class HEINZCRToolBox:
LVL3_HEADERS = ['assortment_group_fk', 'assortment_fk', 'target', 'product_fk',
'in_store', 'kpi_fk_lvl1', 'kpi_fk_lvl2', 'kpi_fk_lvl3', 'group_target_date',
'assortment_super_group_fk']
LVL2_HEADERS = ['assortment_group_fk', 'assortment_fk', 'target', 'passes', 'total',
'kpi_fk_lvl1', 'kpi_fk_lvl2', 'group_target_date']
LVL1_HEADERS = ['assortment_group_fk', 'target', 'passes', 'total', 'kpi_fk_lvl1']
ASSORTMENT_FK = 'assortment_fk'
ASSORTMENT_GROUP_FK = 'assortment_group_fk'
ASSORTMENT_SUPER_GROUP_FK = 'assortment_super_group_fk'
BRAND_VARIENT = 'brand_varient'
NUMERATOR = 'numerator'
DENOMINATOR = 'denominator'
DISTRIBUTION_KPI = 'Distribution - SKU'
OOS_SKU_KPI = 'OOS - SKU'
OOS_KPI = 'OOS'
def __init__(self, data_provider, output):
self.output = output
self.data_provider = data_provider
self.common = CommonV2 # remove later
self.common_v2 = CommonV2(self.data_provider)
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.rds_conn = PSProjectConnector(self.project_name, DbUsers.CalculationEng)
self.kpi_results_queries = []
self.ps_data_provider = PsDataProvider(self.data_provider, self.output)
self.survey = Survey(self.data_provider, output=self.output, ps_data_provider=self.ps_data_provider,
common=self.common_v2)
self.store_sos_policies = self.ps_data_provider.get_store_policies()
self.labels = self.ps_data_provider.get_labels()
self.store_info = self.data_provider[Data.STORE_INFO]
self.store_info = self.ps_data_provider.get_ps_store_info(self.store_info)
self.country = self.store_info['country'].iloc[0]
self.current_date = datetime.now()
self.extra_spaces_template = pd.read_excel(Const.EXTRA_SPACES_RELEVANT_SUB_CATEGORIES_PATH)
self.store_targets = pd.read_excel(Const.STORE_TARGETS_PATH)
self.sub_category_weight = pd.read_excel(Const.SUB_CATEGORY_TARGET_PATH, sheetname='category_score')
self.kpi_weights = pd.read_excel(Const.SUB_CATEGORY_TARGET_PATH, sheetname='max_weight')
self.targets = self.ps_data_provider.get_kpi_external_targets()
self.store_assortment = PSAssortmentDataProvider(
self.data_provider).execute(policy_name=None)
self.supervisor_target = self.get_supervisor_target()
try:
self.sub_category_assortment = pd.merge(self.store_assortment,
self.all_products.loc[:, ['product_fk', 'sub_category',
'sub_category_fk']],
how='left', on='product_fk')
self.sub_category_assortment = \
self.sub_category_assortment[~self.sub_category_assortment['assortment_name'].str.contains(
'ASSORTMENT')]
self.sub_category_assortment = pd.merge(self.sub_category_assortment, self.sub_category_weight, how='left',
left_on='sub_category',
right_on='Category')
except KeyError:
self.sub_category_assortment = pd.DataFrame()
self.update_score_sub_category_weights()
try:
self.store_assortment_without_powerskus = \
self.store_assortment[self.store_assortment['assortment_name'].str.contains('ASSORTMENT')]
except KeyError:
self.store_assortment_without_powerskus = pd.DataFrame()
self.adherence_results = pd.DataFrame(columns=['product_fk', 'trax_average',
'suggested_price', 'into_interval', 'min_target', 'max_target',
'percent_range'])
self.extra_spaces_results = pd.DataFrame(
columns=['sub_category_fk', 'template_fk', 'count'])
self.powersku_scores = {}
self.powersku_empty = {}
self.powersku_bonus = {}
self.powersku_price = {}
self.powersku_sos = {}
def main_calculation(self, *args, **kwargs):
"""
This function calculates the KPI results.
"""
if self.scif.empty:
return
# these function must run first
# self.adherence_results = self.heinz_global_price_adherence(pd.read_excel(Const.PRICE_ADHERENCE_TEMPLATE_PATH,
# sheetname="Price Adherence"))
self.adherence_results = self.heinz_global_price_adherence(self.targets)
self.extra_spaces_results = self.heinz_global_extra_spaces()
self.set_relevant_sub_categories()
# this isn't relevant to the 'Perfect Score' calculation
self.heinz_global_distribution_per_category()
self.calculate_assortment()
self.calculate_powersku_assortment()
self.main_sos_calculation()
self.calculate_powersku_price_adherence()
self.calculate_perfect_store_extra_spaces()
self.check_bonus_question()
self.calculate_perfect_sub_category()
def calculate_assortment(self):
if self.store_assortment_without_powerskus.empty:
return
products_in_store = self.scif[self.scif['facings'] > 0]['product_fk'].unique().tolist()
pass_count = 0
total_kpi_fk = self.common_v2.get_kpi_fk_by_kpi_type('Distribution')
identifier_dict = self.common_v2.get_dictionary(kpi_fk=total_kpi_fk)
oos_kpi_fk = self.common_v2.get_kpi_fk_by_kpi_type('OOS')
oos_identifier_dict = self.common_v2.get_dictionary(kpi_fk=oos_kpi_fk)
for row in self.store_assortment_without_powerskus.itertuples():
result = 0
if row.product_fk in products_in_store:
result = 1
pass_count += 1
sku_kpi_fk = self.common_v2.get_kpi_fk_by_kpi_type('Distribution - SKU')
self.common_v2.write_to_db_result(sku_kpi_fk, numerator_id=row.product_fk, denominator_id=row.assortment_fk,
result=result, identifier_parent=identifier_dict, should_enter=True)
oos_result = 0 if result else 1
oos_sku_kpi_fk = self.common_v2.get_kpi_fk_by_kpi_type('OOS - SKU')
self.common_v2.write_to_db_result(oos_sku_kpi_fk, numerator_id=row.product_fk,
denominator_id=row.assortment_fk,
result=oos_result, identifier_parent=oos_identifier_dict,
should_enter=True)
number_of_products_in_assortment = len(self.store_assortment_without_powerskus)
if number_of_products_in_assortment:
total_result = (pass_count / float(number_of_products_in_assortment)) * 100
oos_products = number_of_products_in_assortment - pass_count
oos_result = (oos_products / float(number_of_products_in_assortment)) * 100
else:
total_result = 0
oos_products = number_of_products_in_assortment
oos_result = number_of_products_in_assortment
self.common_v2.write_to_db_result(total_kpi_fk, numerator_id=Const.OWN_MANUFACTURER_FK,
denominator_id=self.store_id,
numerator_result=pass_count,
denominator_result=number_of_products_in_assortment,
result=total_result, identifier_result=identifier_dict)
self.common_v2.write_to_db_result(oos_kpi_fk, numerator_id=Const.OWN_MANUFACTURER_FK,
denominator_id=self.store_id,
numerator_result=oos_products,
denominator_result=number_of_products_in_assortment,
result=oos_result, identifier_result=oos_identifier_dict)
def calculate_powersku_assortment(self):
if self.sub_category_assortment.empty:
return 0
sub_category_kpi_fk = self.common_v2.get_kpi_fk_by_kpi_type(Const.POWER_SKU_SUB_CATEGORY)
sku_kpi_fk = self.common_v2.get_kpi_fk_by_kpi_type(Const.POWER_SKU)
target_kpi_weight = float(
self.kpi_weights['Score'][self.kpi_weights['KPIs'] == Const.KPI_WEIGHTS['POWERSKU']].iloc[
0])
kpi_weight = self.get_kpi_weight('POWERSKU')
products_in_session = self.scif[self.scif['facings'] > 0]['product_fk'].unique().tolist()
self.sub_category_assortment['in_session'] = \
self.sub_category_assortment.loc[:, 'product_fk'].isin(products_in_session)
# save PowerSKU results at SKU level
for sku in self.sub_category_assortment[
['product_fk', 'sub_category_fk', 'in_session', 'sub_category']].itertuples():
parent_dict = self.common_v2.get_dictionary(
kpi_fk=sub_category_kpi_fk, sub_category_fk=sku.sub_category_fk)
relevant_sub_category_df = self.sub_category_assortment[
self.sub_category_assortment['sub_category'] == sku.sub_category]
if relevant_sub_category_df.empty:
sub_category_count = 0
else:
sub_category_count = len(relevant_sub_category_df)
result = 1 if sku.in_session else 0
score = result * (target_kpi_weight / float(sub_category_count))
self.common_v2.write_to_db_result(sku_kpi_fk, numerator_id=sku.product_fk,
denominator_id=sku.sub_category_fk, score=score,
result=result, identifier_parent=parent_dict, should_enter=True)
# save PowerSKU results at sub_category level
aggregated_results = self.sub_category_assortment.groupby('sub_category_fk').agg(
{'in_session': 'sum', 'product_fk': 'count'}).reset_index().rename(
columns={'product_fk': 'product_count'})
aggregated_results['percent_complete'] = \
aggregated_results.loc[:, 'in_session'] / aggregated_results.loc[:, 'product_count']
aggregated_results['result'] = aggregated_results['percent_complete']
for sub_category in aggregated_results.itertuples():
identifier_dict = self.common_v2.get_dictionary(kpi_fk=sub_category_kpi_fk,
sub_category_fk=sub_category.sub_category_fk)
result = sub_category.result
score = result * kpi_weight
self.powersku_scores[sub_category.sub_category_fk] = score
self.common_v2.write_to_db_result(sub_category_kpi_fk, numerator_id=sub_category.sub_category_fk,
denominator_id=self.store_id,
identifier_parent=sub_category.sub_category_fk,
identifier_result=identifier_dict, result=result * 100, score=score,
weight=target_kpi_weight, target=target_kpi_weight,
should_enter=True)
def heinz_global_distribution_per_category(self):
relevant_stores = pd.DataFrame(columns=self.store_sos_policies.columns)
for row in self.store_sos_policies.itertuples():
policies = json.loads(row.store_policy)
df = self.store_info
for key, value in policies.items():
try:
df_1 = df[df[key].isin(value)]
except KeyError:
continue
if not df_1.empty:
stores = self.store_sos_policies[(self.store_sos_policies['store_policy'] == row.store_policy.encode('utf-8'))
& (
self.store_sos_policies[
'target_validity_start_date'] <= datetime.date(
self.current_date))]
if stores.empty:
relevant_stores = stores
else:
relevant_stores = relevant_stores.append(stores, ignore_index=True)
relevant_stores = relevant_stores.drop_duplicates(subset=['kpi', 'sku_name', 'target', 'sos_policy'],
keep='last')
for row in relevant_stores.itertuples():
sos_policy = json.loads(row.sos_policy)
numerator_key = sos_policy[self.NUMERATOR].keys()[0]
denominator_key = sos_policy[self.DENOMINATOR].keys()[0]
numerator_val = sos_policy[self.NUMERATOR][numerator_key]
denominator_val = sos_policy[self.DENOMINATOR][denominator_key]
target = row.target * 100
if numerator_key == 'manufacturer':
numerator_key = numerator_key + '_name'
if denominator_key == 'sub_category' \
and denominator_val.lower() != 'all' \
and json.loads(row.store_policy).get('store_type') \
and len(json.loads(row.store_policy).get('store_type')) == 1:
try:
denominator_id = self.all_products[self.all_products[denominator_key] == denominator_val][
denominator_key + '_fk'].values[0]
numerator_id = self.all_products[self.all_products[numerator_key] == numerator_val][
numerator_key.split('_')[0] + '_fk'].values[0]
# self.common.write_to_db_result_new_tables(fk=12, numerator_id=numerator_id,
# numerator_result=None,
# denominator_id=denominator_id,
# denominator_result=None,
# result=target)
self.common_v2.write_to_db_result(fk=12, numerator_id=numerator_id, numerator_result=None,
denominator_id=denominator_id, denominator_result=None,
result=target)
except Exception as e:
Log.warning(denominator_key + ' - - ' + denominator_val)
def calculate_perfect_store(self):
pass
def calculate_perfect_sub_category(self):
kpi_fk = self.common_v2.get_kpi_fk_by_kpi_type(Const.PERFECT_STORE_SUB_CATEGORY)
parent_kpi = self.common_v2.get_kpi_fk_by_kpi_type(Const.PERFECT_STORE)
total_score = 0
sub_category_fk_list = []
kpi_type_dict_scores = [self.powersku_scores, self.powersku_empty, self.powersku_price,
self.powersku_sos]
for kpi_dict in kpi_type_dict_scores:
sub_category_fk_list.extend(kpi_dict.keys())
kpi_weight_perfect_store = 0
if self.country in self.sub_category_weight.columns.to_list():
kpi_weight_perfect_store = self.sub_category_weight[self.country][
self.sub_category_weight['Category'] == Const.PERFECT_STORE_KPI_WEIGHT]
if not kpi_weight_perfect_store.empty:
kpi_weight_perfect_store = kpi_weight_perfect_store.iloc[0]
unique_sub_cat_fks = list(dict.fromkeys(sub_category_fk_list))
sub_category_fks = self.sub_category_weight.sub_category_fk.unique().tolist()
relevant_sub_cat_list = [x for x in sub_category_fks if str(x) != 'nan']
# relevant_sub_cat_list = self.sub_category_assortment['sub_category_fk'][
# self.sub_category_assortment['Category'] != pd.np.nan].unique().tolist()
for sub_cat_fk in unique_sub_cat_fks:
if sub_cat_fk in relevant_sub_cat_list:
bonus_score = 0
try:
bonus_score = self.powersku_bonus[sub_cat_fk]
except:
pass
sub_cat_weight = self.get_weight(sub_cat_fk)
sub_cat_score = self.calculate_sub_category_sum(kpi_type_dict_scores, sub_cat_fk)
result = sub_cat_score
score = (result * sub_cat_weight) + bonus_score
total_score += score
self.common_v2.write_to_db_result(kpi_fk, numerator_id=sub_cat_fk,
denominator_id=self.store_id,
result=result, score=score,
identifier_parent=parent_kpi,
identifier_result=sub_cat_fk,
weight=sub_cat_weight * 100,
should_enter=True)
self.common_v2.write_to_db_result(parent_kpi, numerator_id=Const.OWN_MANUFACTURER_FK,
denominator_id=self.store_id,
result=total_score, score=total_score,
identifier_result=parent_kpi,
target=kpi_weight_perfect_store,
should_enter=True)
def main_sos_calculation(self):
relevant_stores = pd.DataFrame(columns=self.store_sos_policies.columns)
for row in self.store_sos_policies.itertuples():
policies = json.loads(row.store_policy)
df = self.store_info
for key, value in policies.items():
try:
if key != 'additional_attribute_3':
df1 = df[df[key].isin(value)]
except KeyError:
continue
if not df1.empty:
stores = \
self.store_sos_policies[(self.store_sos_policies['store_policy'].str.encode(
'utf-8') == row.store_policy.encode('utf-8')) &
(self.store_sos_policies['target_validity_start_date'] <= datetime.date(
self.current_date))]
if stores.empty:
relevant_stores = stores
else:
relevant_stores = relevant_stores.append(stores, ignore_index=True)
relevant_stores = relevant_stores.drop_duplicates(subset=['kpi', 'sku_name', 'target', 'sos_policy'],
keep='last')
results_df = pd.DataFrame(columns=['sub_category', 'sub_category_fk', 'score'])
sos_sub_category_kpi_fk = self.common_v2.get_kpi_fk_by_kpi_type(Const.SOS_SUB_CATEGORY)
for row in relevant_stores.itertuples():
sos_policy = json.loads(row.sos_policy)
numerator_key = sos_policy[self.NUMERATOR].keys()[0]
denominator_key = sos_policy[self.DENOMINATOR].keys()[0]
numerator_val = sos_policy[self.NUMERATOR][numerator_key]
denominator_val = sos_policy[self.DENOMINATOR][denominator_key]
json_policy = json.loads(row.store_policy)
kpi_fk = row.kpi
# This is to assign the KPI to SOS_manufacturer_category_GLOBAL
if json_policy.get('store_type') and len(json_policy.get('store_type')) > 1:
kpi_fk = 8
if numerator_key == 'manufacturer':
numerator_key = numerator_key + '_name'
# we need to include 'Philadelphia' as a manufacturer for all countries EXCEPT Chile
if self.country == 'Chile':
numerator_values = [numerator_val]
else:
numerator_values = [numerator_val, 'Philadelphia']
else:
# if the numerator isn't 'manufacturer', we just need to convert the value to a list
numerator_values = [numerator_val]
if denominator_key == 'sub_category':
include_stacking_list = ['Nuts', 'DRY CHEESE', 'IWSN', 'Shredded', 'SNACK']
if denominator_val in include_stacking_list:
facings_field = 'facings'
else:
facings_field = 'facings_ign_stack'
else:
facings_field = 'facings_ign_stack'
if denominator_key == 'sub_category' and denominator_val.lower() == 'all':
# Here we are talkin on a KPI when the target have no denominator,
# the calculation should be done on Numerator only
numerator = self.scif[(self.scif[numerator_key] == numerator_val) &
(self.scif['location_type'] == 'Primary Shelf')
][facings_field].sum()
kpi_fk = 9
denominator = None
denominator_id = None
else:
numerator = self.scif[(self.scif[numerator_key].isin(numerator_values)) &
(self.scif[denominator_key] == denominator_val) &
(self.scif['location_type'] == 'Primary Shelf')][facings_field].sum()
denominator = self.scif[(self.scif[denominator_key] == denominator_val) &
(self.scif['location_type'] == 'Primary Shelf')][facings_field].sum()
try:
if denominator is not None:
denominator_id = self.all_products[self.all_products[denominator_key] == denominator_val][
denominator_key + '_fk'].values[0]
if numerator is not None:
numerator_id = self.all_products[self.all_products[numerator_key] == numerator_val][
numerator_key.split('_')[0] + '_fk'].values[0]
sos = 0
if numerator and denominator:
sos = np.divide(float(numerator), float(denominator)) * 100
score = 0
target = row.target * 100
if sos >= target:
score = 100
identifier_parent = None
should_enter = False
if denominator_key == 'sub_category' and kpi_fk == row.kpi:
# if this a sub_category result, save it to the results_df for 'Perfect Store' store
results_df.loc[len(results_df)] = [denominator_val, denominator_id, score / 100]
identifier_parent = self.common_v2.get_dictionary(kpi_fk=sos_sub_category_kpi_fk,
sub_category_fk=denominator_id)
should_enter = True
manufacturer = None
self.common_v2.write_to_db_result(kpi_fk, numerator_id=numerator_id, numerator_result=numerator,
denominator_id=denominator_id, denominator_result=denominator,
result=target, score=sos, target=target,
score_after_actions=manufacturer, identifier_parent=identifier_parent,
should_enter=should_enter)
except Exception as e:
Log.warning(denominator_key + ' - - ' + denominator_val)
# if there are no sub_category sos results, there's no perfect store information to be saved
if len(results_df) == 0:
return 0
# save aggregated results for each sub category
kpi_weight = self.get_kpi_weight('SOS')
for row in results_df.itertuples():
identifier_result = \
self.common_v2.get_dictionary(kpi_fk=sos_sub_category_kpi_fk,
sub_category_fk=row.sub_category_fk)
# sub_cat_weight = self.get_weight(row.sub_category_fk)
result = row.score
score = result * kpi_weight
self.powersku_sos[row.sub_category_fk] = score
# limit results so that aggregated results can only add up to 3
self.common_v2.write_to_db_result(sos_sub_category_kpi_fk,
numerator_id=row.sub_category_fk,
denominator_id=self.store_id,
result=row.score, score=score,
identifier_parent=row.sub_category_fk,
identifier_result=identifier_result,
weight=kpi_weight,
target=kpi_weight,
should_enter=True)
def calculate_powersku_price_adherence(self):
adherence_kpi_fk = self.common_v2.get_kpi_fk_by_kpi_type(Const.POWER_SKU_PRICE_ADHERENCE)
adherence_sub_category_kpi_fk = \
self.common_v2.get_kpi_fk_by_kpi_type(Const.POWER_SKU_PRICE_ADHERENCE_SUB_CATEGORY)
if self.sub_category_assortment.empty:
return False
results = pd.merge(self.sub_category_assortment,
self.adherence_results, how='left', on='product_fk')
results['into_interval'].fillna(0, inplace=True)
for row in results.itertuples():
parent_dict = self.common_v2.get_dictionary(kpi_fk=adherence_sub_category_kpi_fk,
sub_category_fk=row.sub_category_fk)
score_value = 'Not Present'
in_session = row.in_session
if in_session:
if not pd.isna(row.trax_average) and row.suggested_price:
price_in_interval = 1 if row.into_interval == 1 else 0
if price_in_interval == 1:
score_value = 'Pass'
else:
score_value = 'Fail'
else:
score_value = 'No Price'
score = Const.PRESENCE_PRICE_VALUES[score_value]
self.common_v2.write_to_db_result(adherence_kpi_fk, numerator_id=row.product_fk,
denominator_id=row.sub_category_fk, result=row.trax_average,
score=score, target=row.suggested_price, numerator_result=row.min_target,
denominator_result=row.max_target,
weight=row.percent_range,
identifier_parent=parent_dict, should_enter=True)
aggregated_results = results.groupby('sub_category_fk').agg(
{'into_interval': 'sum', 'product_fk': 'count'}).reset_index().rename(
columns={'product_fk': 'product_count'})
aggregated_results['percent_complete'] = \
aggregated_results.loc[:, 'into_interval'] / aggregated_results.loc[:, 'product_count']
for row in aggregated_results.itertuples():
identifier_result = self.common_v2.get_dictionary(kpi_fk=adherence_sub_category_kpi_fk,
sub_category_fk=row.sub_category_fk)
kpi_weight = self.get_kpi_weight('PRICE')
result = row.percent_complete
score = result * kpi_weight
self.powersku_price[row.sub_category_fk] = score
self.common_v2.write_to_db_result(adherence_sub_category_kpi_fk, numerator_id=row.sub_category_fk,
denominator_id=self.store_id, result=result, score=score,
numerator_result=row.into_interval, denominator_result=row.product_count,
identifier_parent=row.sub_category_fk,
identifier_result=identifier_result,
weight=kpi_weight, target=kpi_weight,
should_enter=True)
def heinz_global_price_adherence(self, config_df):
config_df = config_df.sort_values(by=["received_time"], ascending=False).drop_duplicates(
subset=['start_date', 'end_date', 'ean_code', 'store_type'], keep="first")
if config_df.empty:
Log.warning("No external_targets data found - Price Adherence will not be calculated")
return self.adherence_results
self.match_product_in_scene.loc[self.match_product_in_scene['price'].isna(), 'price'] = \
self.match_product_in_scene.loc[self.match_product_in_scene['price'].isna(), 'promotion_price']
# =============== remove after updating logic to support promotional pricing ===============
results_df = self.adherence_results
my_config_df = \
config_df[config_df['store_type'].str.encode('utf-8') == self.store_info.store_type[0].encode('utf-8')]
products_in_session = self.scif['product_ean_code'].unique().tolist()
products_in_session = [ean for ean in products_in_session if ean is not pd.np.nan and ean is not None]
my_config_df = my_config_df[my_config_df['ean_code'].isin(products_in_session)]
for row in my_config_df.itertuples():
product_pk = \
self.all_products[self.all_products['product_ean_code']
== row.ean_code]['product_fk'].iloc[0]
mpisc_df_price = \
self.match_product_in_scene[(self.match_product_in_scene['product_fk'] == product_pk) |
(self.match_product_in_scene[
'substitution_product_fk'] == product_pk)]['price']
try:
suggested_price = float(row.suggested_price)
except Exception as e:
Log.error("Product with ean_code {} is not in the configuration file for customer type {}"
.format(row.ean_code, self.store_info.store_type[0].encode('utf-8')))
break
percentage_weight = int(row.percentage_weight)
upper_percentage = (100 + percentage_weight) / float(100)
lower_percentage = (100 - percentage_weight) / float(100)
min_price = suggested_price * lower_percentage
max_price = suggested_price * upper_percentage
percentage_sku = percentage_weight
into_interval = 0
prices_sum = 0
count = 0
trax_average = None
for price in mpisc_df_price:
if price and pd.notna(price):
prices_sum += price
count += 1
if prices_sum > 0:
trax_average = prices_sum / count
into_interval = 0
if not np.isnan(suggested_price):
if min_price <= trax_average <= max_price:
into_interval = 100
results_df.loc[len(results_df)] = [product_pk, trax_average,
suggested_price, into_interval / 100, min_price, max_price,
percentage_sku]
self.common_v2.write_to_db_result(10, numerator_id=product_pk,
numerator_result=suggested_price,
denominator_id=product_pk,
denominator_result=trax_average,
result=row.percentage_weight,
score=into_interval)
if trax_average:
mark_up = (np.divide(np.divide(float(trax_average), float(1.13)),
float(suggested_price)) - 1) * 100
self.common_v2.write_to_db_result(11, numerator_id=product_pk,
numerator_result=suggested_price,
denominator_id=product_pk,
denominator_result=trax_average,
score=mark_up,
result=mark_up)
return results_df
def calculate_perfect_store_extra_spaces(self):
extra_spaces_kpi_fk = self.common_v2.get_kpi_fk_by_kpi_type(
Const.PERFECT_STORE_EXTRA_SPACES_SUB_CATEGORY)
sub_cats_for_store = self.relevant_sub_categories
if self.extra_spaces_results.empty:
pass
try:
relevant_sub_categories = [x.strip() for x in self.extra_spaces_template[
self.extra_spaces_template['country'].str.encode('utf-8') == self.country.encode('utf-8')][
'sub_category'].iloc[0].split(',')]
except IndexError:
Log.warning(
'No relevant sub_categories for the Extra Spaces KPI found for the following country: {}'.format(
self.country))
self.extra_spaces_results = pd.merge(self.extra_spaces_results,
self.all_products.loc[:, [
'sub_category_fk',
'sub_category']].dropna().drop_duplicates(),
how='left', on='sub_category_fk')
relevant_extra_spaces = \
self.extra_spaces_results[self.extra_spaces_results['sub_category'].isin(
relevant_sub_categories)]
kpi_weight = self.get_kpi_weight('EXTRA')
for row in relevant_extra_spaces.itertuples():
self.powersku_empty[row.sub_category_fk] = 1 * kpi_weight
score = result = 1
if row.sub_category_fk in sub_cats_for_store:
sub_cats_for_store.remove(row.sub_category_fk)
self.common_v2.write_to_db_result(extra_spaces_kpi_fk, numerator_id=row.sub_category_fk,
denominator_id=row.template_fk, result=result, score=score,
identifier_parent=row.sub_category_fk,
target=1, should_enter=True)
for sub_cat_fk in sub_cats_for_store:
result = score = 0
self.powersku_empty[sub_cat_fk] = 0
self.common_v2.write_to_db_result(extra_spaces_kpi_fk, numerator_id=sub_cat_fk,
denominator_id=0, result=result, score=score,
identifier_parent=sub_cat_fk,
target=1, should_enter=True)
def heinz_global_extra_spaces(self):
try:
supervisor = self.store_info['additional_attribute_3'][0]
store_target = -1
# for row in self.store_sos_policies.itertuples():
# policies = json.loads(row.store_policy)
# for key, value in policies.items():
# try:
# if key == 'additional_attribute_3' and value[0] == supervisor:
# store_target = row.target
# break
# except KeyError:
# continue
for row in self.supervisor_target.itertuples():
try:
if row.supervisor == supervisor:
store_target = row.target
break
except:
continue
except Exception as e:
Log.error("Supervisor target is not configured for the extra spaces report ")
raise e
results_df = self.extra_spaces_results
# limit to only secondary scenes
relevant_scif = self.scif[(self.scif['location_type_fk'] == float(2)) &
(self.scif['facings'] > 0)]
if relevant_scif.empty:
return results_df
# aggregate facings for every scene/sub_category combination in the visit
relevant_scif = \
relevant_scif.groupby(['scene_fk', 'template_fk', 'sub_category_fk'], as_index=False)['facings'].sum()
# sort sub_categories by number of facings, largest first
relevant_scif = relevant_scif.sort_values(['facings'], ascending=False)
# drop all but the sub_category with the largest number of facings for each scene
relevant_scif = relevant_scif.drop_duplicates(subset=['scene_fk'], keep='first')
for row in relevant_scif.itertuples():
results_df.loc[len(results_df)] = [row.sub_category_fk, row.template_fk, row.facings]
self.common_v2.write_to_db_result(13, numerator_id=row.template_fk,
numerator_result=row.facings,
denominator_id=row.sub_category_fk,
denominator_result=row.facings,
context_id=row.scene_fk,
result=store_target)
return results_df
def check_bonus_question(self):
bonus_kpi_fk = self.common_v2.get_kpi_fk_by_kpi_type(Const.BONUS_QUESTION_SUB_CATEGORY)
bonus_weight = self.kpi_weights['Score'][self.kpi_weights['KPIs'] == Const.KPI_WEIGHTS['Bonus']].iloc[0]
sub_category_fks = self.sub_category_weight.sub_category_fk.unique().tolist()
sub_category_fks = [x for x in sub_category_fks if str(x) != 'nan']
if self.survey.check_survey_answer(('question_fk', Const.BONUS_QUESTION_FK), 'Yes,yes,si,Si'):
result = 1
else:
result = 0
for sub_cat_fk in sub_category_fks:
sub_cat_weight = self.get_weight(sub_cat_fk)
score = result * sub_cat_weight
target_weight = bonus_weight * sub_cat_weight
self.powersku_bonus[sub_cat_fk] = score
self.common_v2.write_to_db_result(bonus_kpi_fk, numerator_id=sub_cat_fk,
denominator_id=self.store_id,
result=result, score=score, identifier_parent=sub_cat_fk,
weight=target_weight, target=target_weight,
should_enter=True)
def commit_results_data(self):
self.common_v2.commit_results_data()
def update_score_sub_category_weights(self):
all_sub_category_fks = self.all_products[['sub_category', 'sub_category_fk']].drop_duplicates()
self.sub_category_weight = pd.merge(self.sub_category_weight, all_sub_category_fks, left_on='Category',
right_on='sub_category',
how='left')
def get_weight(self, sub_category_fk):
weight_value = 0
if self.country in self.sub_category_weight.columns.to_list():
weight_df = self.sub_category_weight[self.country][
(self.sub_category_weight.sub_category_fk == sub_category_fk)]
if weight_df.empty:
return 0
weight_value = weight_df.iloc[0]
if pd.isna(weight_value):
weight_value = 0
weight = weight_value * 0.01
return weight
def get_kpi_weight(self, kpi_name):
weight = self.kpi_weights['Score'][self.kpi_weights['KPIs'] == Const.KPI_WEIGHTS[kpi_name]].iloc[0]
return weight
def get_supervisor_target(self):
supervisor_target = self.targets[self.targets['kpi_type'] == 'Extra Spaces']
return supervisor_target
def calculate_sub_category_sum(self, dict_list, sub_cat_fk):
total_score = 0
for item in dict_list:
try:
total_score += item[sub_cat_fk]
except:
pass
return total_score
def set_relevant_sub_categories(self):
if self.country in self.sub_category_weight.columns.to_list():
df = self.sub_category_weight[['Category', 'sub_category_fk', self.country]].dropna()
self.relevant_sub_categories = df.sub_category_fk.to_list()
else:
self.relevant_sub_categories = []
class LIBERTYToolBox:
def __init__(self, data_provider, output, common_db):
self.output = output
self.data_provider = data_provider
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.ps_data_provider = PsDataProvider(self.data_provider, self.output)
self.store_info = self.ps_data_provider.get_ps_store_info(
self.data_provider[Data.STORE_INFO])
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.scif = self.scif[self.scif['product_type'] != "Irrelevant"]
self.result_values = self.ps_data_provider.get_result_values()
self.templates = self.read_templates()
self.common_db = common_db
self.survey = Survey(self.data_provider, output=self.output, ps_data_provider=self.ps_data_provider,
common=self.common_db)
self.manufacturer_fk = Const.MANUFACTURER_FK
self.region = self.store_info['region_name'].iloc[0]
self.store_type = self.store_info['store_type'].iloc[0]
self.retailer = self.store_info['retailer_name'].iloc[0]
self.branch = self.store_info['branch_name'].iloc[0]
self.additional_attribute_4 = self.store_info['additional_attribute_4'].iloc[0]
self.additional_attribute_7 = self.store_info['additional_attribute_7'].iloc[0]
self.body_armor_delivered = self.get_body_armor_delivery_status()
self.convert_base_size_and_multi_pack()
def read_templates(self):
templates = {}
for sheet in Const.SHEETS:
converters = None
if sheet == Const.MINIMUM_FACINGS:
converters = {Const.BASE_SIZE_MIN: self.convert_base_size_values,
Const.BASE_SIZE_MAX: self.convert_base_size_values}
templates[sheet] = \
pd.read_excel(Const.TEMPLATE_PATH, sheet_name=sheet,
converters=converters).fillna('')
return templates
# main functions:
def main_calculation(self, *args, **kwargs):
"""
This function gets all the scene results from the SceneKPI, after that calculates every session's KPI,
and in the end it calls "filter results" to choose every KPI and scene and write the results in DB.
"""
if self.region != 'Liberty':
return
red_score = 0
main_template = self.templates[Const.KPIS]
for i, main_line in main_template.iterrows():
relevant_store_types = self.does_exist(main_line, Const.ADDITIONAL_ATTRIBUTE_7)
if relevant_store_types and self.additional_attribute_7 not in relevant_store_types:
continue
result = self.calculate_main_kpi(main_line)
if result:
red_score += main_line[Const.WEIGHT] * result
if len(self.common_db.kpi_results) > 0:
kpi_fk = self.common_db.get_kpi_fk_by_kpi_type(Const.RED_SCORE_PARENT)
self.common_db.write_to_db_result(kpi_fk, numerator_id=1, denominator_id=self.store_id, result=red_score,
identifier_result=Const.RED_SCORE_PARENT, should_enter=True)
return
def calculate_main_kpi(self, main_line):
"""
This function gets a line from the main_sheet, transfers it to the match function, and checks all of the
KPIs in the same name in the match sheet.
:param main_line: series from the template of the main_sheet.
"""
relevant_scif = self.scif
scene_types = self.does_exist(main_line, Const.SCENE_TYPE)
if scene_types:
relevant_scif = relevant_scif[relevant_scif['template_name'].isin(scene_types)]
excluded_scene_types = self.does_exist(main_line, Const.EXCLUDED_SCENE_TYPE)
if excluded_scene_types:
relevant_scif = relevant_scif[~relevant_scif['template_name'].isin(
excluded_scene_types)]
template_groups = self.does_exist(main_line, Const.TEMPLATE_GROUP)
if template_groups:
relevant_scif = relevant_scif[relevant_scif['template_group'].isin(template_groups)]
result = self.calculate_kpi_by_type(main_line, relevant_scif)
return result
def calculate_kpi_by_type(self, main_line, relevant_scif):
"""
the function calculates all the kpis
:param main_line: one kpi line from the main template
:param relevant_scif:
:return: boolean, but it can be None if we want not to write it in DB
"""
kpi_type = main_line[Const.KPI_TYPE]
relevant_template = self.templates[kpi_type]
kpi_line = relevant_template[relevant_template[Const.KPI_NAME]
== main_line[Const.KPI_NAME]].iloc[0]
kpi_function = self.get_kpi_function(kpi_type)
weight = main_line[Const.WEIGHT]
if relevant_scif.empty:
result = 0
else:
result = kpi_function(kpi_line, relevant_scif, weight)
result_type_fk = self.ps_data_provider.get_pks_of_result(
Const.PASS) if result > 0 else self.ps_data_provider.get_pks_of_result(Const.FAIL)
if self.does_exist(main_line, Const.PARENT_KPI_NAME):
# if this is a child KPI, we do not need to return a value to the Total Score KPI
return 0
else: # normal behavior for when this isn't a child KPI
kpi_name = kpi_line[Const.KPI_NAME] + Const.LIBERTY
kpi_fk = self.common_db.get_kpi_fk_by_kpi_type(kpi_name)
self.common_db.write_to_db_result(kpi_fk, numerator_id=self.manufacturer_fk, numerator_result=0,
denominator_id=self.store_id, denominator_result=0, weight=weight,
result=result_type_fk, identifier_parent=Const.RED_SCORE_PARENT,
identifier_result=kpi_name, should_enter=True)
return result
# SOS functions
def calculate_sos(self, kpi_line, relevant_scif, weight):
market_share_required = self.does_exist(kpi_line, Const.MARKET_SHARE_TARGET)
if market_share_required:
market_share_target = self.get_market_share_target()
else:
market_share_target = 0
if not market_share_target:
market_share_target = 0
denominator_facings = relevant_scif['facings'].sum()
filtered_scif = relevant_scif.copy()
manufacturer = self.does_exist(kpi_line, Const.MANUFACTURER)
if manufacturer:
filtered_scif = relevant_scif[relevant_scif['manufacturer_name'].isin(manufacturer)]
liberty_truck = self.does_exist(kpi_line, Const.LIBERTY_KEY_MANUFACTURER)
if liberty_truck:
liberty_truck_scif = relevant_scif[relevant_scif[Const.LIBERTY_KEY_MANUFACTURER].isin(
liberty_truck)]
filtered_scif = filtered_scif.append(liberty_truck_scif, sort=False).drop_duplicates()
if self.does_exist(kpi_line, Const.INCLUDE_BODY_ARMOR) and self.body_armor_delivered:
body_armor_scif = relevant_scif[relevant_scif['brand_fk'] == Const.BODY_ARMOR_BRAND_FK]
filtered_scif = filtered_scif.append(body_armor_scif, sort=False)
numerator_facings = filtered_scif['facings'].sum()
sos_value = numerator_facings / float(denominator_facings)
result = 1 if sos_value > market_share_target else 0
parent_kpi_name = kpi_line[Const.KPI_NAME] + Const.LIBERTY
kpi_fk = self.common_db.get_kpi_fk_by_kpi_type(parent_kpi_name + Const.DRILLDOWN)
self.common_db.write_to_db_result(kpi_fk, numerator_id=self.manufacturer_fk, numerator_result=numerator_facings,
denominator_id=self.store_id, denominator_result=denominator_facings,
weight=weight, score=result * weight,
result=sos_value * 100, target=market_share_target * 100,
identifier_parent=parent_kpi_name, should_enter=True)
return result
# Availability functions
def calculate_availability(self, kpi_line, relevant_scif, weight):
survey_question_skus_required = self.does_exist(
kpi_line, Const.SURVEY_QUESTION_SKUS_REQUIRED)
if survey_question_skus_required:
survey_question_skus, secondary_survey_question_skus = \
self.get_relevant_product_assortment_by_kpi_name(kpi_line[Const.KPI_NAME])
unique_skus = \
relevant_scif[relevant_scif['product_fk'].isin(
survey_question_skus)]['product_fk'].unique().tolist()
if secondary_survey_question_skus:
secondary_unique_skus = \
relevant_scif[relevant_scif['product_fk'].isin(secondary_survey_question_skus)][
'product_fk'].unique().tolist()
else:
secondary_unique_skus = None
else:
secondary_unique_skus = None
manufacturer = self.does_exist(kpi_line, Const.MANUFACTURER)
if manufacturer:
relevant_scif = relevant_scif[relevant_scif['manufacturer_name'].isin(manufacturer)]
brand = self.does_exist(kpi_line, Const.BRAND)
if brand:
relevant_scif = relevant_scif[relevant_scif['brand_name'].isin(brand)]
category = self.does_exist(kpi_line, Const.CATEGORY)
if category:
relevant_scif = relevant_scif[relevant_scif['category'].isin(category)]
excluded_brand = self.does_exist(kpi_line, Const.EXCLUDED_BRAND)
if excluded_brand:
relevant_scif = relevant_scif[~relevant_scif['brand_name'].isin(excluded_brand)]
excluded_sku = self.does_exist(kpi_line, Const.EXCLUDED_SKU)
if excluded_sku:
relevant_scif = relevant_scif[~relevant_scif['product_name'].isin(excluded_sku)]
unique_skus = relevant_scif['product_fk'].unique().tolist()
length_of_unique_skus = len(unique_skus)
minimum_number_of_skus = kpi_line[Const.MINIMUM_NUMBER_OF_SKUS]
if length_of_unique_skus >= minimum_number_of_skus:
if secondary_unique_skus:
length_of_unique_skus = len(secondary_unique_skus)
minimum_number_of_skus = kpi_line[Const.SECONDARY_MINIMUM_NUMBER_OF_SKUS]
result = 1 if length_of_unique_skus > minimum_number_of_skus else 0
else:
result = 1
else:
result = 0
parent_kpi_name = kpi_line[Const.KPI_NAME] + Const.LIBERTY
kpi_fk = self.common_db.get_kpi_fk_by_kpi_type(parent_kpi_name + Const.DRILLDOWN)
self.common_db.write_to_db_result(kpi_fk, numerator_id=self.manufacturer_fk, numerator_result=0,
denominator_id=self.store_id, denominator_result=0, weight=weight,
result=length_of_unique_skus, target=minimum_number_of_skus,
score=result * weight,
identifier_parent=parent_kpi_name, should_enter=True)
return result
def get_relevant_product_assortment_by_kpi_name(self, kpi_name):
template = self.templates[Const.SURVEY_QUESTION_SKUS]
relevant_template = template[template[Const.KPI_NAME] == kpi_name]
# we need this to fix dumb template
relevant_template[Const.EAN_CODE] = \
relevant_template[Const.EAN_CODE].apply(lambda x: str(int(x)) if x != '' else None)
primary_ean_codes = \
relevant_template[relevant_template[Const.SECONDARY_GROUP]
!= 'Y'][Const.EAN_CODE].unique().tolist()
primary_ean_codes = [code for code in primary_ean_codes if code is not None]
primary_products = self.all_products[self.all_products['product_ean_code'].isin(
primary_ean_codes)]
primary_product_pks = primary_products['product_fk'].unique().tolist()
secondary_ean_codes = \
relevant_template[relevant_template[Const.SECONDARY_GROUP]
== 'Y'][Const.EAN_CODE].unique().tolist()
if secondary_ean_codes:
secondary_products = self.all_products[self.all_products['product_ean_code'].isin(
secondary_ean_codes)]
secondary_product_pks = secondary_products['product_fk'].unique().tolist()
else:
secondary_product_pks = None
return primary_product_pks, secondary_product_pks
# Count of Display functions
def calculate_count_of_display(self, kpi_line, relevant_scif, weight):
filtered_scif = relevant_scif.copy()
manufacturer = self.does_exist(kpi_line, Const.MANUFACTURER)
if manufacturer:
filtered_scif = relevant_scif[relevant_scif['manufacturer_name'].isin(manufacturer)]
liberty_truck = self.does_exist(kpi_line, Const.LIBERTY_KEY_MANUFACTURER)
if liberty_truck:
liberty_truck_scif = relevant_scif[relevant_scif[Const.LIBERTY_KEY_MANUFACTURER].isin(
liberty_truck)]
filtered_scif = filtered_scif.append(liberty_truck_scif, sort=False).drop_duplicates()
brand = self.does_exist(kpi_line, Const.BRAND)
if brand:
filtered_scif = filtered_scif[filtered_scif['brand_name'].isin(brand)]
category = self.does_exist(kpi_line, Const.CATEGORY)
if category:
filtered_scif = filtered_scif[filtered_scif['category'].isin(category)]
excluded_brand = self.does_exist(kpi_line, Const.EXCLUDED_BRAND)
if excluded_brand:
filtered_scif = filtered_scif[~filtered_scif['brand_name'].isin(excluded_brand)]
excluded_category = self.does_exist(kpi_line, Const.EXCLUDED_CATEGORY)
if excluded_category:
filtered_scif = filtered_scif[~filtered_scif['category'].isin(excluded_category)]
ssd_still = self.does_exist(kpi_line, Const.ATT4)
if ssd_still:
filtered_scif = filtered_scif[filtered_scif['att4'].isin(ssd_still)]
if self.does_exist(kpi_line, Const.INCLUDE_BODY_ARMOR) and self.body_armor_delivered:
body_armor_scif = relevant_scif[relevant_scif['brand_fk'] == Const.BODY_ARMOR_BRAND_FK]
filtered_scif = filtered_scif.append(body_armor_scif, sort=False)
size_subpackages = self.does_exist(kpi_line, Const.SIZE_SUBPACKAGES_NUM)
if size_subpackages:
# convert all pairings of size and number of subpackages to tuples
# size_subpackages_tuples = [tuple([float(i) for i in x.split(';')]) for x in size_subpackages]
size_subpackages_tuples = [tuple([self.convert_base_size_values(i) for i in x.split(';')]) for x in
size_subpackages]
filtered_scif = filtered_scif[pd.Series(list(zip(filtered_scif['Base Size'],
filtered_scif['Multi-Pack Size'])),
index=filtered_scif.index).isin(size_subpackages_tuples)]
excluded_size_subpackages = self.does_exist(kpi_line, Const.EXCLUDED_SIZE_SUBPACKAGES_NUM)
if excluded_size_subpackages:
# convert all pairings of size and number of subpackages to tuples
# size_subpackages_tuples = [tuple([float(i) for i in x.split(';')]) for x in size_subpackages]
size_subpackages_tuples = [tuple([self.convert_base_size_values(i) for i in x.split(';')]) for x in
excluded_size_subpackages]
filtered_scif = filtered_scif[~pd.Series(list(zip(filtered_scif['Base Size'],
filtered_scif['Multi-Pack Size'])),
index=filtered_scif.index).isin(size_subpackages_tuples)]
sub_packages = self.does_exist(kpi_line, Const.SUBPACKAGES_NUM)
if sub_packages:
if sub_packages == [Const.NOT_NULL]:
filtered_scif = filtered_scif[~filtered_scif['Multi-Pack Size'].isnull()]
elif sub_packages == [Const.GREATER_THAN_ONE]:
filtered_scif = filtered_scif[filtered_scif['Multi-Pack Size'] > 1]
else:
filtered_scif = filtered_scif[filtered_scif['Multi-Pack Size'].isin(
[int(i) for i in sub_packages])]
if self.does_exist(kpi_line, Const.MINIMUM_FACINGS_REQUIRED):
number_of_passing_displays, _ = self.get_number_of_passing_displays(filtered_scif)
if self.does_exist(kpi_line, Const.PARENT_KPI_NAME):
parent_kpi_name = kpi_line[Const.PARENT_KPI_NAME] + Const.LIBERTY + Const.DRILLDOWN
kpi_fk = self.common_db.get_kpi_fk_by_kpi_type(
kpi_line[Const.KPI_NAME] + Const.LIBERTY)
self.common_db.write_to_db_result(kpi_fk, numerator_id=self.manufacturer_fk, numerator_result=0,
denominator_id=self.store_id, denominator_result=0, weight=weight,
result=number_of_passing_displays,
score=number_of_passing_displays,
identifier_parent=parent_kpi_name, should_enter=True)
return 0
else:
parent_kpi_name = kpi_line[Const.KPI_NAME] + Const.LIBERTY
identifier_result = parent_kpi_name + Const.DRILLDOWN
kpi_fk = self.common_db.get_kpi_fk_by_kpi_type(parent_kpi_name + Const.DRILLDOWN)
self.common_db.write_to_db_result(kpi_fk, numerator_id=self.manufacturer_fk, numerator_result=0,
denominator_id=self.store_id, denominator_result=0, weight=weight,
result=number_of_passing_displays,
score=number_of_passing_displays * weight,
identifier_parent=parent_kpi_name,
identifier_result=identifier_result, should_enter=True)
return number_of_passing_displays
else:
return 0
# Share of Display functions
def calculate_share_of_display(self, kpi_line, relevant_scif, weight):
base_scif = relevant_scif.copy()
ssd_still = self.does_exist(kpi_line, Const.ATT4)
if ssd_still:
ssd_still_scif = base_scif[base_scif['att4'].isin(ssd_still)]
else:
ssd_still_scif = base_scif
denominator_passing_displays, _ = \
self.get_number_of_passing_displays(ssd_still_scif)
manufacturer = self.does_exist(kpi_line, Const.MANUFACTURER)
if manufacturer:
filtered_scif = ssd_still_scif[ssd_still_scif['manufacturer_name'].isin(manufacturer)]
else:
filtered_scif = ssd_still_scif
liberty_truck = self.does_exist(kpi_line, Const.LIBERTY_KEY_MANUFACTURER)
if liberty_truck:
liberty_truck_scif = ssd_still_scif[ssd_still_scif[Const.LIBERTY_KEY_MANUFACTURER].isin(
liberty_truck)]
filtered_scif = filtered_scif.append(liberty_truck_scif, sort=False).drop_duplicates()
if self.does_exist(kpi_line, Const.MARKET_SHARE_TARGET):
market_share_target = self.get_market_share_target(ssd_still=ssd_still)
else:
market_share_target = 0
if self.does_exist(kpi_line, Const.INCLUDE_BODY_ARMOR) and self.body_armor_delivered:
body_armor_scif = relevant_scif[relevant_scif['brand_fk'] == Const.BODY_ARMOR_BRAND_FK]
filtered_scif = filtered_scif.append(body_armor_scif, sort=False)
if self.does_exist(kpi_line, Const.MINIMUM_FACINGS_REQUIRED):
numerator_passing_displays, _ = \
self.get_number_of_passing_displays(filtered_scif)
if denominator_passing_displays != 0:
share_of_displays = \
numerator_passing_displays / float(denominator_passing_displays)
else:
share_of_displays = 0
result = 1 if share_of_displays > market_share_target else 0
parent_kpi_name = kpi_line[Const.KPI_NAME] + Const.LIBERTY
kpi_fk = self.common_db.get_kpi_fk_by_kpi_type(parent_kpi_name + Const.DRILLDOWN)
self.common_db.write_to_db_result(kpi_fk, numerator_id=self.manufacturer_fk,
numerator_result=numerator_passing_displays,
denominator_id=self.store_id,
denominator_result=denominator_passing_displays, weight=weight,
result=share_of_displays * 100, target=market_share_target * 100,
score=result * weight,
identifier_parent=parent_kpi_name, should_enter=True)
return result
else:
return 0
def get_number_of_passing_displays(self, filtered_scif):
if filtered_scif.empty:
return 0, 0
filtered_scif = \
filtered_scif.groupby(['Base Size', 'Multi-Pack Size', 'scene_id'],
as_index=False)['facings'].sum()
filtered_scif['passed_displays'] = \
filtered_scif.apply(lambda row: self._calculate_pass_status_of_display(row), axis=1)
number_of_displays = filtered_scif['passed_displays'].sum()
facings_of_displays = filtered_scif[filtered_scif['passed_displays'] == 1]['facings'].sum()
return number_of_displays, facings_of_displays
def _calculate_pass_status_of_display(self, row): # need to move to external KPI targets
template = self.templates[Const.MINIMUM_FACINGS]
relevant_template = template[(template[Const.BASE_SIZE_MIN] <= row['Base Size']) &
(template[Const.BASE_SIZE_MAX] >= row['Base Size']) &
(template[Const.MULTI_PACK_SIZE] == row['Multi-Pack Size'])]
if relevant_template.empty:
return 0
minimum_facings = relevant_template[Const.MINIMUM_FACINGS_REQUIRED_FOR_DISPLAY].min()
return 1 if row['facings'] >= minimum_facings else 0
# Share of Cooler functions
def calculate_share_of_coolers(self, kpi_line, relevant_scif, weight):
scene_ids = relevant_scif['scene_id'].unique().tolist()
total_coolers = len(scene_ids)
if total_coolers == 0:
return 0
passing_coolers = 0
if self.does_exist(kpi_line, Const.MARKET_SHARE_TARGET):
market_share_target = self.get_market_share_target()
else:
market_share_target = 0
for scene_id in scene_ids:
cooler_scif = relevant_scif[relevant_scif['scene_id'] == scene_id]
filtered_scif = cooler_scif.copy()
manufacturer = self.does_exist(kpi_line, Const.MANUFACTURER)
if manufacturer:
filtered_scif = cooler_scif[cooler_scif['manufacturer_name'].isin(manufacturer)]
liberty_truck = self.does_exist(kpi_line, Const.LIBERTY_KEY_MANUFACTURER)
if liberty_truck:
liberty_truck_scif = cooler_scif[cooler_scif[Const.LIBERTY_KEY_MANUFACTURER].isin(
liberty_truck)]
filtered_scif = filtered_scif.append(
liberty_truck_scif, sort=False).drop_duplicates()
if self.does_exist(kpi_line, Const.INCLUDE_BODY_ARMOR) and self.body_armor_delivered:
body_armor_scif = cooler_scif[cooler_scif['brand_fk'] == Const.BODY_ARMOR_BRAND_FK]
filtered_scif = filtered_scif.append(body_armor_scif, sort=False).drop_duplicates()
coke_facings_threshold = self.does_exist(kpi_line, Const.COKE_FACINGS_THRESHOLD)
cooler_sos = filtered_scif['facings'].sum() / cooler_scif['facings'].sum()
cooler_result = 1 if cooler_sos >= coke_facings_threshold else 0
passing_coolers += cooler_result
coke_market_share = passing_coolers / float(total_coolers)
result = 1 if coke_market_share > market_share_target else 0
parent_kpi_name = kpi_line[Const.KPI_NAME] + Const.LIBERTY
kpi_fk = self.common_db.get_kpi_fk_by_kpi_type(parent_kpi_name + Const.DRILLDOWN)
self.common_db.write_to_db_result(kpi_fk, numerator_id=self.manufacturer_fk,
numerator_result=passing_coolers,
denominator_id=self.store_id,
denominator_result=total_coolers, weight=weight,
result=coke_market_share * 100, target=market_share_target * 100,
score=result * weight,
identifier_parent=parent_kpi_name, should_enter=True)
return result
# Survey functions
def calculate_survey(self, kpi_line, relevant_scif, weight):
return 1 if self.survey.check_survey_answer(kpi_line[Const.QUESTION_TEXT], 'Yes') else 0
# helper functions
def convert_base_size_and_multi_pack(self):
self.scif.loc[:, 'Base Size'] = self.scif['Base Size'].apply(self.convert_base_size_values)
self.scif.loc[:, 'Multi-Pack Size'] = \
self.scif['Multi-Pack Size'].apply(lambda x: int(x) if x is not None else None)
@staticmethod
def convert_base_size_values(value):
try:
new_value = float(value.split()[0]) if value not in [None, ''] else None
except IndexError:
Log.error('Could not convert base size value for {}'.format(value))
new_value = None
return new_value
def get_market_share_target(self, ssd_still=None): # need to move to external KPI targets
template = self.templates[Const.MARKET_SHARE]
relevant_template = template[(template[Const.ADDITIONAL_ATTRIBUTE_4] == self.additional_attribute_4) &
(template[Const.RETAILER] == self.retailer) &
(template[Const.BRANCH] == self.branch)]
if relevant_template.empty:
if ssd_still:
if ssd_still[0].lower() == Const.SSD.lower():
return 49
elif ssd_still[0].lower() == Const.STILL.lower():
return 16
else:
return 0
else:
return 26
if ssd_still:
if ssd_still[0].lower() == Const.SSD.lower():
return relevant_template[Const.SSD].iloc[0]
elif ssd_still[0].lower() == Const.STILL.lower():
return relevant_template[Const.STILL].iloc[0]
# total 26, ssd only 49, still only 16
return relevant_template[Const.SSD_AND_STILL].iloc[0]
def get_body_armor_delivery_status(self):
if self.store_info['additional_attribute_8'].iloc[0] == 'Y':
return True
else:
return False
def get_kpi_function(self, kpi_type):
"""
transfers every kpi to its own function
:param kpi_type: value from "sheet" column in the main sheet
:return: function
"""
if kpi_type == Const.SOS:
return self.calculate_sos
elif kpi_type == Const.AVAILABILITY:
return self.calculate_availability
elif kpi_type == Const.COUNT_OF_DISPLAY:
return self.calculate_count_of_display
elif kpi_type == Const.SHARE_OF_DISPLAY:
return self.calculate_share_of_display
elif kpi_type == Const.SHARE_OF_COOLERS:
return self.calculate_share_of_coolers
elif kpi_type == Const.SURVEY:
return self.calculate_survey
else:
Log.warning(
"The value '{}' in column sheet in the template is not recognized".format(kpi_type))
return None
@staticmethod
def does_exist(kpi_line, column_name):
"""
checks if kpi_line has values in this column, and if it does - returns a list of these values
:param kpi_line: line from template
:param column_name: str
:return: list of values if there are, otherwise None
"""
if column_name in kpi_line.keys() and kpi_line[column_name] != "":
cell = kpi_line[column_name]
if type(cell) in [int, float, np.float64]:
return [cell]
elif type(cell) in [unicode, str]:
return [x.strip() for x in cell.split(",")]
return None
class JNJToolBox:
NUMERATOR = 'numerator'
DENOMINATOR = 'denominator'
SP_LOCATION_KPI = 'secondary placement location quality'
SP_LOCATION_QUALITY_KPI = 'secondary placement location visibility quality'
LVL3_HEADERS = [
'assortment_group_fk', 'assortment_fk', 'target', 'product_fk',
'in_store', 'kpi_fk_lvl1', 'kpi_fk_lvl2', 'kpi_fk_lvl3',
'group_target_date', 'assortment_super_group_fk', 'super_group_target'
]
LVL2_HEADERS = [
'assortment_super_group_fk', 'assortment_group_fk', 'assortment_fk',
'target', 'passes', 'total', 'kpi_fk_lvl1', 'kpi_fk_lvl2',
'group_target_date', 'super_group_target'
]
LVL1_HEADERS = [
'assortment_super_group_fk', 'assortment_group_fk',
'super_group_target', 'passes', 'total', 'kpi_fk_lvl1'
]
ASSORTMENT_FK = 'assortment_fk'
ASSORTMENT_GROUP_FK = 'assortment_group_fk'
ASSORTMENT_SUPER_GROUP_FK = 'assortment_super_group_fk'
# local_msl availability
# LOCAL_MSL_AVAILABILITY = 'local_msl'
# LOCAL_MSL_AVAILABILITY_SKU = 'local_msl - SKU'
# TODO: change this
# local_msl availability
LOCAL_MSL_AVAILABILITY = 'Distribution'
LOCAL_MSL_AVAILABILITY_SKU = 'Distribution - SKU'
# jnjanz local msl/oos KPIs
OOS_BY_LOCAL_ASSORT_STORE_KPI = 'OOS_BY_LOCAL_ASSORT_STORE'
OOS_BY_LOCAL_ASSORT_PRODUCT = 'OOS_BY_LOCAL_ASSORT_PRODUCT'
OOS_BY_LOCAL_ASSORT_CATEGORY = 'OOS_BY_LOCAL_ASSORT_CATEGORY'
OOS_BY_LOCAL_ASSORT_CATEGORY_SUB_CATEGORY = 'OOS_BY_LOCAL_ASSORT_CATEGORY_SUB_CATEGORY'
OOS_BY_LOCAL_ASSORT_CATEGORY_SUB_CATEGORY_PRODUCT = 'OOS_BY_LOCAL_ASSORT_CATEGORY_SUB_CATEGORY_PRODUCT'
MSL_BY_LOCAL_ASSORT = 'MSL_BY_LOCAL_ASSORT'
MSL_BY_LOCAL_ASSORT_PRODUCT = 'MSL_BY_LOCAL_ASSORT_PRODUCT'
MSL_BY_LOCAL_ASSORT_CATEGORY = 'MSL_BY_LOCAL_ASSORT_CATEGORY'
MSL_BY_LOCAL_ASSORT_CATEGORY_SUB_CATEGORY = 'MSL_BY_LOCAL_ASSORT_CATEGORY_SUB_CATEGORY'
MSL_BY_LOCAL_ASSORT_CATEGORY_SUB_CATEGORY_PRODUCT = 'MSL_BY_LOCAL_ASSORT_CATEGORY_SUB_CATEGORY_PRODUCT'
# msl availability
MSL_AVAILABILITY = 'MSL'
MSL_AVAILABILITY_SKU = 'MSL - SKU'
JNJ = 'JOHNSON & JOHNSON'
TYPE_SKU = 'SKU'
TYPE_OTHER = 'Other'
SUCCESSFUL = [1, 4]
OTHER = 'Other'
YES = 'Yes'
NO = 'No'
OOS = 'OOS'
DISTRIBUTED = 'DISTRIBUTED'
EXTRA = 'EXTRA'
def __init__(self, data_provider, output, common, exclusive_template):
self.output = output
self.data_provider = data_provider
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.session_id = self.data_provider.session_id
self.products = self.data_provider[Data.PRODUCTS]
self.all_products_i_d = self.data_provider[
Data.ALL_PRODUCTS_INCLUDING_DELETED]
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.templates = self.data_provider[Data.ALL_TEMPLATES]
self.survey_response = self.data_provider[Data.SURVEY_RESPONSES]
self.rds_conn = PSProjectConnector(self.project_name,
DbUsers.CalculationEng)
self.tools = JNJGENERALToolBox(self.data_provider,
self.output,
rds_conn=self.rds_conn)
self.ps_data_provider = PsDataProvider(self.data_provider, self.output)
self.common = common
self.New_kpi_static_data = common.get_new_kpi_static_data()
self.kpi_results_new_tables_queries = []
self.all_products = self.ps_data_provider.get_sub_category(
self.all_products, 'sub_category_local_name')
self.store_info = self.data_provider[Data.STORE_INFO]
self.store_info = self.ps_data_provider.get_ps_store_info(
self.store_info)
self.current_date = datetime.now()
self.labels = self.ps_data_provider.get_labels()
self.products_in_ass = []
self.products_to_ass = pd.DataFrame(
columns=assTemplate.COLUMNS_ASSORTMENT_DEFINITION_SHEET)
self.assortment_policy = pd.DataFrame(
columns=assTemplate.COLUMNS_STORE_ATTRIBUTES_TO_ASSORT)
self.ass_deleted_prod = pd.DataFrame(columns=[
assTemplate.COLUMN_GRANULAR_GROUP, assTemplate.COLUMN_EAN_CODE
])
self.session_category_info = pd.DataFrame()
self.session_products = pd.DataFrame()
self.assortment = Assortment(self.data_provider, self.output,
self.ps_data_provider)
self.products_to_remove = []
self.ignore_from_top = 1
self.start_shelf = 3
self.products_for_ass_new = pd.DataFrame(
columns=['session_id', 'product_fk'])
self.prev_session_products_new_ass = pd.DataFrame()
self.session_category_new_ass = pd.DataFrame()
self.own_manuf_fk = int(
self.data_provider.own_manufacturer.param_value.values[0])
self.kpi_result_values = self.get_kpi_result_values_df()
self.parser = Parser
self.exclusive_template = exclusive_template
self.result_values = self.ps_data_provider.get_result_values()
def get_kpi_result_values_df(self):
query = JNJQueries.get_kpi_result_values()
query_result = pd.read_sql_query(query, self.rds_conn.db)
return query_result
def get_session_products(self, session):
return self.session_products[self.session_products['session_id'] ==
session]
def result_value_pk(self, result):
"""
converts string result to its pk (in static.kpi_result_value)
:param result: str
:return: int
"""
pk = self.result_values[self.result_values['value'] ==
result]["pk"].iloc[0]
return pk
@staticmethod
def split_and_strip(value):
return map(lambda x: x.strip(), value.split(';'))
def reset_scif_and_matches(self):
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS].copy()
self.match_product_in_scene = self.data_provider[Data.MATCHES].copy()
def filter_scif_matches_for_kpi(self, kpi_name):
if not self.exclusive_template.empty:
template_filters = {}
kpi_filters_df = self.exclusive_template[
self.exclusive_template['KPI'] == kpi_name]
if kpi_filters_df.empty:
return
if not kpi_filters_df.empty:
if kpi_filters_df['Exclude1'].values[0]:
template_filters.update({
kpi_filters_df['Exclude1'].values[0]:
(self.split_and_strip(
kpi_filters_df['Value1'].values[0]), 0)
})
if kpi_filters_df['Exclude2'].values[0]:
template_filters.update({
kpi_filters_df['Exclude2'].values[0]:
(self.split_and_strip(
kpi_filters_df['Value2'].values[0]), 0)
})
if 'Exclude3' in kpi_filters_df.columns.values:
if kpi_filters_df['Exclude3'].values[0]:
template_filters.update({
kpi_filters_df['Exclude3'].values[0]:
(self.split_and_strip(
kpi_filters_df['Value3'].values[0]), 0)
})
if 'Exclude4' in kpi_filters_df.columns.values:
if kpi_filters_df['Exclude4'].values[0]:
template_filters.update({
template_filters['Exclude4'].values[0]:
(self.split_and_strip(
template_filters['Value4'].values[0]), 0)
})
filters = self.get_filters_for_scif_and_matches(
template_filters)
self.scif = self.scif[self.tools.get_filter_condition(
self.scif, **filters)]
self.match_product_in_scene = self.match_product_in_scene[
self.tools.get_filter_condition(
self.match_product_in_scene, **filters)]
def get_filters_for_scif_and_matches(self, template_filters):
product_keys = filter(
lambda x: x in self.data_provider[Data.ALL_PRODUCTS].columns.values
.tolist(), template_filters.keys())
scene_keys = filter(
lambda x: x in self.data_provider[Data.ALL_TEMPLATES].columns.
values.tolist(), template_filters.keys())
product_filters = {}
scene_filters = {}
filters_all = {}
for key in product_keys:
product_filters.update({key: template_filters[key]})
for key in scene_keys:
scene_filters.update({key: template_filters[key]})
if product_filters:
product_fks = self.get_fk_from_filters(product_filters)
filters_all.update({'product_fk': product_fks})
if scene_filters:
scene_fks = self.get_scene_fk_from_filters(scene_filters)
filters_all.update({'scene_fk': scene_fks})
return filters_all
def get_fk_from_filters(self, filters):
all_products = self.data_provider.all_products
product_fk_list = all_products[self.tools.get_filter_condition(
all_products, **filters)]
product_fk_list = product_fk_list['product_fk'].unique().tolist()
return product_fk_list
def get_scene_fk_from_filters(self, filters):
scif_data = self.data_provider[Data.SCENE_ITEM_FACTS]
scene_fk_list = scif_data[self.tools.get_filter_condition(
scif_data, **filters)]
scene_fk_list = scene_fk_list['scene_fk'].unique().tolist()
return scene_fk_list
def get_own_manufacturer_skus_in_scif(self):
# Filter scif by own_manufacturer & product_type = 'SKU'
return self.scif[(self.scif.manufacturer_fk == self.own_manuf_fk)
& (self.scif.product_type == "SKU")
& (self.scif["facings"] >
0)]['item_id'].unique().tolist()
def fetch_local_assortment_products(self):
# TODO Fix with real assortment
lvl3_assortment = self.assortment.get_lvl3_relevant_ass()
local_msl_ass_fk = self.New_kpi_static_data[
self.New_kpi_static_data['client_name'] ==
self.LOCAL_MSL_AVAILABILITY]['pk'].drop_duplicates().values[0]
local_msl_ass_sku_fk = self.New_kpi_static_data[
self.New_kpi_static_data['client_name'] ==
self.LOCAL_MSL_AVAILABILITY_SKU]['pk'].drop_duplicates().values[0]
if lvl3_assortment.empty:
return [], pd.DataFrame()
lvl3_assortment = lvl3_assortment[lvl3_assortment['kpi_fk_lvl3'] ==
local_msl_ass_sku_fk]
if lvl3_assortment.empty:
return [], pd.DataFrame()
assortments = lvl3_assortment['assortment_group_fk'].unique()
products_in_ass = []
for assortment in assortments:
current_assortment = lvl3_assortment[
lvl3_assortment['assortment_group_fk'] == assortment]
current_assortment_product_fks = list(
current_assortment[~current_assortment['product_fk'].isna()]
['product_fk'].unique())
products_in_ass.extend(current_assortment_product_fks)
#ignore None if anty
products_in_ass = [
p for p in products_in_ass if not ((p == None) or p == 'None')
]
return products_in_ass, lvl3_assortment
@kpi_runtime()
def local_assortment_hierarchy_per_store_calc(self):
Log.debug("starting local_assortment calc")
self.products_in_ass, lvl3_assortment = self.fetch_local_assortment_products(
)
self.products_in_ass = np.unique(self.products_in_ass)
if lvl3_assortment.empty or len(self.products_in_ass) == 0:
Log.warning(
"Assortment list is empty for store_fk {} in the requested session : {} - visit_date: {}"
.format(self.store_id, self.session_id,
self.session_info.get('visit_date').iloc[0]))
return
self.local_assortment_hierarchy_per_category_and_subcategory()
oos_per_product_kpi_fk = self.New_kpi_static_data[
self.New_kpi_static_data['client_name'] ==
self.OOS_BY_LOCAL_ASSORT_PRODUCT]['pk'].values[0]
msl_per_product_kpi_fk = self.New_kpi_static_data[
self.New_kpi_static_data['client_name'] ==
self.MSL_BY_LOCAL_ASSORT_PRODUCT]['pk'].values[0]
products_in_session = self.scif['item_id'].drop_duplicates().values
for sku in self.products_in_ass:
if sku in products_in_session:
result = self.result_value_pk(self.DISTRIBUTED)
result_num = 1
else:
result = self.result_value_pk(self.OOS)
result_num = 0
# Saving OOS
self.common.write_to_db_result(
fk=oos_per_product_kpi_fk,
numerator_id=sku,
numerator_result=result,
result=result,
denominator_id=self.own_manuf_fk,
denominator_result=1,
score=result,
identifier_parent="OOS_Local_store",
should_enter=True)
# Saving MSL
self.common.write_to_db_result(fk=msl_per_product_kpi_fk,
numerator_id=sku,
numerator_result=result_num,
result=result,
denominator_id=self.own_manuf_fk,
denominator_result=1,
score=result,
identifier_parent="MSL_Local_store",
should_enter=True)
# Saving MSL - Extra
# Add the Extra Products found in Session from same manufacturer into MSL
own_manufacturer_skus = self.get_own_manufacturer_skus_in_scif()
extra_products_in_scene = set(products_in_session) - set(
self.products_in_ass)
for sku in extra_products_in_scene:
if sku in own_manufacturer_skus:
result = self.result_value_pk(self.EXTRA) # Extra
result_num = 1
self.common.write_to_db_result(
fk=msl_per_product_kpi_fk,
numerator_id=sku,
numerator_result=result_num,
result=result,
denominator_id=self.own_manuf_fk,
denominator_result=1,
score=result,
identifier_parent="MSL_Local_store",
should_enter=True)
oos_kpi_fk = self.New_kpi_static_data[
self.New_kpi_static_data['client_name'] ==
self.OOS_BY_LOCAL_ASSORT_STORE_KPI]['pk'].values[0]
msl_kpi_fk = self.New_kpi_static_data[
self.New_kpi_static_data['client_name'] ==
self.MSL_BY_LOCAL_ASSORT]['pk'].values[0]
denominator = len(self.products_in_ass)
# Saving OOS
oos_numerator = len(
list(set(self.products_in_ass) - set(products_in_session)))
oos_res = round(
(oos_numerator / float(denominator)), 4) if denominator != 0 else 0
self.common.write_to_db_result(fk=oos_kpi_fk,
numerator_id=self.own_manuf_fk,
denominator_id=self.store_id,
numerator_result=oos_numerator,
result=oos_res,
denominator_result=denominator,
score=oos_res,
identifier_result="OOS_Local_store")
# Saving MSL
msl_numerator = len(
list(set(self.products_in_ass) & set(products_in_session)))
msl_res = round(
(msl_numerator / float(denominator)), 4) if denominator != 0 else 0
self.common.write_to_db_result(fk=msl_kpi_fk,
numerator_id=self.own_manuf_fk,
denominator_id=self.store_id,
numerator_result=msl_numerator,
result=msl_res,
denominator_result=denominator,
score=msl_res,
identifier_result="MSL_Local_store")
Log.debug("finishing oos_per_store_calc")
return
def local_assortment_hierarchy_per_category_and_subcategory(self):
Log.debug("starting oos_per_category_per_sub_category_per_product")
products_in_session = self.scif['product_fk'].drop_duplicates().values
# OOS KPIs
oos_cat_subcat_sku_kpi_fk = self.New_kpi_static_data[
self.New_kpi_static_data['client_name'] == self.
OOS_BY_LOCAL_ASSORT_CATEGORY_SUB_CATEGORY_PRODUCT]['pk'].values[0]
oos_cat_subcat_kpi_fk = self.New_kpi_static_data[
self.New_kpi_static_data['client_name'] ==
self.OOS_BY_LOCAL_ASSORT_CATEGORY_SUB_CATEGORY]['pk'].values[0]
oos_cat_kpi_fk = self.New_kpi_static_data[
self.New_kpi_static_data['client_name'] ==
self.OOS_BY_LOCAL_ASSORT_CATEGORY]['pk'].values[0]
# MSL KPIs
msl_cat_subcat_sku_kpi_fk = self.New_kpi_static_data[
self.New_kpi_static_data['client_name'] == self.
MSL_BY_LOCAL_ASSORT_CATEGORY_SUB_CATEGORY_PRODUCT]['pk'].values[0]
msl_cat_subcat_kpi_fk = self.New_kpi_static_data[
self.New_kpi_static_data['client_name'] ==
self.MSL_BY_LOCAL_ASSORT_CATEGORY_SUB_CATEGORY]['pk'].values[0]
msl_cat_kpi_fk = self.New_kpi_static_data[
self.New_kpi_static_data['client_name'] ==
self.MSL_BY_LOCAL_ASSORT_CATEGORY]['pk'].values[0]
categories = self.all_products[self.all_products['product_fk'].isin(self.products_in_ass)] \
['category_fk'].drop_duplicates().values
for category in categories:
products_in_cat = self.all_products[
self.all_products['category_fk'] ==
category]['product_fk'].drop_duplicates().values
relevant_for_ass = list(
set(self.products_in_ass) & set(products_in_cat))
denominator = len(relevant_for_ass)
# Saving OOS
oos_numerator = len(
list(set(relevant_for_ass) - set(products_in_session)))
oos_res = round((oos_numerator /
float(denominator)), 4) if denominator != 0 else 0
self.common.write_to_db_result(fk=oos_cat_kpi_fk,
numerator_id=self.own_manuf_fk,
numerator_result=oos_numerator,
result=oos_res,
denominator_id=category,
denominator_result=denominator,
score=oos_res,
identifier_result="OOS_Local_cat_" +
str(int(category)))
# Saving MSL
msl_numerator = len(
list(set(relevant_for_ass) & set(products_in_session)))
msl_res = round((msl_numerator /
float(denominator)), 4) if denominator != 0 else 0
self.common.write_to_db_result(fk=msl_cat_kpi_fk,
numerator_id=self.own_manuf_fk,
numerator_result=msl_numerator,
result=msl_res,
denominator_id=category,
denominator_result=denominator,
score=msl_res,
identifier_result="MSL_Local_cat_" +
str(int(category)))
sub_categories = self.all_products[(
self.all_products['product_fk'].isin(self.products_in_ass) &
(self.all_products['category_fk']
== category))]['sub_category_fk'].drop_duplicates().values
for sub_category in sub_categories:
products_in_sub_cat = self.all_products[
(self.all_products['sub_category_fk'] == sub_category)
& (self.all_products['category_fk'] == category
)]['product_fk'].drop_duplicates().values
relevant_for_ass = list(
set(self.products_in_ass) & set(products_in_sub_cat))
denominator = len(relevant_for_ass)
# Saving OOS
oos_numerator = len(
list(set(relevant_for_ass) - set(products_in_session)))
oos_res = round(
(oos_numerator /
float(denominator)), 4) if denominator != 0 else 0
self.common.write_to_db_result(
fk=oos_cat_subcat_kpi_fk,
numerator_id=self.own_manuf_fk,
numerator_result=oos_numerator,
result=oos_res,
denominator_id=sub_category,
denominator_result=denominator,
score=oos_res,
identifier_result="OOS_Local_subcat_" +
str(int(sub_category)),
identifier_parent="OOS_Local_cat_" + str(int(category)),
should_enter=True)
# Saving MSL
msl_numerator = len(
list(set(relevant_for_ass) & set(products_in_session)))
msl_res = round(
(msl_numerator /
float(denominator)), 4) if denominator != 0 else 0
self.common.write_to_db_result(
fk=msl_cat_subcat_kpi_fk,
numerator_id=self.own_manuf_fk,
numerator_result=msl_numerator,
result=msl_res,
denominator_id=sub_category,
denominator_result=denominator,
score=msl_res,
identifier_result="MSL_Local_subcat_" +
str(int(sub_category)),
identifier_parent="MSL_Local_cat_" + str(int(category)),
should_enter=True)
for sku in relevant_for_ass:
if sku in products_in_session:
result = self.result_value_pk(self.DISTRIBUTED)
result_num = 1
else:
result = self.result_value_pk(self.OOS)
result_num = 0
# Saving OOS
self.common.write_to_db_result(
fk=oos_cat_subcat_sku_kpi_fk,
result=result,
score=result,
numerator_id=sku,
numerator_result=result,
denominator_id=sub_category,
denominator_result=1,
identifier_parent="OOS_Local_subcat_" +
str(int(sub_category)),
should_enter=True)
# Saving MSL
self.common.write_to_db_result(
fk=msl_cat_subcat_sku_kpi_fk,
result=result,
score=result,
numerator_id=sku,
numerator_result=result_num,
denominator_id=sub_category,
denominator_result=1,
identifier_parent="MSL_Local_subcat_" +
str(int(sub_category)),
should_enter=True)
# Saving MSL
# Add the New Products found in Session for the subcat,cat from same manufacturer into MSL
# Filter products in session based on sub_cat and category
# extra_products_in_scene = set(products_in_session) - set(self.products_in_ass)
# for sku in extra_products_in_scene:
relevant_products_in_session = list(
set(products_in_session) & set(products_in_sub_cat))
extra_products_in_scene = set(
relevant_products_in_session) - set(relevant_for_ass)
for sku in extra_products_in_scene:
# Filter scif by own_manufacturer & product_type = 'SKU'
own_manufacturer_skus = self.scif[
(self.scif.manufacturer_fk == self.own_manuf_fk)
& (self.scif.product_type == "SKU")
& (self.scif["facings"] > 0)]['item_id'].tolist()
if sku in own_manufacturer_skus:
result = self.result_value_pk(self.EXTRA) # Extra
result_num = 1
self.common.write_to_db_result(
fk=msl_cat_subcat_sku_kpi_fk,
result=result,
score=result,
numerator_id=sku,
numerator_result=result_num,
denominator_id=sub_category,
denominator_result=1,
identifier_parent="MSL_Local_subcat_" +
str(int(sub_category)),
should_enter=True)
Log.debug("finishing assortment_per_category")
return
def main_calculation(self):
try:
if self.scif.empty:
Log.warning('Scene item facts is empty for this session')
Log.warning(
'Unable to calculate local_msl assortment KPIs: SCIF is empty'
)
return 0
self.reset_scif_and_matches()
self.filter_scif_matches_for_kpi(
"Distribution") #changed from local_msl to Distribution
self.local_assortment_hierarchy_per_store_calc()
except Exception as e:
Log.error("Error: {}".format(e))
return 0
class LIBERTYToolBox:
def __init__(self, data_provider, output, common_db):
self.output = output
self.data_provider = data_provider
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.ps_data_provider = PsDataProvider(self.data_provider, self.output)
self.store_info = self.ps_data_provider.get_ps_store_info(self.data_provider[Data.STORE_INFO])
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.scif = self.scif[self.scif['product_type'] != "Irrelevant"]
self.templates = {}
self.result_values = self.ps_data_provider.get_result_values()
for sheet in Const.SHEETS:
self.templates[sheet] = pd.read_excel(Const.TEMPLATE_PATH, sheetname=sheet).fillna('')
self.common_db = common_db
self.survey = Survey(self.data_provider, output=self.output, ps_data_provider=self.ps_data_provider,
common=self.common_db)
self.manufacturer_fk = Const.MANUFACTURER_FK
self.region = self.store_info['region_name'].iloc[0]
self.store_type = self.store_info['store_type'].iloc[0]
self.retailer = self.store_info['retailer_name'].iloc[0]
self.branch = self.store_info['branch_name'].iloc[0]
self.additional_attribute_4 = self.store_info['additional_attribute_4'].iloc[0]
self.additional_attribute_7 = self.store_info['additional_attribute_7'].iloc[0]
self.body_armor_delivered = self.get_body_armor_delivery_status()
# main functions:
def main_calculation(self, *args, **kwargs):
"""
This function gets all the scene results from the SceneKPI, after that calculates every session's KPI,
and in the end it calls "filter results" to choose every KPI and scene and write the results in DB.
"""
red_score = 0
main_template = self.templates[Const.KPIS]
for i, main_line in main_template.iterrows():
relevant_store_types = self.does_exist(main_line, Const.ADDITIONAL_ATTRIBUTE_7)
if relevant_store_types and self.additional_attribute_7 not in relevant_store_types:
continue
result = self.calculate_main_kpi(main_line)
if result:
red_score += main_line[Const.WEIGHT]
if len(self.common_db.kpi_results) > 0:
kpi_fk = self.common_db.get_kpi_fk_by_kpi_type(Const.RED_SCORE_PARENT)
self.common_db.write_to_db_result(kpi_fk, numerator_id=1, denominator_id=self.store_id, result=red_score,
identifier_result=Const.RED_SCORE_PARENT, should_enter=True)
return
def calculate_main_kpi(self, main_line):
"""
This function gets a line from the main_sheet, transfers it to the match function, and checks all of the
KPIs in the same name in the match sheet.
:param main_line: series from the template of the main_sheet.
"""
relevant_scif = self.scif
scene_types = self.does_exist(main_line, Const.SCENE_TYPE)
if scene_types:
relevant_scif = relevant_scif[relevant_scif['template_name'].isin(scene_types)]
excluded_scene_types = self.does_exist(main_line, Const.EXCLUDED_SCENE_TYPE)
if excluded_scene_types:
relevant_scif = relevant_scif[~relevant_scif['template_name'].isin(excluded_scene_types)]
template_groups = self.does_exist(main_line, Const.TEMPLATE_GROUP)
if template_groups:
relevant_scif = relevant_scif[relevant_scif['template_group'].isin(template_groups)]
result = self.calculate_kpi_by_type(main_line, relevant_scif)
return result
def calculate_kpi_by_type(self, main_line, relevant_scif):
"""
the function calculates all the kpis
:param main_line: one kpi line from the main template
:param relevant_scif:
:return: boolean, but it can be None if we want not to write it in DB
"""
kpi_type = main_line[Const.KPI_TYPE]
relevant_template = self.templates[kpi_type]
kpi_line = relevant_template[relevant_template[Const.KPI_NAME] == main_line[Const.KPI_NAME]].iloc[0]
kpi_function = self.get_kpi_function(kpi_type)
weight = main_line[Const.WEIGHT]
if relevant_scif.empty:
result = 0
else:
result = kpi_function(kpi_line, relevant_scif, weight)
result_type_fk = self.ps_data_provider.get_pks_of_result(
Const.PASS) if result > 0 else self.ps_data_provider.get_pks_of_result(Const.FAIL)
kpi_name = kpi_line[Const.KPI_NAME] + Const.LIBERTY
kpi_fk = self.common_db.get_kpi_fk_by_kpi_type(kpi_name)
self.common_db.write_to_db_result(kpi_fk, numerator_id=self.manufacturer_fk, numerator_result=0,
denominator_id=self.store_id, denominator_result=0, weight=weight,
result=result_type_fk, identifier_parent=Const.RED_SCORE_PARENT,
identifier_result=kpi_name, should_enter=True)
return result
# SOS functions
def calculate_sos(self, kpi_line, relevant_scif, weight):
market_share_required = self.does_exist(kpi_line, Const.MARKET_SHARE_TARGET)
if market_share_required:
market_share_target = self.get_market_share_target()
else:
market_share_target = 0
if not market_share_target:
market_share_target = 0
manufacturer = self.does_exist(kpi_line, Const.MANUFACTURER)
if manufacturer:
relevant_scif = relevant_scif[relevant_scif['manufacturer_name'].isin(manufacturer)]
number_of_facings = relevant_scif['facings'].sum()
result = 1 if number_of_facings > market_share_target else 0
parent_kpi_name = kpi_line[Const.KPI_NAME] + Const.LIBERTY
kpi_fk = self.common_db.get_kpi_fk_by_kpi_type(parent_kpi_name + Const.DRILLDOWN)
self.common_db.write_to_db_result(kpi_fk, numerator_id=self.manufacturer_fk, numerator_result=0,
denominator_id=self.store_id, denominator_result=0, weight=weight,
result=number_of_facings, target=market_share_target,
identifier_parent=parent_kpi_name, should_enter=True)
return result
# Availability functions
def calculate_availability(self, kpi_line, relevant_scif, weight):
survey_question_skus_required = self.does_exist(kpi_line, Const.SURVEY_QUESTION_SKUS_REQUIRED)
if survey_question_skus_required:
survey_question_skus = self.get_relevant_product_assortment_by_kpi_name(kpi_line[Const.KPI_NAME])
unique_skus = \
relevant_scif[relevant_scif['product_fk'].isin(survey_question_skus)]['product_fk'].unique().tolist()
else:
manufacturer = self.does_exist(kpi_line, Const.MANUFACTURER)
if manufacturer:
relevant_scif = relevant_scif[relevant_scif['manufacturer_name'].isin(manufacturer)]
brand = self.does_exist(kpi_line, Const.BRAND)
if brand:
relevant_scif = relevant_scif[relevant_scif['brand_name'].isin(brand)]
category = self.does_exist(kpi_line, Const.CATEGORY)
if category:
relevant_scif = relevant_scif[relevant_scif['category'].isin(category)]
excluded_brand = self.does_exist(kpi_line, Const.EXCLUDED_BRAND)
if excluded_brand:
relevant_scif = relevant_scif[~relevant_scif['brand_name'].isin(excluded_brand)]
unique_skus = relevant_scif['product_fk'].unique().tolist()
length_of_unique_skus = len(unique_skus)
minimum_number_of_skus = kpi_line[Const.MINIMUM_NUMBER_OF_SKUS]
result = 1 if length_of_unique_skus >= minimum_number_of_skus else 0
parent_kpi_name = kpi_line[Const.KPI_NAME] + Const.LIBERTY
kpi_fk = self.common_db.get_kpi_fk_by_kpi_type(parent_kpi_name + Const.DRILLDOWN)
self.common_db.write_to_db_result(kpi_fk, numerator_id=self.manufacturer_fk, numerator_result=0,
denominator_id=self.store_id, denominator_result=0, weight=weight,
result=length_of_unique_skus, target=minimum_number_of_skus,
identifier_parent=parent_kpi_name, should_enter=True)
return result
def get_relevant_product_assortment_by_kpi_name(self, kpi_name):
template = self.templates[Const.SURVEY_QUESTION_SKUS]
relevant_template = template[template[Const.KPI_NAME] == kpi_name]
relevant_ean_codes = relevant_template[Const.EAN_CODE].unique().tolist()
relevant_ean_codes = [str(int(x)) for x in relevant_ean_codes if x != ''] # we need this to fix dumb template
relevant_products = self.all_products[self.all_products['product_ean_code'].isin(relevant_ean_codes)]
return relevant_products['product_fk'].unique().tolist()
# Count of Display functions
def calculate_count_of_display(self, kpi_line, relevant_scif, weight):
filtered_scif = relevant_scif
manufacturer = self.does_exist(kpi_line, Const.MANUFACTURER)
if manufacturer:
filtered_scif = relevant_scif[relevant_scif['manufacturer_name'].isin(manufacturer)]
brand = self.does_exist(kpi_line, Const.BRAND)
if brand:
filtered_scif = filtered_scif[filtered_scif['brand_name'].isin(brand)]
ssd_still = self.does_exist(kpi_line, Const.ATT4)
if ssd_still:
filtered_scif = filtered_scif[filtered_scif['att4'].isin(ssd_still)]
size_subpackages = self.does_exist(kpi_line, Const.SIZE_SUBPACKAGES_NUM)
if size_subpackages:
# convert all pairings of size and number of subpackages to tuples
size_subpackages_tuples = [tuple([float(i) for i in x.split(';')]) for x in size_subpackages]
filtered_scif = filtered_scif[pd.Series(list(zip(filtered_scif['size'],
filtered_scif['number_of_sub_packages'])),
index=filtered_scif.index).isin(size_subpackages_tuples)]
sub_packages = self.does_exist(kpi_line, Const.SUBPACKAGES_NUM)
if sub_packages:
if sub_packages == [Const.NOT_NULL]:
filtered_scif = filtered_scif[~filtered_scif['number_of_sub_packages'].isnull()]
else:
filtered_scif = filtered_scif[filtered_scif['number_of_sub_packages'].isin([int(i) for i in sub_packages])]
if self.does_exist(kpi_line, Const.MINIMUM_FACINGS_REQUIRED):
number_of_passing_displays = self.get_number_of_passing_displays(filtered_scif)
parent_kpi_name = kpi_line[Const.KPI_NAME] + Const.LIBERTY
kpi_fk = self.common_db.get_kpi_fk_by_kpi_type(parent_kpi_name + Const.DRILLDOWN)
self.common_db.write_to_db_result(kpi_fk, numerator_id=self.manufacturer_fk, numerator_result=0,
denominator_id=self.store_id, denominator_result=0, weight=weight,
result=number_of_passing_displays,
identifier_parent=parent_kpi_name, should_enter=True)
return 1 if number_of_passing_displays > 0 else 0
else:
return 0
# Share of Display functions
def calculate_share_of_display(self, kpi_line, relevant_scif, weight):
filtered_scif = relevant_scif
manufacturer = self.does_exist(kpi_line, Const.MANUFACTURER)
if manufacturer:
filtered_scif = relevant_scif[relevant_scif['manufacturer_name'].isin(manufacturer)]
ssd_still = self.does_exist(kpi_line, Const.ATT4)
if ssd_still:
filtered_scif = filtered_scif[filtered_scif['att4'].isin(ssd_still)]
if self.does_exist(kpi_line, Const.MARKET_SHARE_TARGET):
market_share_target = self.get_market_share_target(ssd_still=ssd_still)
else:
market_share_target = 0
if self.does_exist(kpi_line, Const.INCLUDE_BODY_ARMOR) and self.body_armor_delivered:
body_armor_scif = relevant_scif[relevant_scif['brand_fk'] == Const.BODY_ARMOR_BRAND_FK]
filtered_scif = filtered_scif.append(body_armor_scif, sort=False)
if self.does_exist(kpi_line, Const.MINIMUM_FACINGS_REQUIRED):
number_of_passing_displays = self.get_number_of_passing_displays(filtered_scif)
result = 1 if number_of_passing_displays > market_share_target else 0
parent_kpi_name = kpi_line[Const.KPI_NAME] + Const.LIBERTY
kpi_fk = self.common_db.get_kpi_fk_by_kpi_type(parent_kpi_name + Const.DRILLDOWN)
self.common_db.write_to_db_result(kpi_fk, numerator_id=self.manufacturer_fk, numerator_result=0,
denominator_id=self.store_id, denominator_result=0, weight=weight,
result=number_of_passing_displays, target=market_share_target,
identifier_parent=parent_kpi_name, should_enter=True)
return result
else:
return 0
def get_number_of_passing_displays(self, filtered_scif):
if filtered_scif.empty:
return 0
filtered_scif['passed_displays'] = \
filtered_scif.apply(lambda row: self._calculate_pass_status_of_display(row), axis=1)
return filtered_scif['passed_displays'].sum()
def _calculate_pass_status_of_display(self, row): # need to move to external KPI targets
template = self.templates[Const.MINIMUM_FACINGS]
package_category = (row['size'], row['number_of_sub_packages'], row['size_unit'])
relevant_template = template[pd.Series(zip(template['size'],
template['subpackages_num'],
template['unit_of_measure'])) == package_category]
minimum_facings = relevant_template[Const.MINIMUM_FACINGS_REQUIRED_FOR_DISPLAY].min()
return 1 if row['facings'] > minimum_facings else 0
# Survey functions
def calculate_survey(self, kpi_line, relevant_scif, weight):
return 1 if self.survey.check_survey_answer(kpi_line[Const.QUESTION_TEXT], 'Yes') else 0
# helper functions
def get_market_share_target(self, ssd_still=None): # need to move to external KPI targets
template = self.templates[Const.MARKET_SHARE]
relevant_template = template[(template[Const.ADDITIONAL_ATTRIBUTE_4] == self.additional_attribute_4) &
(template[Const.RETAILER] == self.retailer) &
(template[Const.BRANCH] == self.branch)]
if relevant_template.empty:
if ssd_still:
if ssd_still[0].lower() == Const.SSD.lower():
return 49
elif ssd_still[0].lower() == Const.STILL.lower():
return 16
else:
return 0
else:
return 26
if ssd_still:
if ssd_still[0].lower() == Const.SSD.lower():
return relevant_template[Const.SSD].iloc[0]
elif ssd_still[0].lower() == Const.STILL.lower():
return relevant_template[Const.STILL].iloc[0]
# total 26, ssd only 49, still only 16
return relevant_template[Const.SSD_AND_STILL].iloc[0]
def get_body_armor_delivery_status(self):
if self.store_info['additional_attribute_8'].iloc[0] == 'Y':
return True
else:
return False
def get_kpi_function(self, kpi_type):
"""
transfers every kpi to its own function
:param kpi_type: value from "sheet" column in the main sheet
:return: function
"""
if kpi_type == Const.SOS:
return self.calculate_sos
elif kpi_type == Const.AVAILABILITY:
return self.calculate_availability
elif kpi_type == Const.COUNT_OF_DISPLAY:
return self.calculate_count_of_display
elif kpi_type == Const.SHARE_OF_DISPLAY:
return self.calculate_share_of_display
elif kpi_type == Const.SURVEY:
return self.calculate_survey
else:
Log.warning(
"The value '{}' in column sheet in the template is not recognized".format(kpi_type))
return None
@staticmethod
def does_exist(kpi_line, column_name):
"""
checks if kpi_line has values in this column, and if it does - returns a list of these values
:param kpi_line: line from template
:param column_name: str
:return: list of values if there are, otherwise None
"""
if column_name in kpi_line.keys() and kpi_line[column_name] != "":
cell = kpi_line[column_name]
if type(cell) in [int, float]:
return [cell]
elif type(cell) in [unicode, str]:
return [x.strip() for x in cell.split(",")]
return None
