"""
This function gets a dictionary with store type, additional attribute 1, 2 and 3 and filters the template by it.
:param kpis_template: KPI sheet of the project's template.
:param store_attributes: {store_type: X, additional_attribute_1: Y, ... }.
:return: A filtered DataFrame.
"""
for store_att, store_val in store_attributes.iteritems():
if store_val is None:
store_val = ""
kpis_template = kpis_template[(
kpis_template[store_att].str.encode('utf-8') ==
store_val.encode('utf-8')) | (kpis_template[store_att] == "")]
return kpis_template
def get_relevant_scenes_by_params(self, params):
"""
This function returns the relevant scene_fks to calculate.
:param params: The Atomic KPI row filters from the template.
:return: List of scene fks.
"""
template_names = params[Consts.TEMPLATE_NAME].split(Consts.SEPARATOR)
template_groups = params[Consts.TEMPLATE_GROUP].split(Consts.SEPARATOR)
filtered_scif = self.scif[[
Consts.SCENE_ID, 'template_name', 'template_group'
]]
if template_names and any(template_names):
filtered_scif = filtered_scif[filtered_scif['template_name'].isin(
template_names)]
if template_groups and any(template_groups):
filtered_scif = filtered_scif[filtered_scif['template_group'].isin(
template_groups)]
return filtered_scif[Consts.SCENE_ID].unique().tolist()
def get_general_filters(self, params):
"""
This function returns the relevant KPI filters according to the template.
Filter params 1 & 2 are included and param 3 is for exclusion.
:param params: The Atomic KPI row in the template
:return: A dictionary with the relevant filters.
"""
general_filters = {
Consts.TARGET: params[Consts.TARGET],
Consts.SPLIT_SCORE: params[Consts.SPLIT_SCORE],
Consts.KPI_FILTERS: dict()
}
relevant_scenes = self.get_relevant_scenes_by_params(params)
if not relevant_scenes:
return None
else:
general_filters[Consts.KPI_FILTERS][
Consts.SCENE_ID] = relevant_scenes
for type_col, value_col in Consts.KPI_FILTER_VALUE_LIST:
if params[value_col]:
should_included = Consts.INCLUDE_VAL if value_col != Consts.PARAMS_VALUE_3 else Consts.EXCLUDE_VAL
param_type, param_value = params[type_col], params[value_col]
filter_param = self.handle_param_values(
param_type, param_value)
general_filters[Consts.KPI_FILTERS][param_type] = (
filter_param, should_included)
return general_filters
@staticmethod
def handle_param_values(param_type, param_value):
"""
:param param_type: The param type to filter by. E.g: product_ean code or brand_name
:param param_value: The value to filter by.
:return: list of param values.
"""
values_list = param_value.split(Consts.SEPARATOR)
params = map(
lambda val: float(val) if unicode.isdigit(val) and param_type !=
Consts.EAN_CODE else val.strip(), values_list)
return params
def get_kpi_weight(self, kpi, kpi_set):
"""
This method returns the KPI weight according to the project's template.
:param kpi: The KPI name.
:param kpi_set: Set KPI name.
:return: The kpi weight (Float).
"""
row = self.kpi_weights[(self.kpi_weights[Consts.KPI_SET].str.encode(
'utf-8') == kpi_set.encode('utf-8')) & (self.kpi_weights[
Consts.KPI_NAME].str.encode('utf-8') == kpi.encode('utf-8'))]
weight = row.get(Consts.WEIGHT)
return float(weight.values[0]) if not weight.empty else None
def merge_and_filter_scif_and_matches_for_eye_level(self, **kpi_filters):
"""
This function merges between scene_item_facts and match_product_in_scene DataFrames and filters the merged DF
according to the @param kpi_filters.
:param kpi_filters: Dictionary with attributes and values to filter the DataFrame by.
:return: The merged and filtered DataFrame.
"""
scif_matches_diff = self.match_product_in_scene[
['scene_fk', 'product_fk'] +
list(self.match_product_in_scene.keys().difference(
self.scif.keys()))]
merged_df = pd.merge(self.scif[self.scif.facings != 0],
scif_matches_diff,
how='outer',
left_on=['scene_id', 'item_id'],
right_on=[Consts.SCENE_FK, Consts.PRODUCT_FK])
merged_df = merged_df[self.general_toolbox.get_filter_condition(
merged_df, **kpi_filters)]
return merged_df
@kpi_runtime()
def calculate_eye_level(self, **general_filters):
"""
This function calculates the Eye level KPI. It filters and products according to the template and
returns a Tuple: (eye_level_facings / total_facings, score).
:param general_filters: A dictionary with the relevant KPI filters.
:return: E.g: (10, 20, 50) or (8, 10, 100) --> score >= 75 turns to 100.
"""
merged_df = self.merge_and_filter_scif_and_matches_for_eye_level(
**general_filters[Consts.KPI_FILTERS])
relevant_scenes = merged_df['scene_id'].unique().tolist()
total_number_of_facings = eye_level_facings = 0
for scene in relevant_scenes:
scene_merged_df = merged_df[merged_df['scene_id'] == scene]
scene_matches = self.match_product_in_scene[
self.match_product_in_scene['scene_fk'] == scene]
total_number_of_facings += len(scene_merged_df)
scene_merged_df = self.filter_df_by_shelves(
scene_merged_df, scene_matches, Consts.EYE_LEVEL_PER_SHELF)
eye_level_facings += len(scene_merged_df)
total_score = eye_level_facings / float(
total_number_of_facings) if total_number_of_facings else 0
total_score = 100 if total_score >= 0.75 else total_score * 100
return eye_level_facings, total_number_of_facings, total_score
@staticmethod
def filter_df_by_shelves(df, scene_matches, eye_level_definition):
"""
This function filters the df according to the eye-level definition
:param df: data frame to filter
:param scene_matches: match_product_in_scene for particular scene
:param eye_level_definition: definition for eye level shelves
:return: filtered data frame
"""
# number_of_shelves = df.shelf_number_from_bottom.max()
number_of_shelves = max(scene_matches.shelf_number_from_bottom.max(),
scene_matches.shelf_number.max())
top, bottom = 0, 0
for json_def in eye_level_definition:
if json_def[Consts.MIN] <= number_of_shelves <= json_def[
Consts.MAX]:
top = json_def[Consts.TOP]
bottom = json_def[Consts.BOTTOM]
return df[(df.shelf_number > top)
& (df.shelf_number_from_bottom > bottom)]
@kpi_runtime()
def calculate_availability_from_bottom(self, **general_filters):
"""
This function checks if *all* of the relevant products are in the lowest shelf.
:param general_filters: A dictionary with the relevant KPI filters.
:return:
"""
allowed_products_dict = self.get_allowed_product_by_params(
**general_filters)
filtered_matches = self.match_product_in_scene[
self.match_product_in_scene[Consts.PRODUCT_FK].isin(
allowed_products_dict[Consts.PRODUCT_FK])]
relevant_shelves_to_check = set(
filtered_matches[Consts.SHELF_NUM_FROM_BOTTOM].unique().tolist())
# Check bottom shelf condition
return 0 if len(
relevant_shelves_to_check
) != 1 or Consts.LOWEST_SHELF not in relevant_shelves_to_check else 100
@kpi_runtime()
def calculate_brand_block(self, **general_filters):
"""
This function calculates the brand block KPI. It filters and excluded products according to the template and
than checks if at least one scene has a block.
:param general_filters: A dictionary with the relevant KPI filters.
:return: 100 if at least one scene has a block, 0 otherwise.
"""
products_dict = self.get_allowed_product_by_params(**general_filters)
block_result = self.block.network_x_block_together(
population=products_dict,
additional={
'minimum_block_ratio': Consts.MIN_BLOCK_RATIO,
'minimum_facing_for_block': Consts.MIN_FACINGS_IN_BLOCK,
'allowed_products_filters': {
'product_type': ['Empty']
},
'calculate_all_scenes': False,
'include_stacking': True,
'check_vertical_horizontal': False
})
result = 100 if not block_result.empty and not block_result[
block_result.is_block].empty else 0
return result
def get_allowed_product_by_params(self, **filters):
"""
This function filters the relevant products for the block together KPI and exclude the ones that needs to be
excluded by the template.
:param filters: Atomic KPI filters.
:return: A Dictionary with the relevant products. E.g: {'product_fk': [1,2,3,4,5]}.
"""
allowed_product = dict()
filtered_scif = self.calculate_availability(return_df=True, **filters)
allowed_product[Consts.PRODUCT_FK] = filtered_scif[
Consts.PRODUCT_FK].unique().tolist()
return allowed_product
@kpi_runtime()
def calculate_survey(self, **general_filters):
"""
This function calculates the result for Survey KPI.
:param general_filters: A dictionary with the relevant KPI filters.
:return: 100 if the answer is yes, else 0.
"""
if Consts.QUESTION_ID not in general_filters[
Consts.KPI_FILTERS].keys():
Log.warning(Consts.MISSING_QUESTION_LOG)
return 0
survey_question_id = general_filters[Consts.KPI_FILTERS].get(
Consts.QUESTION_ID)
# General filters returns output for filter_df basically so we need to adjust it here.
if isinstance(survey_question_id, tuple):
survey_question_id = survey_question_id[0] # Get rid of the tuple
if isinstance(survey_question_id, list):
survey_question_id = int(
survey_question_id[0]) # Get rid of the list
target_answer = general_filters[Consts.TARGET]
survey_answer = self.survey.get_survey_answer(
(Consts.QUESTION_FK, survey_question_id))
if survey_answer in Consts.SURVEY_ANSWERS_TO_IGNORE:
return None
elif survey_answer:
return 100 if survey_answer.strip() == target_answer else 0
return 0
@kpi_runtime()
def calculate_availability(self, return_df=False, **general_filters):
"""
This functions checks for availability by filters.
During the calculation, if the KPI was passed, the results is being saved for future usage of
"MIN 2 AVAILABILITY KPI".
:param return_df: If True, the function returns the filtered scene item facts, else, returns the score.
:param general_filters: A dictionary with the relevant KPI filters.
:return: See @param return_df.
"""
filtered_scif = self.scif[self.general_toolbox.get_filter_condition(
self.scif, **general_filters[Consts.KPI_FILTERS])]
if return_df:
return filtered_scif
if not filtered_scif.empty:
tested_products = general_filters[Consts.KPI_FILTERS][
Consts.EAN_CODE][0]
self.passed_availability.append(tested_products)
return 100
return 0
@staticmethod
def get_number_of_facings_per_product_dict(df, ignore_stack=False):
"""
This function gets a DataFrame and returns a dictionary with number of facings per products.
:param df: Pandas.DataFrame with 'product_ean_code' and 'facings' / 'facings_ign_stack' fields.
:param ignore_stack: If True will use 'facings_ign_stack' field, else 'facings' field.
:return: E.g: {ean_code1: 10, ean_code2: 5, ean_code3: 1...}
"""
stacking_field = Consts.FACINGS_IGN_STACK if ignore_stack else Consts.FACINGS
df = df[[Consts.EAN_CODE, stacking_field]].dropna()
df = df[df[stacking_field] > 0]
facings_dict = dict(zip(df[Consts.EAN_CODE], df[stacking_field]))
return facings_dict
@kpi_runtime()
def calculate_min_2_availability(self, **general_filters):
"""
This KPI checks for all of the Availability Atomics KPIs that passed, if the tested products have at least
2 facings in case of IGNORE STACKING!
:param general_filters: A dictionary with the relevant KPI filters.
:return: numerator result, denominator result and total_score
"""
score = 0
filtered_df = self.calculate_availability(return_df=True,
**general_filters)
facings_counter = self.get_number_of_facings_per_product_dict(
filtered_df, ignore_stack=True)
for products in self.passed_availability:
score += 1 if sum([
facings_counter[product]
for product in products if product in facings_counter
]) > 1 else 0
total_score = (score / float(len(self.passed_availability))
) * 100 if self.passed_availability else 0
return score, len(self.passed_availability), total_score
class PSAPAC_SAND3ToolBox:
# Gsk Japan kpis
# DEFAULT_TARGET = {ProductsConsts.BRAND_FK: [-1], 'shelves': ["1,2,3"], 'block_target': [80], 'brand_target': [100], 'position_target': [80]}
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_kpi_static_data()
self.kpi_results_queries = []
self.set_up_template = pd.read_excel(os.path.join(
os.path.dirname(os.path.realpath(__file__)), '..', 'Data',
'gsk_set_up.xlsx'),
sheet_name='Functional KPIs',
keep_default_na=False)
self.gsk_generator = GSKGenerator(self.data_provider, self.output,
self.common, self.set_up_template)
self.blocking_generator = Block(self.data_provider)
self.assortment = self.gsk_generator.get_assortment_data_provider()
self.store_info = self.data_provider['store_info']
self.store_fk = self.data_provider[StoreInfoConsts.STORE_FK]
self.ps_data_provider = PsDataProvider(self.data_provider, self.output)
self.targets = self.ps_data_provider.get_kpi_external_targets(
key_fields=Consts.KEY_FIELDS, data_fields=Consts.DATA_FIELDS)
self.own_manufacturer = self.get_manufacturer
self.set_up_data = {
(Consts.PLN_BLOCK, Const.KPI_TYPE_COLUMN): Const.NO_INFO,
(Consts.POSITION_SCORE, Const.KPI_TYPE_COLUMN): Const.NO_INFO,
(Consts.ECAPS_FILTER_IDENT, Const.KPI_TYPE_COLUMN): Const.NO_INFO,
(Consts.PLN_MSL, Const.KPI_TYPE_COLUMN): Const.NO_INFO,
("GSK_PLN_LSOS_SCORE", Const.KPI_TYPE_COLUMN): Const.NO_INFO,
(Consts.POSM, Const.KPI_TYPE_COLUMN): Const.NO_INFO
}
@property
def get_manufacturer(self):
return int(self.data_provider.own_manufacturer[
self.data_provider.own_manufacturer['param_name'] ==
'manufacturer_id']['param_value'].iloc[0])
def main_calculation(self, *args, **kwargs):
"""
This function calculates the KPI results.Global functions and local functions
"""
# global kpis
assortment_store_dict = self.gsk_generator.availability_store_function(
)
self.common.save_json_to_new_tables(assortment_store_dict)
assortment_category_dict = self.gsk_generator.availability_category_function(
)
self.common.save_json_to_new_tables(assortment_category_dict)
assortment_subcategory_dict = self.gsk_generator.availability_subcategory_function(
)
self.common.save_json_to_new_tables(assortment_subcategory_dict)
linear_sos_dict = self.gsk_generator.gsk_global_linear_sos_by_sub_category_function(
)
self.common.save_json_to_new_tables(linear_sos_dict)
linear_sos_dict = self.gsk_generator.gsk_global_linear_sos_by_category_function(
)
self.common.save_json_to_new_tables(linear_sos_dict)
linear_sos_dict = self.gsk_generator.gsk_global_linear_sos_whole_store_function(
)
self.common.save_json_to_new_tables(linear_sos_dict)
# # local kpis
for kpi in Consts.KPI_DICT.keys():
self.gsk_generator.tool_box.extract_data_set_up_file(
kpi, self.set_up_data, Consts.KPI_DICT)
results_ecaps = self.gsk_ecaps_kpis()
self.common.save_json_to_new_tables(results_ecaps)
self.get_store_target() # choosing the policy
if self.targets.empty:
Log.warning('There is no target policy matching this store')
else:
results_compliance = self.gsk_compliance()
self.common.save_json_to_new_tables(results_compliance)
results_pos = self.gsk_pos_kpis()
self.common.save_json_to_new_tables(results_pos)
self.common.commit_results_data()
return
def position_shelf(self, brand_fk, policy, df):
"""
:param brand_fk :
:param policy : dictionary that contains {
'shelves':"1 ,2 ,4 ,5" (or any other string of numbers separate by ','),
'position_target': 80 (or any other percentage you want the score to
reach)
}
:param df: data frame that contains columns MatchesConsts.SHELF_NUMBER , "brand kf"
:returns tuple of (result,score,numerator,denominator)
result = number of products from brand_fk in shelves / number of products from brand_fk ,
score = if result reach position target 100 else 0 ,
numerator = number of products from brand_fk in shelves
denominator = number of products from brand_fk
"""
if (Consts.SHELVES
not in policy.keys()) or policy[Consts.SHELVES].empty:
Log.warning(
'This sessions have external targets but doesnt have value for shelves position'
)
return 0, 0, 0, 0, 0
if isinstance(policy[Consts.SHELVES].iloc[0], list):
shelf_from_bottom = [
int(shelf) for shelf in policy[Consts.SHELVES].iloc[0]
]
else:
shelf_from_bottom = [
int(shelf)
for shelf in policy[Consts.SHELVES].iloc[0].split(",")
]
threshold = policy[Consts.POSITION_TARGET].iloc[0]
brand_df = df[df[ProductsConsts.BRAND_FK] == brand_fk]
shelf_df = brand_df[brand_df[MatchesConsts.SHELF_NUMBER].isin(
shelf_from_bottom)]
numerator = shelf_df.shape[0]
denominator = brand_df.shape[0]
result = float(numerator) / float(denominator)
score = 1 if (result * 100) >= threshold else 0
return result, score, numerator, denominator, threshold
def lsos_score(self, brand, policy):
"""
:param brand : pk of brand
:param policy : dictionary of { 'brand_target' : lsos number you want to reach}
This function uses the lsos_in whole_store global calculation.
it takes the result of the parameter 'brand' according to the policy set target and results.
:return result,score,target
result : result of this brand lsos
score : result / brand_target ,
target : branf_target
"""
df = pd.merge(self.match_product_in_scene,
self.all_products[Const.PRODUCTS_COLUMNS],
how='left',
on=[MatchesConsts.PRODUCT_FK])
df = pd.merge(self.scif[Const.SCIF_COLUMNS],
df,
how='right',
right_on=[ScifConsts.SCENE_FK, ScifConsts.PRODUCT_FK],
left_on=[ScifConsts.SCENE_ID, ScifConsts.PRODUCT_FK])
if df.empty:
Log.warning('match_product_in_scene is empty ')
return 0, 0, 0
df = self.gsk_generator.tool_box.tests_by_template(
'GSK_PLN_LSOS_SCORE', df, self.set_up_data)
if df is None:
Log.warning('match_product_in_scene is empty ')
return 0, 0, 0
result = self.gsk_generator.tool_box.calculate_sos(
df, {ProductsConsts.BRAND_FK: brand}, {}, Const.LINEAR)[0]
target = policy['brand_target'].iloc[0]
score = float(result) / float(target)
return result, score, target
def brand_blocking(self, brand, policy):
"""
:param brand : pk of brand
:param policy : dictionary of { 'block_target' : number you want to reach}
:return result : 1 if there is a block answer set_up_data conditions else 0
"""
templates = self.set_up_data[(Const.SCENE_TYPE, Consts.PLN_BLOCK)]
template_name = {
ScifConsts.TEMPLATE_NAME: templates
} if templates else None # figure out which template name should I use
ignore_empty = False
# taking from params from set up info
stacking_param = False if not self.set_up_data[(
Const.INCLUDE_STACKING, Consts.PLN_BLOCK)] else True # false
population_parameters = {
ProductsConsts.BRAND_FK: [brand],
ProductsConsts.PRODUCT_TYPE: [ProductTypeConsts.SKU]
}
if self.set_up_data[(Const.INCLUDE_OTHERS, Consts.PLN_BLOCK)]:
population_parameters[ProductsConsts.PRODUCT_TYPE].append(
Const.OTHER)
if self.set_up_data[(Const.INCLUDE_IRRELEVANT, Consts.PLN_BLOCK)]:
population_parameters[ProductsConsts.PRODUCT_TYPE].append(
Const.IRRELEVANT)
if self.set_up_data[(Const.INCLUDE_EMPTY, Consts.PLN_BLOCK)]:
population_parameters[ProductsConsts.PRODUCT_TYPE].append(
Const.EMPTY)
else:
ignore_empty = True
if self.set_up_data[(Const.CATEGORY_INCLUDE,
Consts.PLN_BLOCK)]: # category_name
population_parameters[ProductsConsts.CATEGORY] = self.set_up_data[(
Const.CATEGORY_INCLUDE, Consts.PLN_BLOCK)]
if self.set_up_data[(Const.SUB_CATEGORY_INCLUDE,
Consts.PLN_BLOCK)]: # sub_category_name
population_parameters[
ProductsConsts.SUB_CATEGORY] = self.set_up_data[(
Const.SUB_CATEGORY_INCLUDE, Consts.PLN_BLOCK)]
# from Data file
target = float(policy['block_target'].iloc[0]) / float(100)
result = self.blocking_generator.network_x_block_together(
location=template_name,
population=population_parameters,
additional={
'minimum_block_ratio': target,
'calculate_all_scenes': True,
'ignore_empty': ignore_empty,
'include_stacking': stacking_param,
'check_vertical_horizontal': True,
'minimum_facing_for_block': 1
})
result.sort_values('facing_percentage', ascending=False, inplace=True)
score = 0 if result[result['is_block']].empty else 1
numerator = 0 if result.empty else result['block_facings'].iloc[0]
denominator = 0 if result.empty else result['total_facings'].iloc[0]
return score, target, numerator, denominator
def msl_assortment(self, kpi_fk, kpi_name):
"""
:param kpi_fk : name of level 3 assortment kpi
:param kpi_name: GSK_PLN_MSL_SCORE assortment , or GSK_ECAPS assortment
:return kpi_results : data frame of assortment products of the kpi, product's availability,
product details.
filtered by set up
"""
lvl3_assort, filter_scif = self.gsk_generator.tool_box.get_assortment_filtered(
self.set_up_data, kpi_name)
if lvl3_assort is None or lvl3_assort.empty:
return None
kpi_assortment_fk = self.common.get_kpi_fk_by_kpi_type(kpi_fk)
kpi_results = lvl3_assort[lvl3_assort['kpi_fk_lvl3'] ==
kpi_assortment_fk] # general assortment
kpi_results = pd.merge(kpi_results,
self.all_products[Const.PRODUCTS_COLUMNS],
how='left',
on=ProductsConsts.PRODUCT_FK)
kpi_results = kpi_results[kpi_results[
ProductsConsts.SUBSTITUTION_PRODUCT_FK].isnull()]
return kpi_results
def pln_ecaps_score(self, brand, assortment):
"""
:param brand : pk of desired brand
:param assortment : data frame of assortment products of the kpi, product's availability,
product details. filtered by set up
besides result of lvl2_assortment function writing level 3 assortment product presence
results
:return numerator : how many products available out of the granular groups
denominator : how many products in assortment groups
result : (numerator/denominator)*100
results : array of dictionary, each dict contains the result details
"""
identifier_parent = self.common.get_dictionary(
brand_fk=brand,
kpi_fk=self.common.get_kpi_fk_by_kpi_type(Consts.ECAP_ALL_BRAND))
results = []
kpi_ecaps_product = self.common.get_kpi_fk_by_kpi_type(
Consts.PRODUCT_PRESENCE)
ecaps_assortment_fk = self.common.get_kpi_fk_by_kpi_type(
Consts.PLN_ASSORTMENT_KPI)
if assortment.empty:
return 0, 0, 0, results
brand_results = assortment[assortment[ProductsConsts.BRAND_FK] ==
brand] # only assortment of desired brand
for result in brand_results.itertuples():
if (math.isnan(result.in_store)) | (result.kpi_fk_lvl3 !=
ecaps_assortment_fk):
score = self.gsk_generator.tool_box.result_value_pk(
Const.EXTRA)
result_num = 1
else:
score = self.gsk_generator.tool_box.result_value_pk(Const.OOS) if result.in_store == 0 else \
self.gsk_generator.tool_box.result_value_pk(Const.DISTRIBUTED)
result_num = result.in_store
last_status = self.gsk_generator.tool_box.get_last_status(
kpi_ecaps_product, result.product_fk)
# score = result.in_store * 100
results.append({
'fk': kpi_ecaps_product,
SessionResultsConsts.NUMERATOR_ID: result.product_fk,
SessionResultsConsts.DENOMINATOR_ID: self.store_fk,
SessionResultsConsts.DENOMINATOR_RESULT: 1,
SessionResultsConsts.NUMERATOR_RESULT: result_num,
SessionResultsConsts.RESULT: score,
SessionResultsConsts.SCORE: last_status,
'identifier_parent': identifier_parent,
'identifier_result': 1,
'should_enter': True
})
if 'total' not in self.assortment.LVL2_HEADERS or 'passes' not in self.assortment.LVL2_HEADERS:
self.assortment.LVL2_HEADERS.extend(['total', 'passes'])
lvl2 = self.assortment.calculate_lvl2_assortment(brand_results)
if lvl2.empty:
return 0, 0, 0, results # in case of no assortment return 0
result = round(
np.divide(float(lvl2.iloc[0].passes), float(lvl2.iloc[0].total)),
4)
return lvl2.iloc[0].passes, lvl2.iloc[0].total, result, results
def pln_msl_summary(self, brand, assortment):
"""
:param brand : pk of desired brand
:param assortment : data frame of assortment products of the kpi, product's availability,
product details. filtered by set up
:return numerator : how many products available out of the granular groups
denominator : how many products in assortment groups
result : (numerator/denominator)*100
results : array of dictionary, each dict contains the result details
"""
if assortment is None or assortment.empty:
return 0, 0, 0, 0
brand_results = assortment[assortment[ProductsConsts.BRAND_FK] ==
brand] # only assortment of desired brand
if 'total' not in self.assortment.LVL2_HEADERS or 'passes' not in self.assortment.LVL2_HEADERS:
self.assortment.LVL2_HEADERS.extend(['total', 'passes'])
lvl2 = self.assortment.calculate_lvl2_assortment(brand_results)
if lvl2.empty:
return 0, 0, 0, 0 # in case of no assortment return 0
result = round(
np.divide(float(lvl2.iloc[0].passes), float(lvl2.iloc[0].total)),
4)
return lvl2.iloc[0].passes, lvl2.iloc[0].total, result, lvl2.iloc[
0].assortment_group_fk
def get_store_target(self):
"""
Function checks which policies out of self.target are relevant to this store visit according to store
attributes.
"""
parameters_dict = {StoreInfoConsts.STORE_NUMBER_1: 'store_number'}
for store_param, target_param in parameters_dict.items():
if target_param in self.targets.columns:
if self.store_info[store_param][0] is None:
if self.targets.empty or self.targets[
self.targets[target_param] != ''].empty:
continue
else:
self.targets.drop(self.targets.index, inplace=True)
self.targets = self.targets[
(self.targets[target_param] ==
self.store_info[store_param][0].encode(HelperConsts.UTF8))
| (self.targets[target_param] == '')]
def gsk_compliance(self):
"""
Function calculate compliance score for each brand based on :
position score, brand-assortment score,
block score ,lsos score.
Also calculate compliance summary score - average of brands compliance scores
"""
results_df = []
df = self.scif
# kpis
kpi_block_fk = self.common.get_kpi_fk_by_kpi_type(Consts.PLN_BLOCK)
kpi_position_fk = self.common.get_kpi_fk_by_kpi_type(
Consts.POSITION_SCORE)
kpi_lsos_fk = self.common.get_kpi_fk_by_kpi_type(Consts.PLN_LSOS)
kpi_msl_fk = self.common.get_kpi_fk_by_kpi_type(Consts.PLN_MSL)
kpi_compliance_brands_fk = self.common.get_kpi_fk_by_kpi_type(
Consts.COMPLIANCE_ALL_BRANDS)
kpi_compliance_summary_fk = self.common.get_kpi_fk_by_kpi_type(
Consts.COMPLIANCE_SUMMARY)
identifier_compliance_summary = self.common.get_dictionary(
kpi_fk=kpi_compliance_summary_fk)
# targets
block_target = 0.25
posit_target = 0.25
lsos_target = 0.25
msl_target = 0.25
total_brand_score = 0
counter_brands = 0
# assortment_lvl3 msl df initialize
self.gsk_generator.tool_box.extract_data_set_up_file(
Consts.PLN_MSL, self.set_up_data, Consts.KPI_DICT)
assortment_msl = self.msl_assortment(Const.DISTRIBUTION,
Consts.PLN_MSL)
# set data frame to find position shelf
df_position_score = pd.merge(self.match_product_in_scene,
self.all_products,
on=ProductsConsts.PRODUCT_FK)
df_position_score = pd.merge(
self.scif[Const.SCIF_COLUMNS],
df_position_score,
how='right',
right_on=[ScifConsts.SCENE_FK, ProductsConsts.PRODUCT_FK],
left_on=[ScifConsts.SCENE_ID, ScifConsts.PRODUCT_FK])
df_position_score = self.gsk_generator.tool_box.tests_by_template(
Consts.POSITION_SCORE, df_position_score, self.set_up_data)
if not self.set_up_data[(Const.INCLUDE_STACKING,
Consts.POSITION_SCORE)]:
df_position_score = df_position_score if df_position_score is None else df_position_score[
df_position_score[MatchesConsts.STACKING_LAYER] == 1]
# calculate all brands if template doesnt require specific brand else only for specific brands
template_brands = self.set_up_data[(Const.BRANDS_INCLUDE,
Consts.PLN_BLOCK)]
brands = df[df[ProductsConsts.BRAND_NAME].isin(template_brands)][ProductsConsts.BRAND_FK].unique() if \
template_brands else df[ProductsConsts.BRAND_FK].dropna().unique()
for brand in brands:
policy = self.targets[self.targets[ProductsConsts.BRAND_FK] ==
brand]
if policy.empty:
Log.warning('There is no target policy matching brand'
) # adding brand name
return results_df
identifier_parent = self.common.get_dictionary(
brand_fk=brand, kpi_fk=kpi_compliance_brands_fk)
# msl_kpi
msl_numerator, msl_denominator, msl_result, msl_assortment_group = self.pln_msl_summary(
brand, assortment_msl)
msl_score = msl_result * msl_target
results_df.append({
'fk': kpi_msl_fk,
SessionResultsConsts.NUMERATOR_ID: brand,
SessionResultsConsts.DENOMINATOR_ID: self.store_fk,
SessionResultsConsts.DENOMINATOR_RESULT: msl_denominator,
SessionResultsConsts.NUMERATOR_RESULT: msl_numerator,
SessionResultsConsts.RESULT: msl_result,
SessionResultsConsts.SCORE: msl_score,
SessionResultsConsts.TARGET: msl_target,
SessionResultsConsts.CONTEXT_ID: msl_assortment_group,
'identifier_parent': identifier_parent,
'should_enter': True
})
# lsos kpi
lsos_numerator, lsos_result, lsos_denominator = self.lsos_score(
brand, policy)
lsos_result = 1 if lsos_result > 1 else lsos_result
lsos_score = lsos_result * lsos_target
results_df.append({
'fk': kpi_lsos_fk,
SessionResultsConsts.NUMERATOR_ID: brand,
SessionResultsConsts.DENOMINATOR_ID: self.store_fk,
SessionResultsConsts.DENOMINATOR_RESULT: lsos_denominator,
SessionResultsConsts.NUMERATOR_RESULT: lsos_numerator,
SessionResultsConsts.RESULT: lsos_result,
SessionResultsConsts.SCORE: lsos_score,
SessionResultsConsts.TARGET: lsos_target,
'identifier_parent': identifier_parent,
SessionResultsConsts.WEIGHT: lsos_denominator,
'should_enter': True
})
# block_score
block_result, block_benchmark, numerator_block, block_denominator = self.brand_blocking(
brand, policy)
block_score = round(block_result * block_target, 4)
results_df.append({
'fk':
kpi_block_fk,
SessionResultsConsts.NUMERATOR_ID:
brand,
SessionResultsConsts.DENOMINATOR_ID:
self.store_fk,
SessionResultsConsts.DENOMINATOR_RESULT:
block_denominator,
SessionResultsConsts.NUMERATOR_RESULT:
numerator_block,
SessionResultsConsts.RESULT:
block_result,
SessionResultsConsts.SCORE:
block_score,
SessionResultsConsts.TARGET:
block_target,
'identifier_parent':
identifier_parent,
'should_enter':
True,
SessionResultsConsts.WEIGHT: (block_benchmark * 100)
})
# position score
if df_position_score is not None:
position_result, position_score, position_num, position_den, position_benchmark = self.position_shelf(
brand, policy, df_position_score)
else:
position_result, position_score, position_num, position_den, position_benchmark = 0, 0, 0, 0, 0
position_score = round(position_score * posit_target, 4)
results_df.append({
'fk': kpi_position_fk,
SessionResultsConsts.NUMERATOR_ID: brand,
SessionResultsConsts.DENOMINATOR_ID: self.store_fk,
SessionResultsConsts.DENOMINATOR_RESULT: position_den,
SessionResultsConsts.NUMERATOR_RESULT: position_num,
SessionResultsConsts.RESULT: position_result,
SessionResultsConsts.SCORE: position_score,
SessionResultsConsts.TARGET: posit_target,
'identifier_parent': identifier_parent,
'should_enter': True,
SessionResultsConsts.WEIGHT: position_benchmark
})
# compliance score per brand
compliance_score = round(
position_score + block_score + lsos_score + msl_score, 4)
results_df.append({
'fk': kpi_compliance_brands_fk,
SessionResultsConsts.NUMERATOR_ID: self.own_manufacturer,
SessionResultsConsts.DENOMINATOR_ID: brand,
SessionResultsConsts.DENOMINATOR_RESULT: 1,
SessionResultsConsts.NUMERATOR_RESULT: compliance_score,
SessionResultsConsts.RESULT: compliance_score,
SessionResultsConsts.SCORE: compliance_score,
'identifier_parent': identifier_compliance_summary,
'identifier_result': identifier_parent,
'should_enter': True
})
# counter and sum updates
total_brand_score = round(total_brand_score + compliance_score, 4)
counter_brands = counter_brands + 1
if counter_brands == 0:
return results_df
# compliance summary
average_brand_score = round(total_brand_score / counter_brands, 4)
results_df.append({
'fk': kpi_compliance_summary_fk,
SessionResultsConsts.NUMERATOR_ID: self.own_manufacturer,
SessionResultsConsts.DENOMINATOR_ID: self.store_fk,
SessionResultsConsts.DENOMINATOR_RESULT: counter_brands,
SessionResultsConsts.NUMERATOR_RESULT: total_brand_score,
SessionResultsConsts.RESULT: average_brand_score,
SessionResultsConsts.SCORE: average_brand_score,
'identifier_result': identifier_compliance_summary
})
return results_df
def gsk_ecaps_kpis(self):
"""
Function calculate for each brand ecaps score, and for all brands together set ecaps summary score
:return
results_df : array of dictionary, each dict contains kpi's result details
"""
results_df = []
kpi_ecaps_brands_fk = self.common.get_kpi_fk_by_kpi_type(
Consts.ECAP_ALL_BRAND)
kpi_ecaps_summary_fk = self.common.get_kpi_fk_by_kpi_type(
Consts.ECAP_SUMMARY)
identifier_ecaps_summary = self.common.get_dictionary(
kpi_fk=kpi_ecaps_summary_fk)
total_brand_score = 0
assortment_display = self.msl_assortment(Consts.PLN_ASSORTMENT_KPI,
Consts.ECAPS_FILTER_IDENT)
if assortment_display is None or assortment_display.empty:
return results_df
template_brands = self.set_up_data[(Const.BRANDS_INCLUDE,
Consts.ECAPS_FILTER_IDENT)]
brands = assortment_display[assortment_display[ProductsConsts.BRAND_NAME].isin(template_brands)][
ProductsConsts.BRAND_FK].unique() if \
template_brands else assortment_display[ProductsConsts.BRAND_FK].dropna().unique()
for brand in brands:
numerator_res, denominator_res, result, product_presence_df = self.pln_ecaps_score(
brand, assortment_display)
results_df.extend(product_presence_df)
identifier_all_brand = self.common.get_dictionary(
brand_fk=brand,
kpi_fk=self.common.get_kpi_fk_by_kpi_type(
Consts.ECAP_ALL_BRAND))
results_df.append({
'fk': kpi_ecaps_brands_fk,
SessionResultsConsts.NUMERATOR_ID: self.own_manufacturer,
SessionResultsConsts.DENOMINATOR_ID: brand,
SessionResultsConsts.DENOMINATOR_RESULT: denominator_res,
SessionResultsConsts.NUMERATOR_RESULT: numerator_res,
SessionResultsConsts.RESULT: result,
SessionResultsConsts.SCORE: result,
'identifier_parent': identifier_ecaps_summary,
'identifier_result': identifier_all_brand,
'should_enter': True
})
total_brand_score = total_brand_score + result
if len(
brands
) > 0: # don't want to show result in case of there are no brands relevan to the template
result_summary = round(total_brand_score / len(brands), 4)
results_df.append({
'fk':
kpi_ecaps_summary_fk,
SessionResultsConsts.NUMERATOR_ID:
self.own_manufacturer,
SessionResultsConsts.DENOMINATOR_ID:
self.store_fk,
SessionResultsConsts.DENOMINATOR_RESULT:
len(brands),
SessionResultsConsts.NUMERATOR_RESULT:
total_brand_score,
SessionResultsConsts.RESULT:
result_summary,
SessionResultsConsts.SCORE:
result_summary,
'identifier_result':
identifier_ecaps_summary
})
return results_df
def gsk_pos_kpis(self):
"""
Function calculate POSM Distribution
:return
- results : array of dictionary, each dict contains kpi's result details
"""
results = []
OOS = 1
DISTRIBUTED = 2
self.gsk_generator.tool_box.extract_data_set_up_file(
Consts.POSM, self.set_up_data, Consts.KPI_DICT)
assortment_pos = self.msl_assortment(Consts.POSM_SKU, Consts.POSM)
kpi_gsk_pos_distribution_store_fk = self.common.get_kpi_fk_by_kpi_type(
Consts.GSK_POS_DISTRIBUTION_STORE)
kpi_gsk_pos_distribution_brand_fk = self.common.get_kpi_fk_by_kpi_type(
Consts.GSK_POS_DISTRIBUTION_BRAND)
kpi_gsk_pos_distribution_sku_fk = self.common.get_kpi_fk_by_kpi_type(
Consts.GSK_POS_DISTRIBUTION_SKU)
if assortment_pos is None or assortment_pos.empty:
Log.info(
"Assortment df is empty. GSK_POS_DISTRIBUTION Kpis are not calculated"
)
return results
# Calculate KPI : GSK_POS_DISTRIBUTION_STORE
assortment_pos['in_store'] = assortment_pos['in_store'].astype('int')
Log.info(
"Dropping duplicate product_fks accros multiple-granular groups")
Log.info("Before : {}".format(len(assortment_pos)))
assortment_pos = assortment_pos.drop_duplicates(
subset=[ProductsConsts.PRODUCT_FK])
Log.info("After : {}".format(len(assortment_pos)))
numerator_res = len(assortment_pos[assortment_pos['in_store'] == 1])
denominator_res = len(assortment_pos)
result = round(
(numerator_res /
float(denominator_res)), 4) if denominator_res != 0 else 0
results.append({
'fk': kpi_gsk_pos_distribution_store_fk,
SessionResultsConsts.NUMERATOR_ID: self.own_manufacturer,
SessionResultsConsts.DENOMINATOR_ID: self.store_fk,
SessionResultsConsts.NUMERATOR_RESULT: numerator_res,
SessionResultsConsts.DENOMINATOR_RESULT: denominator_res,
SessionResultsConsts.RESULT: result,
SessionResultsConsts.SCORE: result,
# 'identifier_parent': identifier_ecaps_summary,
'identifier_result': "Gsk_Pos_Distribution_Store",
'should_enter': True
})
# Calculate KPI: GSK_POS_DISTRIBUTION_BRAND
brands_group = assortment_pos.groupby([ProductsConsts.BRAND_FK])
for brand, assortment_pos_by_brand in brands_group:
numerator_res = len(assortment_pos_by_brand[
assortment_pos_by_brand['in_store'] == 1])
denominator_res = len(assortment_pos_by_brand)
result = round(
(numerator_res /
float(denominator_res)), 4) if denominator_res != 0 else 0
results.append({
'fk':
kpi_gsk_pos_distribution_brand_fk,
SessionResultsConsts.NUMERATOR_ID:
int(brand),
SessionResultsConsts.DENOMINATOR_ID:
self.store_fk,
SessionResultsConsts.NUMERATOR_RESULT:
numerator_res,
SessionResultsConsts.DENOMINATOR_RESULT:
denominator_res,
SessionResultsConsts.RESULT:
result,
SessionResultsConsts.SCORE:
result,
'identifier_parent':
"Gsk_Pos_Distribution_Store",
'identifier_result':
"Gsk_Pos_Distribution_Brand_" + str(int(brand)),
'should_enter':
True
})
for idx, each_product in assortment_pos_by_brand.iterrows():
product_fk = each_product[ProductsConsts.PRODUCT_FK]
result = 1 if int(each_product['in_store']) == 1 else 0
result_status = DISTRIBUTED if result == 1 else OOS
last_status = self.gsk_generator.tool_box.get_last_status(
kpi_gsk_pos_distribution_sku_fk, product_fk)
results.append({
'fk':
kpi_gsk_pos_distribution_sku_fk,
SessionResultsConsts.NUMERATOR_ID:
product_fk,
SessionResultsConsts.DENOMINATOR_ID:
self.store_fk,
SessionResultsConsts.NUMERATOR_RESULT:
result,
SessionResultsConsts.DENOMINATOR_RESULT:
1,
SessionResultsConsts.RESULT:
result_status,
SessionResultsConsts.SCORE:
last_status,
'identifier_parent':
"Gsk_Pos_Distribution_Brand_" + str(int(brand)),
'identifier_result':
"Gsk_Pos_Distribution_SKU_" + str(int(product_fk)),
'should_enter':
True
})
return results
class TysonToolBox:
def __init__(self, data_provider, output):
self.data_provider = data_provider
self.common = Common(data_provider)
self.output = output
self.ps_data_provider = PsDataProvider(data_provider, output)
self.session_info = self.data_provider[Data.SESSION_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.manufacturer_id = self.get_manufacturer_id_from_manufacturer_name(Const.MANUFACTURER)
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.template_info = self.data_provider[Data.TEMPLATES]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.block = Block(self.data_provider, self.output, common=self.common)
self.mpis = self.match_product_in_scene \
.merge(self.products, on='product_fk', suffixes=['', '_p']) \
.merge(self.scene_info, on='scene_fk', suffixes=['', '_s'])[COLUMNS]
def main_calculation(self):
self.calculate_shelf_neighbors('scrambles', 'breakfast meat')
self.calculate_shelf_neighbors('scrambles', 'irrelevant')
self.calculate_shelf_neighbors('ore ida', 'breakfast meat')
self.calculate_shelf_neighbors('ore ida', 'irrelevant')
self.calculate_max_block_adjacency('scrambles', 'ore ida')
self.calculate_adjacent_bay('breakfast meat', 'irrelevant')
def calculate_shelf_neighbors(self, anchor, target):
"""
Determines whether any products in `anchor` are located in the same scene and bay as any products in `target`.
:param anchor: Key referencing the anchor product.
:param target: Key referencing the products being compared.
"""
kpi = Const.KPIs[(anchor, target)]
kpi_id = self.common.get_kpi_fk_by_kpi_name(kpi)
brand_id = self.get_brand_id_from_brand_name(Const.BRANDs.get(anchor))
result = self.neighbors(anchor, target, target_type='product') if target == 'irrelevant' \
else self.neighbors(anchor, target)
self.common.write_to_db_result(
fk=kpi_id,
numerator_id=brand_id,
numerator_result=result,
denominator_id=self.store_id,
denominator_result=1,
result=result
)
def calculate_max_block_adjacency(self, anchor, target):
"""
Calculates the Max Block Adjacency for `anchor` and `target` products.
:param anchor: Key for customer products.
:param target: Key for competitor products.
"""
kpi = Const.KPIs[('scrambles', 'ore ida')]
kpi_id = self.common.get_kpi_fk_by_kpi_name(kpi)
anchor_max_block, anchor_scene = self.get_max_block_from_products(Const.PRODUCTS[anchor])
target_max_block, target_scene = self.get_max_block_from_products(Const.PRODUCTS[target])
result = 0
if anchor_max_block and target_max_block and (anchor_scene == target_scene):
possible_adjacencies = itertools.product(anchor_max_block.nodes, target_max_block.nodes)
adj_graph = self.block.adj_graphs_by_scene
directed_edges = [list(val.edges) for key, val in adj_graph.items() if str(anchor_scene) in key][0]
complimentary_edges = [edge[::-1] for edge in directed_edges if edge[::-1] not in directed_edges]
all_edges = directed_edges + complimentary_edges
result = int(any(True for edge in possible_adjacencies if edge in all_edges))
if not result:
empty_matches = self.match_product_in_scene[
(self.match_product_in_scene['product_fk'] == 0)
& (self.match_product_in_scene['scene_fk'] == anchor_scene)]['scene_match_fk']
empty_edges = {match: [edge[1] for edge in all_edges if edge[0] == match] for match in empty_matches}
result = int(any([True for _, val in empty_edges.items()
if any([True for product in anchor_max_block.nodes if product in val])
and any([True for product in target_max_block.nodes if product in val])]))
self.common.write_to_db_result(
fk=kpi_id,
numerator_id=self.manufacturer_id,
numerator_result=result,
denominator_id=self.store_id,
denominator_result=1,
result=result
)
def calculate_adjacent_bay(self, anchor, target):
"""
Determines whether any products in `anchor` are located in the same scene
and same or adjacent bay as any products in `target`.
:param anchor: Key referencing the target product.
:param target: Key referencing the products being compared.
"""
kpi = Const.KPIs.get((anchor, target))
kpi_id = self.common.get_kpi_fk_by_kpi_name(kpi)
result = self.neighbors(anchor, target,
anchor_type='category', target_type='product', same_bay=False)
self.common.write_to_db_result(
fk=kpi_id,
numerator_id=self.manufacturer_id,
numerator_result=result,
denominator_id=self.store_id,
denominator_result=1,
result=result
)
def neighbors(self, anchor, target, anchor_type='product', target_type='category', same_bay=True):
"""
Determine whether any of the products in `anchor` and `target` are
in the same scene and same bay (if `same_bay` is True)
or in the same scene and same or adjacent bay (if `same_bay`is False).
:param anchor:
:param target:
:param anchor_type:
:param target_type:
:param same_bay: Indicates whether to count the same bay or also adjacent bays.
:return: Returns 1 if any products are neighbors, else 0.
"""
anchor_ids = None
target_ids = None
if anchor_type == 'product':
anchor_ids = self.get_product_id_from_product_name(Const.PRODUCTS[anchor])
elif anchor_type == 'category':
anchor_ids = self.get_category_id_from_category_name(Const.CATEGORIES[anchor])
if target_type == 'category':
target_ids = self.get_category_id_from_category_name(Const.CATEGORIES[target])
elif target_type == 'product':
target_ids = self.get_product_id_from_product_name(Const.PRODUCTS[target])
anchor_products = self.filter_df(self.mpis, anchor_type + '_fk', anchor_ids).drop_duplicates()
target_products = self.filter_df(self.mpis, target_type + '_fk', target_ids).drop_duplicates()
if same_bay:
neighbors = anchor_products.merge(target_products, how='inner', on=['scene_fk', 'bay_number'])
else:
try:
scenes = pd.concat([anchor_products['scene_fk'], target_products['scene_fk']]).unique()
probe_groups = pd.DataFrame()
for scene in scenes:
probe_groups = pd.concat([probe_groups, self.get_probe_groups(scene)])
anchor_products = anchor_products.merge(probe_groups, on=['scene_fk', 'product_fk'])
target_products = target_products.merge(probe_groups, on=['scene_fk', 'product_fk'])
except Exception:
Log.warning("Probe Group query failed.")
scene_neighbors = anchor_products.merge(target_products, on=['scene_fk'], suffixes=['', '_y'])
neighbors = scene_neighbors.apply(
lambda row: 1 if abs(int(row['bay_number']) - int(row['bay_number_y'])) < 2
and ('group_id' not in scene_neighbors.columns or row['group_id'] == row['group_id_y'])
else np.nan,
axis='columns'
).dropna()
return int(not neighbors.empty)
def get_brand_id_from_brand_name(self, brand_name):
# return self.all_products.set_index(['brand_name']).loc[brand_name, 'brand_fk'].iloc[0]
# return self.all_products.loc[self.all_products['brand_name'] == brand_name, 'brand_fk'].values[0]
return self.all_products[self.all_products['brand_name'] == brand_name].iloc[0].at['brand_fk']
def get_product_id_from_product_name(self, product_name):
return self.all_products.set_index(['product_english_name']).loc[product_name, 'product_fk'].unique()
def get_category_id_from_category_name(self, category_name):
return self.all_products.set_index(['category']).loc[category_name, 'category_fk'].unique()
def get_manufacturer_id_from_manufacturer_name(self, manufacturer_name):
return self.all_products.loc[self.all_products['manufacturer_name'] == manufacturer_name,
'manufacturer_fk'].values[0]
@staticmethod
def filter_df(df, column, values):
"""
Returns a subset of `df` whose values are in `values`.
:param df: DataFrame to filter
:param column: Column name to filter on
:param values: Values list to filter by
:return: The filtered DataFrame
"""
filtered = None
if hasattr(values, '__iter__'):
filtered = df[df[column].isin(values)]
elif isinstance(values, str):
filtered = df[df[column] == values]
return filtered
def get_max_block_from_products(self, products):
"""
Get max block based on list of product names.
:param products: List of product names.
:return: (Max block graph, scene_fk).
"""
product_ids = self.get_product_id_from_product_name(products)
blocks = self.block.network_x_block_together(
{'product_fk': product_ids},
additional={
'calculate_all_scenes': True,
'use_masking_only': True,
'minimum_facing_for_block': 2,
}
)
blocks.sort_values(by=['block_facings', 'facing_percentage'], ascending=False, inplace=True)
max_block = scene_id = None
if not blocks.empty and any(blocks['is_block']):
max_block = blocks['cluster'].iloc[0]
scene_id = blocks['scene_fk'].iloc[0]
return max_block, scene_id
def get_probe_groups(self, scene):
"""
Retrieves from probedata.stitching_probe_info the probe groups for all products in `scenes`.
:param scene: np.ndarray of scene_fk.
:return: dataframe containing product_fk, scene_fk, and group_id of products in `scenes`.
"""
query = """
SELECT DISTINCT mpip.product_fk, spi.group_id, ssi.scene_fk
FROM probedata.stitching_probe_info AS spi
LEFT JOIN probedata.stitching_scene_info AS ssi ON ssi.pk = spi.stitching_scene_info_fk
LEFT JOIN probedata.match_product_in_probe AS mpip ON mpip.probe_fk = spi.probe_fk
WHERE ssi.scene_fk = {};
""".format(scene)
return pd.read_sql_query(query, self.ps_data_provider.rds_conn.db)
def commit_results(self):
self.common.commit_results_data()