class CCBOTTLERSUSREDToolBox:
def __init__(self, data_provider, output, calculation_type):
self.output = output
self.data_provider = data_provider
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.store_info = self.data_provider[Data.STORE_INFO]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.scif = self.scif[self.scif['product_type'] != "Irrelevant"]
self.united_scenes = self.get_united_scenes(
) # we don't need to check scenes without United products
self.survey = Survey(self.data_provider, self.output)
self.templates = {}
self.calculation_type = calculation_type
if self.calculation_type == Const.SOVI:
self.TEMPLATE_PATH = TEMPLATE_PATH
self.RED_SCORE = Const.RED_SCORE
self.RED_SCORE_INTEG = Const.RED_SCORE_INTEG
for sheet in Const.SHEETS:
self.templates[sheet] = pd.read_excel(
self.TEMPLATE_PATH, sheetname=sheet).fillna('')
self.converters = self.templates[Const.CONVERTERS]
else:
self.TEMPLATE_PATH = SURVEY_TEMPLATE_PATH
self.RED_SCORE = Const.MANUAL_RED_SCORE
self.RED_SCORE_INTEG = Const.MANUAL_RED_SCORE_INTEG
for sheet in Const.SHEETS_MANUAL:
self.templates[sheet] = pd.read_excel(
self.TEMPLATE_PATH, sheetname=sheet).fillna('')
self.common_db_integ = Common(self.data_provider, self.RED_SCORE_INTEG)
self.kpi_static_data_integ = self.common_db_integ.get_kpi_static_data()
self.common_db = Common(self.data_provider, self.RED_SCORE)
self.region = self.store_info['region_name'].iloc[0]
self.store_type = self.store_info['store_type'].iloc[0]
if self.store_type in STORE_TYPES: #####
self.store_type = STORE_TYPES[self.store_type] ####
self.store_attr = self.store_info['additional_attribute_15'].iloc[0]
self.kpi_static_data = self.common_db.get_kpi_static_data()
main_template = self.templates[Const.KPIS]
self.templates[Const.KPIS] = main_template[
(main_template[Const.REGION] == self.region)
& (main_template[Const.STORE_TYPE] == self.store_type)]
self.scene_calculator = CCBOTTLERSUSSceneRedToolBox(
data_provider, output, self.templates, self)
self.scenes_results = pd.DataFrame(columns=Const.COLUMNS_OF_SCENE)
self.session_results = pd.DataFrame(columns=Const.COLUMNS_OF_SESSION)
self.all_results = pd.DataFrame(columns=Const.COLUMNS_OF_SCENE)
self.used_scenes = []
self.red_score = 0
self.weight_factor = self.get_weight_factor()
# 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.
"""
main_template = self.templates[Const.KPIS]
if self.calculation_type == Const.SOVI:
self.scenes_results = self.scene_calculator.main_calculation()
session_template = main_template[main_template[Const.SESSION_LEVEL]
== Const.V]
for i, main_line in session_template.iterrows():
self.calculate_main_kpi(main_line)
else:
for i, main_line in main_template.iterrows():
self.calculate_manual_kpi(main_line)
self.choose_and_write_results()
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.
"""
kpi_name = main_line[Const.KPI_NAME]
kpi_type = main_line[Const.SHEET]
relevant_scif = self.scif[
(self.scif['scene_id'].isin(self.united_scenes))
& (self.scif['product_type'] != 'Empty')]
scene_types = self.does_exist(main_line, Const.SCENE_TYPE)
if scene_types:
relevant_scif = relevant_scif[relevant_scif['template_name'].isin(
scene_types)]
scene_groups = self.does_exist(main_line, Const.SCENE_TYPE_GROUP)
if scene_groups:
relevant_scif = relevant_scif[relevant_scif['template_group'].isin(
scene_groups)]
if kpi_type == Const.SCENE_AVAILABILITY:
result = False if relevant_scif.empty else True
else:
isnt_dp = True if self.store_attr != Const.DP and main_line[
Const.STORE_ATTRIBUTE] == Const.DP else False
relevant_template = self.templates[kpi_type]
relevant_template = relevant_template[relevant_template[
Const.KPI_NAME] == kpi_name]
target = len(relevant_template) if main_line[Const.GROUP_TARGET] == Const.ALL \
else main_line[Const.GROUP_TARGET]
if main_line[Const.SAME_PACK] == Const.V:
result = self.calculate_availability_with_same_pack(
relevant_template, relevant_scif, isnt_dp, target)
else:
function = self.get_kpi_function(kpi_type)
passed_counter = 0
for i, kpi_line in relevant_template.iterrows():
answer = function(kpi_line, relevant_scif, isnt_dp)
if answer:
passed_counter += 1
result = passed_counter >= target
self.write_to_session_level(kpi_name=kpi_name, result=result)
def calculate_manual_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.
"""
kpi_name = main_line[Const.KPI_NAME]
relevant_template = self.templates[Const.SURVEY]
relevant_template = relevant_template[relevant_template[Const.KPI_NAME]
== kpi_name]
target = len(relevant_template) if main_line[Const.GROUP_TARGET] == Const.ALL \
else main_line[Const.GROUP_TARGET]
passed_counter = 0
for i, kpi_line in relevant_template.iterrows():
answer = self.calculate_survey_specific(kpi_line)
if answer:
passed_counter += 1
result = passed_counter >= target
self.write_to_session_level(kpi_name=kpi_name, result=result)
# write in DF:
def write_to_session_level(self, kpi_name, result=0):
"""
Writes a result in the DF
:param kpi_name: string
:param result: boolean
"""
result_dict = {Const.KPI_NAME: kpi_name, Const.RESULT: result * 1}
self.session_results = self.session_results.append(result_dict,
ignore_index=True)
def write_to_all_levels(self,
kpi_name,
result,
display_text,
weight,
scene_fk=None,
reuse_scene=False):
"""
Writes the final result in the "all" DF, add the score to the red score and writes the KPI in the DB
:param kpi_name: str
:param result: int
:param display_text: str
:param weight: int/float
:param scene_fk: for the scene's kpi
:param reuse_scene: this kpi can use scenes that were used
"""
score = self.get_score(weight) * (result > 0)
self.red_score += score
result_dict = {
Const.KPI_NAME: kpi_name,
Const.RESULT: result,
Const.SCORE: score
}
if scene_fk:
result_dict[Const.SCENE_FK] = scene_fk
if not reuse_scene:
self.used_scenes.append(scene_fk)
self.all_results = self.all_results.append(result_dict,
ignore_index=True)
self.write_to_db(kpi_name, score, display_text=display_text)
# survey:
def calculate_survey_specific(self,
kpi_line,
relevant_scif=None,
isnt_dp=None):
"""
returns a survey line if True or False
:param kpi_line: line from the survey sheet
:param relevant_scif:
:param isnt_dp:
:return: True or False - if the question gets the needed answer
"""
question = kpi_line[Const.Q_TEXT]
if not question:
question_id = kpi_line[Const.Q_ID]
if question_id == "":
Log.warning(
"The template has a survey question without ID or text")
return False
question = ('question_fk', int(question_id))
answers = kpi_line[Const.ACCEPTED_ANSWER].split(',')
min_answer = None if kpi_line[Const.REQUIRED_ANSWER] == '' else True
for answer in answers:
if self.survey.check_survey_answer(survey_text=question,
target_answer=answer,
min_required_answer=min_answer):
return True
return False
# availability:
def calculate_availability_with_same_pack(self, relevant_template,
relevant_scif, isnt_dp, target):
"""
checks if all the lines in the availability sheet passes the KPI, AND if all of these filtered scif has
at least one common product that has the same size and number of sub_packages.
:param relevant_template: all the match lines from the availability sheet.
:param relevant_scif: filtered scif
:param isnt_dp: if "store attribute" in the main sheet has DP, and the store is not DP, we shouldn't calculate
DP lines
:param target: how many lines should pass
:return: boolean
"""
relevant_scif = relevant_scif.fillna("NAN")
# only items categorized as SSD should be evaluated in this calculation; see PROS-6342
relevant_scif = relevant_scif[relevant_scif['att4'] == 'SSD']
if relevant_scif.empty:
return False
sizes = relevant_scif['size'].tolist()
sub_packages_nums = relevant_scif['number_of_sub_packages'].tolist()
packages = set(zip(sizes, sub_packages_nums))
for package in packages:
passed_counter = 0
filtered_scif = relevant_scif[
(relevant_scif['size'] == package[0])
& (relevant_scif['number_of_sub_packages'] == package[1])]
for i, kpi_line in relevant_template.iterrows():
answer = self.calculate_availability(kpi_line, filtered_scif,
isnt_dp)
if answer:
passed_counter += 1
if passed_counter < target:
return False
return True
def calculate_availability(self, kpi_line, relevant_scif, isnt_dp):
"""
checks if all the lines in the availability sheet passes the KPI (there is at least one product
in this relevant scif that has the attributes).
:param relevant_scif: filtered scif
:param isnt_dp: if "store attribute" in the main sheet has DP, and the store is not DP, we shouldn't calculate
DP lines
:param kpi_line: line from the availability sheet
:return: boolean
"""
if isnt_dp and kpi_line[Const.MANUFACTURER] in Const.DP_MANU:
return True
filtered_scif = self.filter_scif_availability(kpi_line, relevant_scif)
target = kpi_line[Const.TARGET]
return filtered_scif[
filtered_scif['facings'] > 0]['facings'].count() >= target
def filter_scif_specific(self, relevant_scif, kpi_line, name_in_template,
name_in_scif):
"""
takes scif and filters it from the template
:param relevant_scif: the current filtered scif
:param kpi_line: line from one sheet (availability for example)
:param name_in_template: the column name in the template
:param name_in_scif: the column name in SCIF
:return:
"""
values = self.does_exist(kpi_line, name_in_template)
if values:
if name_in_scif in Const.NUMERIC_VALUES_TYPES:
values = [float(x) for x in values]
return relevant_scif[relevant_scif[name_in_scif].isin(values)]
return relevant_scif
def filter_scif_availability(self, kpi_line, relevant_scif):
"""
calls filter_scif_specific for every column in the template of availability
:param kpi_line:
:param relevant_scif:
:return:
"""
names_of_columns = {
Const.MANUFACTURER: "manufacturer_name",
Const.BRAND: "brand_name",
Const.TRADEMARK: "att2",
Const.SIZE: "size",
Const.NUM_SUB_PACKAGES: "number_of_sub_packages",
Const.PREMIUM_SSD: "Premium SSD",
Const.INNOVATION_BRAND: "Innovation Brand",
}
for name in names_of_columns:
relevant_scif = self.filter_scif_specific(relevant_scif, kpi_line,
name,
names_of_columns[name])
return relevant_scif
# SOS:
def calculate_sos(self, kpi_line, relevant_scif, isnt_dp):
"""
calculates SOS line in the relevant scif.
:param kpi_line: line from SOS sheet.
:param relevant_scif: filtered scif.
:param isnt_dp: if "store attribute" in the main sheet has DP, and the store is not DP, we should filter
all the DP products out of the numerator.
:return: boolean
"""
kpi_name = kpi_line[Const.KPI_NAME]
if kpi_line[Const.EXCLUSION_SHEET] == Const.V:
exclusion_sheet = self.templates[Const.SKU_EXCLUSION]
relevant_exclusions = exclusion_sheet[exclusion_sheet[
Const.KPI_NAME] == kpi_name]
for i, exc_line in relevant_exclusions.iterrows():
relevant_scif = self.exclude_scif(exc_line, relevant_scif)
relevant_scif = relevant_scif[relevant_scif['product_type'] != "Empty"]
den_type = kpi_line[Const.DEN_TYPES_1]
den_value = kpi_line[Const.DEN_VALUES_1]
relevant_scif = self.filter_by_type_value(relevant_scif, den_type,
den_value)
if kpi_line[Const.SSD_STILL] != "":
relevant_scif = self.filter_by_type_value(
relevant_scif, Const.SSD_STILL, kpi_line[Const.SSD_STILL])
num_type = kpi_line[Const.NUM_TYPES_1]
num_value = kpi_line[Const.NUM_VALUES_1]
num_scif = self.filter_by_type_value(relevant_scif, num_type,
num_value)
if isnt_dp:
num_scif = num_scif[~(
num_scif['manufacturer_name'].isin(Const.DP_MANU))]
target = float(kpi_line[Const.TARGET]) / 100
percentage = num_scif['facings'].sum() / relevant_scif['facings'].sum() if relevant_scif['facings'].sum() > 0 \
else 0
return percentage >= target
# SOS majority:
def calculate_sos_maj(self, kpi_line, relevant_scif, isnt_dp):
"""
calculates SOS majority line in the relevant scif. Filters the denominator and sends the line to the
match function (majority or dominant)
:param kpi_line: line from SOS majority sheet.
:param relevant_scif: filtered scif.
:param isnt_dp: if "store attribute" in the main sheet has DP, and the store is not DP, we should filter
all the DP products out of the numerator (and the denominator of the dominant part).
:return: boolean
"""
kpi_name = kpi_line[Const.KPI_NAME]
if kpi_line[Const.EXCLUSION_SHEET] == Const.V:
exclusion_sheet = self.templates[Const.SKU_EXCLUSION]
relevant_exclusions = exclusion_sheet[exclusion_sheet[
Const.KPI_NAME] == kpi_name]
for i, exc_line in relevant_exclusions.iterrows():
relevant_scif = self.exclude_scif(exc_line, relevant_scif)
relevant_scif = relevant_scif[relevant_scif['product_type'] != "Empty"]
den_type = kpi_line[Const.DEN_TYPES_1]
den_value = kpi_line[Const.DEN_VALUES_1]
relevant_scif = self.filter_by_type_value(relevant_scif, den_type,
den_value)
den_type = kpi_line[Const.DEN_TYPES_2]
den_value = kpi_line[Const.DEN_VALUES_2]
relevant_scif = self.filter_by_type_value(relevant_scif, den_type,
den_value)
if kpi_line[Const.MAJ_DOM] == Const.MAJOR:
answer = self.calculate_majority_part(kpi_line, relevant_scif,
isnt_dp)
elif kpi_line[Const.MAJ_DOM] == Const.DOMINANT:
answer = self.calculate_dominant_part(kpi_line, relevant_scif,
isnt_dp)
else:
Log.warning("SOS majority does not know '{}' part".format(
kpi_line[Const.MAJ_DOM]))
answer = False
return answer
def calculate_majority_part(self, kpi_line, relevant_scif, isnt_dp):
"""
filters the numerator and checks if the SOS is bigger than 50%.
:param kpi_line: line from SOS majority sheet.
:param relevant_scif: filtered scif.
:param isnt_dp: if "store attribute" in the main sheet has DP, and the store is not DP, we should filter
all the DP products out of the numerator.
:return: boolean
"""
num_type = kpi_line[Const.NUM_TYPES_1]
num_value = kpi_line[Const.NUM_VALUES_1]
num_scif = self.filter_by_type_value(relevant_scif, num_type,
num_value)
num_type = kpi_line[Const.NUM_TYPES_2]
num_value = kpi_line[Const.NUM_VALUES_2]
num_scif = self.filter_by_type_value(num_scif, num_type, num_value)
if num_scif.empty:
return None
if isnt_dp:
num_scif = num_scif[~(
num_scif['manufacturer_name'].isin(Const.DP_MANU))]
target = Const.MAJORITY_TARGET
return num_scif['facings'].sum() / relevant_scif['facings'].sum(
) >= target
def calculate_dominant_part(self, kpi_line, relevant_scif, isnt_dp):
"""
filters the numerator and checks if the given value in the given type is the one with the most facings.
:param kpi_line: line from SOS majority sheet.
:param relevant_scif: filtered scif.
:param isnt_dp: if "store attribute" in the main sheet has DP, and the store is not DP, we should filter
all the DP products out.
:return: boolean
"""
type_name = self.get_column_name(kpi_line[Const.NUM_TYPES_1],
relevant_scif)
values = str(kpi_line[Const.NUM_VALUES_1]).split(', ')
if isnt_dp:
relevant_scif = relevant_scif[~(
relevant_scif['manufacturer_name'].isin(Const.DP_MANU))]
if kpi_line[Const.ADD_IF_NOT_DP] != "":
values_to_add = str(kpi_line[Const.ADD_IF_NOT_DP]).split(', ')
values = values + values_to_add
if type_name in Const.NUMERIC_VALUES_TYPES:
values = [float(x) for x in values]
max_facings, needed_one = 0, 0
values_type = relevant_scif[type_name].unique().tolist()
if None in values_type:
values_type.remove(None)
current_sum = relevant_scif[
relevant_scif[type_name].isnull()]['facings'].sum()
if current_sum > max_facings:
max_facings = current_sum
for value in values_type:
current_sum = relevant_scif[relevant_scif[type_name] ==
value]['facings'].sum()
if current_sum > max_facings:
max_facings = current_sum
if value in values:
needed_one += current_sum
return needed_one >= max_facings
# helpers:
def get_column_name(self, field_name, df):
"""
checks what the real field name in DttFrame is (if it exists in the DF or exists in the "converter" sheet).
:param field_name: str
:param df: scif/products
:return: real column name (if exists)
"""
if field_name in df.columns:
return field_name
if field_name.upper() in self.converters[
Const.NAME_IN_TEMP].str.upper().tolist():
field_name = self.converters[self.converters[
Const.NAME_IN_TEMP].str.upper() == field_name.upper()][
Const.NAME_IN_DB].iloc[0]
return field_name
return None
def filter_by_type_value(self, relevant_scif, type_name, value):
"""
filters scif with the type and value
:param relevant_scif: current filtered scif
:param type_name: str (from the template)
:param value: str
:return: new scif
"""
if type_name == "":
return relevant_scif
values = value.split(', ')
new_type_name = self.get_column_name(type_name, relevant_scif)
if not new_type_name:
print "There is no field '{}'".format(type_name)
return relevant_scif
if new_type_name in Const.NUMERIC_VALUES_TYPES:
values = [float(x) for x in values]
return relevant_scif[relevant_scif[new_type_name].isin(values)]
@staticmethod
def exclude_scif(exclude_line, relevant_scif):
"""
filters products out of the scif
:param exclude_line: line from the exclusion sheet
:param relevant_scif: current filtered scif
:return: new scif
"""
if exclude_line[Const.PRODUCT_EAN] != "":
exclude_products = exclude_line[Const.PRODUCT_EAN].split(', ')
relevant_scif = relevant_scif[~(
relevant_scif['product_ean_code'].isin(exclude_products))]
if exclude_line[Const.BRAND] != "":
exclude_brands = exclude_line[Const.BRAND].split(', ')
relevant_scif = relevant_scif[~(
relevant_scif['brand_name'].isin(exclude_brands))]
return relevant_scif
@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 cell.split(", ")
return None
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.SURVEY:
return self.calculate_survey_specific
elif kpi_type == Const.AVAILABILITY:
return self.calculate_availability
elif kpi_type == Const.SOS:
return self.calculate_sos
elif kpi_type == Const.SOS_MAJOR:
return self.calculate_sos_maj
else:
Log.warning(
"The value '{}' in column sheet in the template is not recognized"
.format(kpi_type))
return None
def choose_and_write_results(self):
"""
writes all the KPI in the DB: first the session's ones, second the scene's ones and in the end the ones
that depends on the previous ones. After all it writes the red score
"""
# self.scenes_results.to_csv('results/{}/scene {}.csv'.format(self.calculation_type, self.session_uid))####
# self.session_results.to_csv('results/{}/session {}.csv'.format(self.calculation_type, self.session_uid))####
main_template = self.templates[Const.KPIS]
self.write_session_kpis(main_template)
if self.calculation_type == Const.SOVI:
self.write_scene_kpis(main_template)
self.write_condition_kpis(main_template)
self.write_missings(main_template)
self.write_to_db(self.RED_SCORE, self.red_score)
# result_dict = {Const.KPI_NAME: 'RED SCORE', Const.SCORE: self.red_score}####
# self.all_results = self.all_results.append(result_dict, ignore_index=True)####
# self.all_results.to_csv('results/{}/{}.csv'.format(self.calculation_type, self.session_uid))####
def write_missings(self, main_template):
"""
write 0 in all the KPIs that didn't get score
:param main_template:
"""
for i, main_line in main_template.iterrows():
kpi_name = main_line[Const.KPI_NAME]
if not self.all_results[self.all_results[Const.KPI_NAME] ==
kpi_name].empty:
continue
result = 0
display_text = main_line[Const.DISPLAY_TEXT]
weight = main_line[Const.WEIGHT]
self.write_to_all_levels(kpi_name, result, display_text, weight)
def write_session_kpis(self, main_template):
"""
iterates all the session's KPIs and saves them
:param main_template: main_sheet.
"""
session_template = main_template[main_template[Const.CONDITION] == ""]
if self.calculation_type == Const.SOVI:
session_template = session_template[session_template[
Const.SESSION_LEVEL] == Const.V]
for i, main_line in session_template.iterrows():
kpi_name = main_line[Const.KPI_NAME]
result = self.session_results[self.session_results[Const.KPI_NAME]
== kpi_name]
if result.empty:
continue
result = result.iloc[0][Const.RESULT]
display_text = main_line[Const.DISPLAY_TEXT]
weight = main_line[Const.WEIGHT]
self.write_to_all_levels(kpi_name, result, display_text, weight)
def write_incremental_kpis(self, scene_template):
"""
lets the incremental KPIs choose their scenes (if they passed).
if KPI passed some scenes, we will choose the scene that the children passed
:param scene_template: filtered main_sheet
:return: the new template (without the KPI written already)
"""
incremental_template = scene_template[
scene_template[Const.INCREMENTAL] != ""]
while not incremental_template.empty:
for i, main_line in incremental_template.iterrows():
kpi_name = main_line[Const.KPI_NAME]
reuse_scene = main_line[Const.REUSE_SCENE] == Const.V
kpi_results = self.scenes_results[self.scenes_results[
Const.KPI_NAME] == kpi_name]
if not reuse_scene:
kpi_results = kpi_results[~(
kpi_results[Const.SCENE_FK].isin(self.used_scenes))]
true_results = kpi_results[kpi_results[Const.RESULT] > 0]
increments = main_line[Const.INCREMENTAL]
if ', ' in increments:
first_kpi = increments.split(', ')[0]
others = increments.replace(', '.format(first_kpi), '')
scene_template.loc[scene_template[Const.KPI_NAME] ==
first_kpi, Const.INCREMENTAL] = others
if true_results.empty:
scene_template.loc[scene_template[Const.KPI_NAME] ==
kpi_name, Const.INCREMENTAL] = ""
else:
true_results = true_results.sort_values(by=Const.RESULT,
ascending=False)
display_text = main_line[Const.DISPLAY_TEXT]
weight = main_line[Const.WEIGHT]
scene_fk = true_results.iloc[0][Const.SCENE_FK]
self.write_to_all_levels(
kpi_name,
true_results.iloc[0][Const.RESULT],
display_text,
weight,
scene_fk=scene_fk,
reuse_scene=reuse_scene)
scene_template = scene_template[~(
scene_template[Const.KPI_NAME] == kpi_name)]
incremental_template = scene_template[
scene_template[Const.INCREMENTAL] != ""]
return scene_template
def write_regular_scene_kpis(self, scene_template):
"""
lets the regular KPIs choose their scenes (if they passed).
Like in the incremental part - if KPI passed some scenes, we will choose the scene that the children passed
:param scene_template: filtered main_sheet (only scene KPIs, and without the passed incremental)
:return: the new template (without the KPI written already)
"""
for i, main_line in scene_template.iterrows():
kpi_name = main_line[Const.KPI_NAME]
reuse_scene = main_line[Const.REUSE_SCENE] == Const.V
kpi_results = self.scenes_results[self.scenes_results[
Const.KPI_NAME] == kpi_name]
if not reuse_scene:
kpi_results = kpi_results[~(
kpi_results[Const.SCENE_FK].isin(self.used_scenes))]
true_results = kpi_results[kpi_results[Const.RESULT] > 0]
display_text = main_line[Const.DISPLAY_TEXT]
weight = main_line[Const.WEIGHT]
if true_results.empty:
continue
true_results = true_results.sort_values(by=Const.RESULT,
ascending=False)
scene_fk = true_results.iloc[0][Const.SCENE_FK]
self.write_to_all_levels(kpi_name,
true_results.iloc[0][Const.RESULT],
display_text,
weight,
scene_fk=scene_fk,
reuse_scene=reuse_scene)
scene_template = scene_template[~(
scene_template[Const.KPI_NAME] == kpi_name)]
return scene_template
def write_not_passed_scene_kpis(self, scene_template):
"""
lets the KPIs not passed choose their scenes.
:param scene_template: filtered main_sheet (only scene KPIs, and without the passed KPIs)
"""
for i, main_line in scene_template.iterrows():
kpi_name = main_line[Const.KPI_NAME]
reuse_scene = main_line[Const.REUSE_SCENE] == Const.V
kpi_results = self.scenes_results[self.scenes_results[
Const.KPI_NAME] == kpi_name]
if not reuse_scene:
kpi_results = kpi_results[~(
kpi_results[Const.SCENE_FK].isin(self.used_scenes))]
display_text = main_line[Const.DISPLAY_TEXT]
weight = main_line[Const.WEIGHT]
if kpi_results.empty:
continue
scene_fk = kpi_results.iloc[0][Const.SCENE_FK]
self.write_to_all_levels(kpi_name,
0,
display_text,
weight,
scene_fk=scene_fk,
reuse_scene=reuse_scene)
def write_scene_kpis(self, main_template):
"""
iterates every scene_kpi that does not depend on others, and choose the scene they will take:
1. the incrementals take their scene (if they passed).
2. the regular KPIs that passed choose their scenes.
3. the ones that didn't pass choose their random scenes.
:param main_template: main_sheet.
"""
scene_template = main_template[
(main_template[Const.SESSION_LEVEL] != Const.V)
& (main_template[Const.CONDITION] == "")]
scene_template = self.write_incremental_kpis(scene_template)
scene_template = self.write_regular_scene_kpis(scene_template)
self.write_not_passed_scene_kpis(scene_template)
def write_condition_kpis(self, main_template):
"""
writes all the KPI that depend on other KPIs by checking if the parent KPI has passed and in which scene.
:param main_template: main_sheet
"""
condition_template = main_template[
main_template[Const.CONDITION] != '']
for i, main_line in condition_template.iterrows():
condition = main_line[Const.CONDITION]
kpi_name = main_line[Const.KPI_NAME]
if self.calculation_type == Const.MANUAL or main_line[
Const.SESSION_LEVEL] == Const.V:
kpi_results = self.session_results[self.session_results[
Const.KPI_NAME] == kpi_name]
else:
kpi_results = self.scenes_results[self.scenes_results[
Const.KPI_NAME] == kpi_name]
condition_result = self.all_results[
(self.all_results[Const.KPI_NAME] == condition)
& (self.all_results[Const.RESULT] > 0)]
if condition_result.empty:
continue
condition_result = condition_result.iloc[0]
condition_scene = condition_result[Const.SCENE_FK]
if condition_scene and Const.SCENE_FK in kpi_results:
results = kpi_results[kpi_results[Const.SCENE_FK] ==
condition_scene]
else:
results = kpi_results
if results.empty:
continue
result = results.iloc[0][Const.RESULT]
display_text = main_line[Const.DISPLAY_TEXT]
weight = main_line[Const.WEIGHT]
scene_fk = results.iloc[0][
Const.SCENE_FK] if Const.SCENE_FK in kpi_results else None
self.write_to_all_levels(kpi_name,
result,
display_text,
weight,
scene_fk=scene_fk)
def get_united_scenes(self):
return self.scif[self.scif['United Deliver'] ==
'Y']['scene_id'].unique().tolist()
def get_weight_factor(self):
sum_weights = self.templates[Const.KPIS][Const.WEIGHT].sum()
return sum_weights / 100.0
def get_score(self, weight):
return weight / self.weight_factor
def write_to_db(self, kpi_name, score, display_text=''):
"""
writes result in the DB
:param kpi_name: str
:param score: float
:param display_text: str
"""
if kpi_name == self.RED_SCORE:
self.write_to_db_result(self.common_db.get_kpi_fk_by_kpi_name(
self.RED_SCORE, 1),
score=score,
level=1)
if self.common_db_integ:
self.write_to_db_result(
self.common_db_integ.get_kpi_fk_by_kpi_name(
self.RED_SCORE_INTEG, 1),
score=score,
level=1,
set_type=Const.MANUAL)
else:
self.write_to_db_result(self.common_db.get_kpi_fk_by_kpi_name(
kpi_name, 2),
score=score,
level=2)
self.write_to_db_result(self.common_db.get_kpi_fk_by_kpi_name(
kpi_name, 3),
score=score,
level=3,
display_text=display_text)
if self.common_db_integ:
self.write_to_db_result(
self.common_db_integ.get_kpi_fk_by_kpi_name(kpi_name, 3),
score=score,
level=3,
display_text=kpi_name,
set_type=Const.MANUAL)
def write_to_db_result(self,
fk,
level,
score,
set_type=Const.SOVI,
**kwargs):
"""
This function creates the result data frame of every KPI (atomic KPI/KPI/KPI set),
and appends the insert SQL query into the queries' list, later to be written to the DB.
"""
if kwargs:
kwargs['score'] = score
attributes = self.create_attributes_dict(fk=fk,
level=level,
set_type=set_type,
**kwargs)
else:
attributes = self.create_attributes_dict(fk=fk,
score=score,
set_type=set_type,
level=level)
if level == self.common_db.LEVEL1:
table = self.common_db.KPS_RESULT
elif level == self.common_db.LEVEL2:
table = self.common_db.KPK_RESULT
elif level == self.common_db.LEVEL3:
table = self.common_db.KPI_RESULT
else:
return
query = insert(attributes, table)
if set_type == Const.SOVI:
self.common_db.kpi_results_queries.append(query)
else:
self.common_db_integ.kpi_results_queries.append(query)
def create_attributes_dict(self,
score,
fk=None,
level=None,
display_text=None,
set_type=Const.SOVI,
**kwargs):
"""
This function creates a data frame with all attributes needed for saving in KPI results tables.
or
you can send dict with all values in kwargs
"""
kpi_static_data = self.kpi_static_data if set_type == Const.SOVI else self.kpi_static_data_integ
if level == self.common_db.LEVEL1:
if kwargs:
kwargs['score'] = score
values = [val for val in kwargs.values()]
col = [col for col in kwargs.keys()]
attributes = pd.DataFrame(values, columns=col)
else:
kpi_set_name = kpi_static_data[kpi_static_data['kpi_set_fk'] ==
fk]['kpi_set_name'].values[0]
attributes = pd.DataFrame(
[(kpi_set_name, self.session_uid, self.store_id,
self.visit_date.isoformat(), format(score, '.2f'), fk)],
columns=[
'kps_name', 'session_uid', 'store_fk', 'visit_date',
'score_1', 'kpi_set_fk'
])
elif level == self.common_db.LEVEL2:
if kwargs:
kwargs['score'] = score
values = [val for val in kwargs.values()]
col = [col for col in kwargs.keys()]
attributes = pd.DataFrame(values, columns=col)
else:
kpi_name = kpi_static_data[kpi_static_data['kpi_fk'] ==
fk]['kpi_name'].values[0].replace(
"'", "\\'")
attributes = pd.DataFrame(
[(self.session_uid, self.store_id,
self.visit_date.isoformat(), fk, kpi_name, score)],
columns=[
'session_uid', 'store_fk', 'visit_date', 'kpi_fk',
'kpk_name', 'score'
])
elif level == self.common_db.LEVEL3:
if kwargs:
kwargs['score'] = score
values = tuple([val for val in kwargs.values()])
col = [col for col in kwargs.keys()]
attributes = pd.DataFrame([values], columns=col)
else:
data = kpi_static_data[kpi_static_data['atomic_kpi_fk'] == fk]
kpi_fk = data['kpi_fk'].values[0]
kpi_set_name = kpi_static_data[kpi_static_data['atomic_kpi_fk']
== fk]['kpi_set_name'].values[0]
attributes = pd.DataFrame(
[(display_text, self.session_uid, kpi_set_name,
self.store_id, self.visit_date.isoformat(),
datetime.utcnow().isoformat(), score, kpi_fk, fk)],
columns=[
'display_text', 'session_uid', 'kps_name', 'store_fk',
'visit_date', 'calculation_time', 'score', 'kpi_fk',
'atomic_kpi_fk'
])
else:
attributes = pd.DataFrame()
return attributes.to_dict()
def commit_results(self):
"""
committing the results in both sets
"""
self.common_db.delete_results_data_by_kpi_set()
self.common_db.commit_results_data_without_delete()
if self.common_db_integ:
self.common_db_integ.delete_results_data_by_kpi_set()
self.common_db_integ.commit_results_data_without_delete()
class CCBOTTLERSUSCMASOUTHWESTToolBox:
EXCLUDE_FILTER = 0
INCLUDE_FILTER = 1
CONTAIN_FILTER = 2
def __init__(self, data_provider, output, common_v2):
self.output = output
self.data_provider = data_provider
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.store_info = self.data_provider[Data.STORE_INFO]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.scif = self.scif[~(self.scif['product_type'] == 'Irrelevant')]
self.sw_scenes = self.get_sw_scenes(
) # we don't need to check scenes without United products
self.survey = Survey(self.data_provider, self.output)
self.sos = SOS(self.data_provider, self.output)
self.templates = {}
self.common_db = Common(self.data_provider, CMA_COMPLIANCE)
self.common_db2 = common_v2
self.common_scene = CommonV2(self.data_provider)
self.region = self.store_info['region_name'].iloc[0]
self.store_type = self.store_info['store_type'].iloc[0]
self.program = self.store_info['additional_attribute_3'].iloc[0]
self.sales_center = self.store_info['additional_attribute_5'].iloc[0]
if self.store_type in STORE_TYPES: #####
self.store_type = STORE_TYPES[self.store_type] ####
self.store_attr = self.store_info['additional_attribute_3'].iloc[0]
self.kpi_static_data = self.common_db.get_kpi_static_data()
self.total_score = 0
self.sub_scores = defaultdict(int)
self.sub_totals = defaultdict(int)
self.ignore_stacking = False
self.facings_field = 'facings' if not self.ignore_stacking else 'facings_ign_stack'
for sheet in Const.SHEETS_CMA:
self.templates[sheet] = pd.read_excel(TEMPLATE_PATH,
sheetname=sheet).fillna('')
# 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.
"""
main_template = self.templates[Const.KPIS]
if self.region in Const.REGIONS:
for i, main_line in main_template.iterrows():
store_type = self.does_exist(main_line, Const.STORE_TYPE)
if store_type is None or self.store_type in store_type:
self.calculate_main_kpi(main_line)
self.write_sub_parents()
self.write_parent()
self.write_to_db_result(self.common_db.get_kpi_fk_by_kpi_name(
CMA_COMPLIANCE, 1),
score=self.total_score,
level=1)
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.
"""
kpi_name = main_line[Const.KPI_NAME]
kpi_type = main_line[Const.TYPE]
relevant_scif = self.scif[self.scif['scene_id'].isin(self.sw_scenes)]
scene_types = self.does_exist(main_line, Const.SCENE_TYPE)
scene_level = self.does_exist(main_line, Const.SCENE_LEVEL)
store_attrs = main_line[Const.PROGRAM].split(',')
result = score = target = None
general_filters = {}
if scene_types:
relevant_scif = relevant_scif[relevant_scif['template_name'].isin(
scene_types)]
general_filters['template_name'] = scene_types
scene_groups = self.does_exist(main_line, Const.TEMPLATE_GROUP)
if scene_groups:
relevant_scif = relevant_scif[relevant_scif['template_group'].isin(
scene_groups)]
general_filters['template_group'] = scene_groups
if kpi_type == 'shelves bonus':
relevant_template = self.templates['shelves']
else:
relevant_template = self.templates[kpi_type]
relevant_template = relevant_template[relevant_template[Const.KPI_NAME]
== kpi_name]
function = self.get_kpi_function(kpi_type)
for i, kpi_line in relevant_template.iterrows():
if not self.store_attr or (store_attrs[0] != '' and self.store_attr not in store_attrs)\
or relevant_scif.empty:
continue
if scene_level:
self.scene_level_kpis(kpi_line, relevant_scif, general_filters,
function)
else:
result, score, target = function(kpi_line, relevant_scif,
general_filters)
# write in DF:
if result is None and score is None and target is None:
continue
if 'Bonus' in self.get_kpi_parent(kpi_name):
self.update_sub_score(kpi_name, passed=result)
else:
self.update_sub_score(kpi_name, passed=score)
if target is None:
target = 0
self.write_to_all_levels(kpi_name=kpi_name,
result=result,
score=score,
target=target)
else:
pass
def write_to_session_level(self, kpi_name, result=0):
"""
Writes a result in the DF
:param kpi_name: string
:param result: boolean
"""
result_dict = {Const.KPI_NAME: kpi_name, Const.RESULT: result * 1}
self.session_results = self.session_results.append(result_dict,
ignore_index=True)
def write_to_all_levels(self,
kpi_name,
result,
score,
target=None,
scene_fk=None,
reuse_scene=False):
"""
Writes the final result in the "all" DF, add the score to the red score and writes the KPI in the DB
:param kpi_name: str
:param result: int
:param display_text: str
:param weight: int/float
:param scene_fk: for the scene's kpi
:param reuse_scene: this kpi can use scenes that were used
"""
result_dict = {
Const.KPI_NAME: kpi_name,
Const.RESULT: result,
Const.SCORE: score,
Const.THRESHOLD: target
}
# self.all_results = self.all_results.append(result_dict, ignore_index=True)
self.write_to_db(kpi_name, score, result=result, threshold=target)
# availability:
def calculate_availability(self, kpi_line, relevant_scif):
"""
checks if all the lines in the availability sheet passes the KPI (there is at least one product
in this relevant scif that has the attributes).
:param relevant_scif: filtered scif
:param isnt_dp: if "store attribute" in the main sheet has DP, and the store is not DP, we shouldn't calculate
DP lines
:param kpi_line: line from the availability sheet
:return: boolean
"""
filtered_scif = self.filter_scif_availability(kpi_line, relevant_scif)
target = kpi_line[Const.TARGET]
return filtered_scif[
filtered_scif['facings'] > 0]['facings'].count() >= target
def filter_scif_specific(self, relevant_scif, kpi_line, name_in_template,
name_in_scif):
"""
takes scif and filters it from the template
:param relevant_scif: the current filtered scif
:param kpi_line: line from one sheet (availability for example)
:param name_in_template: the column name in the template
:param name_in_scif: the column name in SCIF
:return:
"""
values = self.does_exist(kpi_line, name_in_template)
if values:
if name_in_scif in Const.NUMERIC_VALUES_TYPES:
values = [float(x) for x in values]
return relevant_scif[relevant_scif[name_in_scif].isin(values)]
return relevant_scif
def filter_scif_availability(self, kpi_line, relevant_scif):
"""
calls filter_scif_specific for every column in the template of availability
:param kpi_line:
:param relevant_scif:
:return:
"""
names_of_columns = {
Const.MANUFACTURER: "manufacturer_name",
Const.BRAND: "brand_name",
Const.TRADEMARK: "att2",
Const.SIZE: "size",
Const.NUM_SUB_PACKAGES: "number_of_sub_packages",
# CCBOTTLERSUSConst.PREMIUM_SSD: "Premium SSD",
# CCBOTTLERSUSConst.INNOVATION_BRAND: "Innovation Brand",
}
for name in names_of_columns:
relevant_scif = self.filter_scif_specific(relevant_scif, kpi_line,
name,
names_of_columns[name])
return relevant_scif
# SOS:
def calculate_sos(self, kpi_line, relevant_scif, general_filters):
"""
calculates SOS line in the relevant scif.
:param kpi_line: line from SOS sheet.
:param relevant_scif: filtered scif.
:param isnt_dp: if "store attribute" in the main sheet has DP, and the store is not DP, we should filter
all the DP products out of the numerator.
:return: boolean
"""
kpi_name = kpi_line[Const.KPI_NAME]
relevant_scif = relevant_scif[relevant_scif['product_type'] != "Empty"]
den_type = kpi_line[Const.DEN_TYPES_1]
den_value = kpi_line[Const.DEN_VALUES_1].split(',')
# relevant_scif = self.filter_by_type_value(relevant_scif, den_type, den_value)
num_type = kpi_line[Const.NUM_TYPES_1]
num_value = kpi_line[Const.NUM_VALUES_1].split(',')
# num_scif = self.filter_by_type_value(relevant_scif, num_type, num_value)
general_filters['product_type'] = (['Empty', 'Irrelevant'], 0)
if kpi_line['range'] == 'Y':
upper_limit, lower_limit = self.get_sos_targets(kpi_name,
sos_range=True)
target = None
else:
upper_limit, lower_limit = None, None
target = self.get_sos_targets(kpi_name)
general_filters[den_type] = den_value
if kpi_line[Const.DEN_TYPES_2]:
den_type_2 = kpi_line[Const.DEN_TYPES_2]
den_value_2 = kpi_line[Const.DEN_VALUES_2].split(',')
general_filters[den_type_2] = den_value_2
sos_filters = {num_type: num_value}
if kpi_line[Const.NUM_TYPES_2]:
num_type_2 = kpi_line[Const.NUM_TYPES_2]
num_value_2 = kpi_line[Const.NUM_VALUES_2].split(',')
sos_filters[num_type_2] = num_value_2
sos_value = self.sos.calculate_share_of_shelf(sos_filters,
**general_filters)
sos_value *= 100
sos_value = round(sos_value, 2)
if target:
target = target * 100
score = 1 if sos_value >= target else 0
elif not target and upper_limit and lower_limit:
score = 1 if (
lower_limit * 100 <= sos_value <= upper_limit * 100) else 0
target = '{}% - {}%'.format(lower_limit, upper_limit)
else:
score = 1
target = 0
return sos_value, score, target
# Targets:
def get_sos_targets(self, kpi_name, sos_range=False):
targets_template = self.templates[Const.TARGETS]
store_targets = targets_template.loc[
(targets_template[Const.PROGRAM] == self.program)
& (targets_template['region'] == self.region)]
filtered_targets_to_kpi = store_targets.loc[
targets_template['KPI name'] == kpi_name]
if sos_range:
if not filtered_targets_to_kpi.empty:
range = filtered_targets_to_kpi['target'].values[0].split(
' - ')
upper_limit = int(range[1].replace('%', '').strip())
lower_limit = int(range[0].replace('%', '').strip())
else:
upper_limit, lower_limit = None, None
return upper_limit, lower_limit
else:
if not filtered_targets_to_kpi.empty:
target = float(filtered_targets_to_kpi[Const.TARGET].values[0])
else:
target = None
return target
def get_targets(self, kpi_name):
targets_template = self.templates[Const.TARGETS]
store_targets = targets_template.loc[
(targets_template[Const.PROGRAM] == self.program)
& (targets_template['region'] == self.region)]
filtered_targets_to_kpi = store_targets.loc[
targets_template['KPI name'] == kpi_name]
if not filtered_targets_to_kpi.empty:
target = filtered_targets_to_kpi[Const.TARGET].values[0]
else:
target = None
return target
@staticmethod
def get_kpi_line_filters(kpi_line):
filters = {}
attribs = list(kpi_line.index)
c = 1
while 1:
if 'Param {}'.format(c) in attribs and kpi_line['Param {}'.format(
c)]:
filters[kpi_line['Param {}'.format(c)]] = kpi_line[
'Value {}'.format(c)].split(',')
else:
if c > 3: # just in case someone inexplicably chose a nonlinear numbering format.
break
c += 1
return filters
@staticmethod
def get_kpi_line_targets(kpi_line):
mask = kpi_line.index.str.contains('Target')
if mask.any():
targets = kpi_line.loc[mask].replace('', np.nan).dropna()
targets.index = [
int(x.split(Const.SEPERATOR)[1].split(' ')[0])
for x in targets.index
]
targets = targets.to_dict()
else:
targets = {}
return targets
@staticmethod
def extrapolate_target(targets, c):
while 1:
if targets[c]:
target = targets[c]
break
else:
c -= 1
if c < 0:
target = 0
break
return target
def scene_level_kpis(self, kpi_line, scif, general_filters, func):
num_filters = self.get_kpi_line_filters(kpi_line)
general_filters['product_type'] = (['Empty', 'Irrelevant'], 0)
scenes = scif['scene_fk'].unique().tolist()
if not isinstance(scenes, list):
scenes = [scenes]
total_num = 0
total_den = 0
for scene in scenes:
# self.data_provider.load_scene_data(self.session_uid, scene)
self.common_scene.scene_id = scene
scene_scif = scif[scif['scene_fk'] == scene]
if scif.empty:
pass
Log.warning('Match product in scene is empty for this scene')
else:
num, ratio, den = func(kpi_line, scene_scif, num_filters,
general_filters)
total_num += num
total_den += den
self.common_scene.commit_results_data(result_entity='scene')
self.common_scene.kpi_results = pd.DataFrame(
columns=self.common_db2.COLUMNS)
# self.common_db2.write_to_db_result(fk=2161, numerator_result=total_num,
# denominator_result=total_den, result=ratio,
# identifier_result=self.common_db2.get_dictionary(
# parent_name='Total Coke Cooler Purity'),
# should_enter=True)
def sos_with_num_and_dem(self, kpi_line, relevant_scif, num_filters,
general_filters):
kpi_fk = self.common_db2.get_kpi_fk_by_kpi_name(kpi_line['KPI name'])
num_scif = relevant_scif[self.get_filter_condition(
relevant_scif, **num_filters)]
den_scif = relevant_scif[self.get_filter_condition(
relevant_scif, **general_filters)]
try:
Validation.is_empty_df(den_scif)
Validation.is_empty_df(num_scif)
Validation.df_columns_equality(den_scif, num_scif)
Validation.is_subset(den_scif, num_scif)
except Exception, e:
msg = "Data verification failed: {}.".format(e)
raise Exception(msg)
num = num_scif[self.facings_field].sum()
den = den_scif[self.facings_field].sum()
ratio = num / float(den)
# numerator_id=product_fk,
self.common_scene.write_to_db_result(fk=kpi_fk,
numerator_result=num,
denominator_result=den,
result=ratio,
by_scene=True)
# self.common_scene.write_to_db_result(fk=kpi_fk, numerator_result=num,
# denominator_result=den, result=ratio, by_scene=True
# identifier_parent=self.common_db2.get_dictionary(
# parent_name='Total Coke Cooler Purity'),
# should_enter=True)
return num, ratio, den
class CMAToolBox:
EXCLUDE_FILTER = 0
INCLUDE_FILTER = 1
CONTAIN_FILTER = 2
def __init__(self, data_provider, output, common_db2):
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.manufacturer_fk = 1
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.store_info = self.data_provider[Data.STORE_INFO]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.united_scenes = self.get_united_scenes(
) # we don't need to check scenes without United products
self.survey = Survey(self.data_provider, self.output)
self.ps_data_provider = PsDataProvider(self.data_provider, self.output)
self.sos = SOS(self.data_provider, self.output)
self.templates = {}
self.common_db = Common(self.data_provider, SUB_PROJECT)
self.common_db2 = common_db2
self.result_values = self.ps_data_provider.get_result_values()
self.region = self.store_info['region_name'].iloc[0]
self.store_type = self.store_info['store_type'].iloc[0]
self.program = self.store_info['additional_attribute_14'].iloc[0]
self.sales_center = self.store_info['additional_attribute_5'].iloc[0]
if self.store_type in STORE_TYPES:
self.store_type = STORE_TYPES[self.store_type]
self.store_attr = self.store_info['additional_attribute_15'].iloc[0]
self.kpi_static_data = self.common_db.get_kpi_static_data()
self.ignore_stacking = False
self.facings_field = 'facings' if not self.ignore_stacking else 'facings_ign_stack'
self.total_score = 0
self.total_count = 0
for sheet in Const.SHEETS_CMA:
self.templates[sheet] = pd.read_excel(TEMPLATE_PATH,
sheetname=sheet).fillna('')
self.tools = Shared(self.data_provider, self.output)
# 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.
"""
main_template = self.templates[Const.KPIS]
if self.region in Const.REGIONS:
for i, main_line in main_template.iterrows():
store_type = self.does_exist(main_line, Const.STORE_TYPE)
if store_type is None or self.store_type in self.does_exist(
main_line, Const.STORE_TYPE):
self.calculate_main_kpi(main_line)
kpi_fk = self.common_db2.get_kpi_fk_by_kpi_name(SUB_PROJECT)
result = 0
if self.total_count:
result = self.total_score * 100.0 / self.total_count
self.common_db2.write_to_db_result(
fk=kpi_fk,
result=result,
numerator_result=self.total_score,
numerator_id=self.manufacturer_fk,
denominator_result=self.total_count,
denominator_id=self.store_id,
identifier_result=self.common_db2.get_dictionary(
parent_name=SUB_PROJECT))
self.write_to_db_result(self.common_db.get_kpi_fk_by_kpi_name(
SUB_PROJECT, 1),
score=self.total_score,
level=1)
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.
"""
kpi_name = main_line[Const.KPI_NAME]
kpi_type = main_line[Const.TYPE]
relevant_scif = self.scif[self.scif['scene_id'].isin(
self.united_scenes)]
scene_types = self.does_exist(main_line, Const.SCENE_TYPE)
result = score = target = None
general_filters = {}
if scene_types:
relevant_scif = relevant_scif[relevant_scif['template_name'].isin(
scene_types)]
general_filters['template_name'] = scene_types
scene_groups = self.does_exist(main_line, Const.TEMPLATE_GROUP)
if scene_groups:
relevant_scif = relevant_scif[relevant_scif['template_group'].isin(
scene_groups)]
general_filters['template_group'] = scene_groups
if kpi_type == Const.SOS:
isnt_dp = True if self.store_attr != Const.DP and main_line[
Const.STORE_ATTRIBUTE] == Const.DP else False
relevant_template = self.templates[kpi_type]
relevant_template = relevant_template[relevant_template[
Const.KPI_NAME] == kpi_name]
kpi_function = self.get_kpi_function(kpi_type)
for i, kpi_line in relevant_template.iterrows():
result, score, target = kpi_function(kpi_line, relevant_scif,
isnt_dp, general_filters)
else:
pass
self.total_count += 1
if score > 0:
self.total_score += 1
if isinstance(result, tuple):
self.write_to_all_levels(kpi_name=kpi_name,
result=result[0],
score=score,
target=target,
num=result[1],
den=result[2])
else:
self.write_to_all_levels(kpi_name=kpi_name,
result=result,
score=score,
target=target)
# write in DF:
def write_to_all_levels(self,
kpi_name,
result,
score,
target=None,
num=None,
den=None):
"""
Writes the final result in the "all" DF, add the score to the red score and writes the KPI in the DB
:param kpi_name: str
:param result: int
:param display_text: str
:param weight: int/float
:param scene_fk: for the scene's kpi
:param reuse_scene: this kpi can use scenes that were used
"""
# result_dict = {Const.KPI_NAME: kpi_name, Const.RESULT: result, Const.SCORE: score, Const.THRESHOLD: target}
# self.all_results = self.all_results.append(result_dict, ignore_index=True)
self.write_to_db(kpi_name,
score,
result=result,
target=target,
num=num,
den=den)
# survey:
def calculate_survey_specific(self,
kpi_line,
relevant_scif=None,
isnt_dp=None):
"""
returns a survey line if True or False
:param kpi_line: line from the survey sheet
:param relevant_scif:
:param isnt_dp:
:return: True or False - if the question gets the needed answer
"""
question = kpi_line[Const.Q_TEXT]
if not question:
question_id = kpi_line[Const.Q_ID]
if question_id == "":
Log.warning(
"The template has a survey question without ID or text")
return False
question = ('question_fk', int(question_id))
answers = kpi_line[Const.ACCEPTED_ANSWER].split(',')
min_answer = None if kpi_line[Const.REQUIRED_ANSWER] == '' else True
for answer in answers:
if self.survey.check_survey_answer(survey_text=question,
target_answer=answer,
min_required_answer=min_answer):
return True
return False
# availability:
def calculate_availability_with_same_pack(self, relevant_template,
relevant_scif, isnt_dp):
"""
checks if all the lines in the availability sheet passes the KPI, AND if all of these filtered scif has
at least one common product that has the same size and number of sub_packages.
:param relevant_template: all the match lines from the availability sheet.
:param relevant_scif: filtered scif
:param isnt_dp: if "store attribute" in the main sheet has DP, and the store is not DP, we shouldn't calculate
DP lines
:return: boolean
"""
packages = None
for i, kpi_line in relevant_template.iterrows():
if isnt_dp and kpi_line[Const.MANUFACTURER] in Const.DP_MANU:
continue
filtered_scif = self.filter_scif_availability(
kpi_line, relevant_scif)
filtered_scif = filtered_scif.fillna("NAN")
target = kpi_line[Const.TARGET]
sizes = filtered_scif['size'].tolist()
sub_packages_nums = filtered_scif['number_of_sub_packages'].tolist(
)
cur_packages = set(zip(sizes, sub_packages_nums))
if packages is None:
packages = cur_packages
else:
packages = cur_packages & packages
if len(packages) == 0:
return False
if filtered_scif[
filtered_scif['facings'] > 0]['facings'].count() < target:
return False
if len(packages) > 1:
return False
return True
def calculate_availability(self, kpi_line, relevant_scif, isnt_dp):
"""
checks if all the lines in the availability sheet passes the KPI (there is at least one product
in this relevant scif that has the attributes).
:param relevant_scif: filtered scif
:param isnt_dp: if "store attribute" in the main sheet has DP, and the store is not DP, we shouldn't calculate
DP lines
:param kpi_line: line from the availability sheet
:return: boolean
"""
if isnt_dp and kpi_line[Const.MANUFACTURER] in Const.DP_MANU:
return True
filtered_scif = self.filter_scif_availability(kpi_line, relevant_scif)
target = kpi_line[Const.TARGET]
return filtered_scif[
filtered_scif['facings'] > 0]['facings'].count() >= target
def filter_scif_specific(self, relevant_scif, kpi_line, name_in_template,
name_in_scif):
"""
takes scif and filters it from the template
:param relevant_scif: the current filtered scif
:param kpi_line: line from one sheet (availability for example)
:param name_in_template: the column name in the template
:param name_in_scif: the column name in SCIF
:return:
"""
values = self.does_exist(kpi_line, name_in_template)
if values:
if name_in_scif in Const.NUMERIC_VALUES_TYPES:
values = [float(x) for x in values]
return relevant_scif[relevant_scif[name_in_scif].isin(values)]
return relevant_scif
def filter_scif_availability(self, kpi_line, relevant_scif):
"""
calls filter_scif_specific for every column in the template of availability
:param kpi_line:
:param relevant_scif:
:return:
"""
names_of_columns = {
Const.MANUFACTURER: "manufacturer_name",
Const.BRAND: "brand_name",
Const.TRADEMARK: "att2",
Const.SIZE: "size",
Const.NUM_SUB_PACKAGES: "number_of_sub_packages",
}
for name in names_of_columns:
relevant_scif = self.filter_scif_specific(relevant_scif, kpi_line,
name,
names_of_columns[name])
return relevant_scif
# SOS:
def calculate_sos(self, kpi_line, relevant_scif, isnt_dp, general_filters):
"""
calculates SOS line in the relevant scif.
:param kpi_line: line from SOS sheet.
:param relevant_scif: filtered scif.
:param isnt_dp: if "store attribute" in the main sheet has DP, and the store is not DP, we should filter
all the DP products out of the numerator.
:return: boolean
"""
kpi_name = kpi_line[Const.KPI_NAME]
den_type = kpi_line[Const.DEN_TYPES_1]
den_value = kpi_line[Const.DEN_VALUES_1].split(',')
num_type = kpi_line[Const.NUM_TYPES_1]
num_value = kpi_line[Const.NUM_VALUES_1].split(',')
target = self.get_sos_targets(kpi_name)
general_filters[den_type] = den_value
if kpi_line[Const.DEN_TYPES_2]:
den_type_2 = kpi_line[Const.DEN_TYPES_2]
den_value_2 = kpi_line[Const.DEN_VALUES_2].split(',')
general_filters[den_type_2] = den_value_2
sos_filters = {num_type: num_value}
if isnt_dp:
sos_filters['manufacturer_name'] = (Const.DP_MANU, 0)
if kpi_line[Const.NUM_TYPES_2]:
num_type_2 = kpi_line[Const.NUM_TYPES_2]
num_value_2 = kpi_line[Const.NUM_VALUES_2].split(',')
sos_filters[num_type_2] = num_value_2
num_scif = relevant_scif[self.get_filter_condition(
relevant_scif, **sos_filters)]
den_scif = relevant_scif[self.get_filter_condition(
relevant_scif, **general_filters)]
sos_value, num, den = self.tools.sos_with_num_and_dem(
kpi_line, num_scif, den_scif, self.facings_field)
# sos_value = self.sos.calculate_share_of_shelf(sos_filters, **general_filters)
# sos_value *= 100
# sos_value = round(sos_value, 2)
if target:
target *= 100
score = 1 if sos_value >= target else 0
else:
score = 0
target = 0
return (sos_value, num, den), score, target
# SOS majority:
def get_sos_targets(self, kpi_name):
targets_template = self.templates[Const.TARGETS]
store_targets = targets_template.loc[
(targets_template['program'] == self.program)
& (targets_template['channel'] == self.store_type)]
filtered_targets_to_kpi = store_targets.loc[
targets_template['KPI name'] == kpi_name]
if not filtered_targets_to_kpi.empty:
target = filtered_targets_to_kpi[Const.TARGET].values[0]
else:
target = None
return target
# return False
def calculate_sos_maj(self, kpi_line, relevant_scif, isnt_dp):
"""
calculates SOS majority line in the relevant scif. Filters the denominator and sends the line to the
match function (majority or dominant)
:param kpi_line: line from SOS majority sheet.
:param relevant_scif: filtered scif.
:param isnt_dp: if "store attribute" in the main sheet has DP, and the store is not DP, we should filter
all the DP products out of the numerator (and the denominator of the dominant part).
:return: boolean
"""
kpi_name = kpi_line[Const.KPI_NAME]
if kpi_line[Const.EXCLUSION_SHEET] == Const.V:
exclusion_sheet = self.templates[Const.SKU_EXCLUSION]
relevant_exclusions = exclusion_sheet[exclusion_sheet[
Const.KPI_NAME] == kpi_name]
for i, exc_line in relevant_exclusions.iterrows():
relevant_scif = self.exclude_scif(exc_line, relevant_scif)
relevant_scif = relevant_scif[relevant_scif['product_type'] != "Empty"]
den_type = kpi_line[Const.DEN_TYPES_1]
den_value = kpi_line[Const.DEN_VALUES_1]
relevant_scif = self.filter_by_type_value(relevant_scif, den_type,
den_value)
den_type = kpi_line[Const.DEN_TYPES_2]
den_value = kpi_line[Const.DEN_VALUES_2]
relevant_scif = self.filter_by_type_value(relevant_scif, den_type,
den_value)
if kpi_line[Const.MAJ_DOM] == Const.MAJOR:
answer = self.calculate_majority_part(kpi_line, relevant_scif,
isnt_dp)
elif kpi_line[Const.MAJ_DOM] == Const.DOMINANT:
answer = self.calculate_dominant_part(kpi_line, relevant_scif,
isnt_dp)
else:
Log.warning("SOS majority does not know '{}' part".format(
kpi_line[Const.MAJ_DOM]))
answer = False
return answer
def calculate_majority_part(self, kpi_line, relevant_scif, isnt_dp):
"""
filters the numerator and checks if the SOS is bigger than 50%.
:param kpi_line: line from SOS majority sheet.
:param relevant_scif: filtered scif.
:param isnt_dp: if "store attribute" in the main sheet has DP, and the store is not DP, we should filter
all the DP products out of the numerator.
:return: boolean
"""
num_type = kpi_line[Const.NUM_TYPES_1]
num_value = kpi_line[Const.NUM_VALUES_1]
num_scif = self.filter_by_type_value(relevant_scif, num_type,
num_value)
num_type = kpi_line[Const.NUM_TYPES_2]
num_value = kpi_line[Const.NUM_VALUES_2]
num_scif = self.filter_by_type_value(num_scif, num_type, num_value)
if num_scif.empty:
return None
if isnt_dp:
num_scif = num_scif[~(
num_scif['manufacturer_name'].isin(Const.DP_MANU))]
target = Const.MAJORITY_TARGET
return num_scif['facings'].sum() / relevant_scif['facings'].sum(
) >= target
def calculate_dominant_part(self, kpi_line, relevant_scif, isnt_dp):
"""
filters the numerator and checks if the given value in the given type is the one with the most facings.
:param kpi_line: line from SOS majority sheet.
:param relevant_scif: filtered scif.
:param isnt_dp: if "store attribute" in the main sheet has DP, and the store is not DP, we should filter
all the DP products out.
:return: boolean
"""
if isnt_dp:
relevant_scif = relevant_scif[~(
relevant_scif['manufacturer_name'].isin(Const.DP_MANU))]
type_name = self.get_column_name(kpi_line[Const.NUM_TYPES_1],
relevant_scif)
values = str(kpi_line[Const.NUM_VALUES_1]).split(', ')
if type_name in Const.NUMERIC_VALUES_TYPES:
values = [float(x) for x in values]
max_facings, needed_one = 0, 0
values_type = relevant_scif[type_name].unique().tolist()
if None in values_type:
values_type.remove(None)
current_sum = relevant_scif[
relevant_scif[type_name].isnull()]['facings'].sum()
if current_sum > max_facings:
max_facings = current_sum
for value in values_type:
current_sum = relevant_scif[relevant_scif[type_name] ==
value]['facings'].sum()
if current_sum > max_facings:
max_facings = current_sum
if value in values:
needed_one += current_sum
return needed_one >= max_facings
def get_filter_condition(self, df, **filters):
"""
:param df: The data frame to be filters.
:param filters: These are the parameters which the data frame is filtered by.
Every parameter would be a tuple of the value and an include/exclude flag.
INPUT EXAMPLE (1): manufacturer_name = ('Diageo', DIAGEOAUPNGAMERICAGENERALToolBox.INCLUDE_FILTER)
INPUT EXAMPLE (2): manufacturer_name = 'Diageo'
:return: a filtered Scene Item Facts data frame.
"""
if not filters:
return df['pk'].apply(bool)
if self.facings_field in df.keys():
filter_condition = (df[self.facings_field] > 0)
else:
filter_condition = None
for field in filters.keys():
if field in df.keys():
if isinstance(filters[field], tuple):
value, exclude_or_include = filters[field]
else:
value, exclude_or_include = filters[
field], self.INCLUDE_FILTER
if not value:
continue
if not isinstance(value, list):
value = [value]
if exclude_or_include == self.INCLUDE_FILTER:
condition = (df[field].isin(value))
elif exclude_or_include == self.EXCLUDE_FILTER:
condition = (~df[field].isin(value))
elif exclude_or_include == self.CONTAIN_FILTER:
condition = (df[field].str.contains(value[0], regex=False))
for v in value[1:]:
condition |= df[field].str.contains(v, regex=False)
else:
continue
if filter_condition is None:
filter_condition = condition
else:
filter_condition &= condition
else:
Log.warning('field {} is not in the Data Frame'.format(field))
return filter_condition
# helpers:
@staticmethod
def get_column_name(field_name, df):
"""
checks what the real field name in DttFrame is (if it exists in the DF or exists in the "converter" sheet).
:param field_name: str
:param df: scif/products
:return: real column name (if exists)
"""
if field_name in df.columns:
return field_name
return None
def filter_by_type_value(self, relevant_scif, type_name, value):
"""
filters scif with the type and value
:param relevant_scif: current filtered scif
:param type_name: str (from the template)
:param value: str
:return: new scif
"""
if type_name == "":
return relevant_scif
values = value.split(', ')
new_type_name = self.get_column_name(type_name, relevant_scif)
if not new_type_name:
print "There is no field '{}'".format(type_name)
return relevant_scif
if new_type_name in Const.NUMERIC_VALUES_TYPES:
values = [float(x) for x in values]
return relevant_scif[relevant_scif[new_type_name].isin(values)]
@staticmethod
def exclude_scif(exclude_line, relevant_scif):
"""
filters products out of the scif
:param exclude_line: line from the exclusion sheet
:param relevant_scif: current filtered scif
:return: new scif
"""
exclude_products = exclude_line[Const.PRODUCT_EAN].split(', ')
return relevant_scif[~(
relevant_scif['product_ean_code'].isin(exclude_products))]
@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]:
if ", " in cell:
return cell.split(", ")
else:
return cell.split(',')
return None
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.SURVEY:
return self.calculate_survey_specific
elif kpi_type == Const.AVAILABILITY:
return self.calculate_availability
elif kpi_type == Const.SOS:
return self.calculate_sos
elif kpi_type == Const.SOS_MAJOR:
return self.calculate_sos_maj
else:
Log.warning(
"The value '{}' in column sheet in the template is not recognized"
.format(kpi_type))
return None
def get_united_scenes(self):
return self.scif[self.scif['United Deliver'] ==
'Y']['scene_id'].unique().tolist()
def get_pks_of_result(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
def write_to_db(self,
kpi_name,
score,
result=None,
target=None,
num=None,
den=None):
"""
writes result in the DB
:param kpi_name: str
:param score: float
:param result: str
:param target: int
"""
if target and score == 0:
delta = den * (target / 100) - num
else:
delta = 0
score_value = Const.PASS if score == 1 else Const.FAIL
score = self.get_pks_of_result(score_value)
kpi_fk = self.common_db2.get_kpi_fk_by_kpi_type(SUB_PROJECT + " " +
kpi_name)
self.common_db2.write_to_db_result(
fk=kpi_fk,
result=result,
score=score,
should_enter=True,
target=target,
numerator_result=num,
denominator_result=den,
weight=delta,
numerator_id=Const.MANUFACTURER_FK,
denominator_id=self.store_id,
identifier_parent=self.common_db2.get_dictionary(
parent_name=SUB_PROJECT))
self.write_to_db_result(self.common_db.get_kpi_fk_by_kpi_name(
kpi_name, 2),
score=score,
level=2)
self.write_to_db_result(self.common_db.get_kpi_fk_by_kpi_name(
kpi_name, 3),
score=score,
level=3,
threshold=target,
result=result)
def write_to_db_result(self,
fk,
level,
score,
set_type=Const.SOVI,
**kwargs):
"""
This function creates the result data frame of every KPI (atomic KPI/KPI/KPI set),
and appends the insert SQL query into the queries' list, later to be written to the DB.
"""
if kwargs:
kwargs['score'] = score
attributes = self.create_attributes_dict(fk=fk,
level=level,
**kwargs)
else:
attributes = self.create_attributes_dict(fk=fk,
score=score,
level=level)
if level == self.common_db.LEVEL1:
table = self.common_db.KPS_RESULT
elif level == self.common_db.LEVEL2:
table = self.common_db.KPK_RESULT
elif level == self.common_db.LEVEL3:
table = self.common_db.KPI_RESULT
else:
return
query = insert(attributes, table)
self.common_db.kpi_results_queries.append(query)
def create_attributes_dict(self,
score,
fk=None,
level=None,
display_text=None,
set_type=Const.SOVI,
**kwargs):
"""
This function creates a data frame with all attributes needed for saving in KPI results tables.
or
you can send dict with all values in kwargs
"""
kpi_static_data = self.kpi_static_data if set_type == Const.SOVI else self.kpi_static_data_integ
if level == self.common_db.LEVEL1:
if kwargs:
kwargs['score'] = score
values = [val for val in kwargs.values()]
col = [col for col in kwargs.keys()]
attributes = pd.DataFrame(values, columns=col)
else:
kpi_set_name = kpi_static_data[kpi_static_data['kpi_set_fk'] ==
fk]['kpi_set_name'].values[0]
attributes = pd.DataFrame(
[(kpi_set_name, self.session_uid, self.store_id,
self.visit_date.isoformat(), format(score, '.2f'), fk)],
columns=[
'kps_name', 'session_uid', 'store_fk', 'visit_date',
'score_1', 'kpi_set_fk'
])
elif level == self.common_db.LEVEL2:
if kwargs:
kwargs['score'] = score
values = [val for val in kwargs.values()]
col = [col for col in kwargs.keys()]
attributes = pd.DataFrame(values, columns=col)
else:
kpi_name = kpi_static_data[kpi_static_data['kpi_fk'] ==
fk]['kpi_name'].values[0].replace(
"'", "\\'")
attributes = pd.DataFrame(
[(self.session_uid, self.store_id,
self.visit_date.isoformat(), fk, kpi_name, score)],
columns=[
'session_uid', 'store_fk', 'visit_date', 'kpi_fk',
'kpk_name', 'score'
])
elif level == self.common_db.LEVEL3:
data = kpi_static_data[kpi_static_data['atomic_kpi_fk'] == fk]
kpi_fk = data['kpi_fk'].values[0]
kpi_set_name = kpi_static_data[kpi_static_data['atomic_kpi_fk'] ==
fk]['kpi_set_name'].values[0]
display_text = data['kpi_name'].values[0]
if kwargs:
kwargs = self.add_additional_data_to_attributes(
kwargs, score, kpi_set_name, kpi_fk, fk,
datetime.utcnow().isoformat(), display_text)
values = tuple([val for val in kwargs.values()])
col = [col for col in kwargs.keys()]
attributes = pd.DataFrame([values], columns=col)
else:
attributes = pd.DataFrame(
[(display_text, self.session_uid, kpi_set_name,
self.store_id, self.visit_date.isoformat(),
datetime.utcnow().isoformat(), score, kpi_fk, fk)],
columns=[
'display_text', 'session_uid', 'kps_name', 'store_fk',
'visit_date', 'calculation_time', 'score', 'kpi_fk',
'atomic_kpi_fk'
])
else:
attributes = pd.DataFrame()
return attributes.to_dict()
def add_additional_data_to_attributes(self, kwargs_dict, score,
kpi_set_name, kpi_fk, fk, calc_time,
display_text):
kwargs_dict['score'] = score
kwargs_dict['kps_name'] = kpi_set_name
kwargs_dict['kpi_fk'] = kpi_fk
kwargs_dict['atomic_kpi_fk'] = fk
kwargs_dict['calculation_time'] = calc_time
kwargs_dict['session_uid'] = self.session_uid
kwargs_dict['store_fk'] = self.store_id
kwargs_dict['visit_date'] = self.visit_date.isoformat()
kwargs_dict['display_text'] = display_text
return kwargs_dict
def commit_results(self):
"""
committing the results in both sets
"""
self.common_db.delete_results_data_by_kpi_set()
self.common_db.commit_results_data_without_delete()
class REDToolBox:
def __init__(self, data_provider, output, calculation_type, common_db2):
self.output = output
self.data_provider = data_provider
self.project_name = self.data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.products = self.data_provider[Data.PRODUCTS]
self.all_products = self.data_provider[Data.ALL_PRODUCTS]
self.match_product_in_scene = self.data_provider[Data.MATCHES]
self.visit_date = self.data_provider[Data.VISIT_DATE]
self.session_info = self.data_provider[Data.SESSION_INFO]
self.scene_info = self.data_provider[Data.SCENES_INFO]
self.store_id = self.data_provider[Data.STORE_FK]
self.store_info = self.data_provider[Data.STORE_INFO]
self.scif = self.data_provider[Data.SCENE_ITEM_FACTS]
self.scif = self.scif[self.scif['product_type'] != "Irrelevant"]
self.ps_data_provider = PsDataProvider(self.data_provider, self.output)
self.templates = {}
self.result_values = self.ps_data_provider.get_result_values()
self.calculation_type = calculation_type
if self.calculation_type == Const.SOVI:
self.TEMPLATE_PATH = Const.TEMPLATE_PATH
self.RED_SCORE = Const.RED_SCORE
self.RED_SCORE_INTEG = Const.RED_SCORE_INTEG
for sheet in Const.SHEETS:
self.templates[sheet] = pd.read_excel(self.TEMPLATE_PATH, sheetname=sheet).fillna('')
self.converters = self.templates[Const.CONVERTERS]
self.scenes_results = self.ps_data_provider.get_scene_results(
self.scene_info['scene_fk'].drop_duplicates().values)
self.scenes_results = self.scenes_results[[Const.DB_RESULT, Const.DB_SCENE_FK, Const.DB_SCENE_KPI_FK]]
else:
self.TEMPLATE_PATH = Const.SURVEY_TEMPLATE_PATH
self.RED_SCORE = Const.MANUAL_RED_SCORE
self.RED_SCORE_INTEG = Const.MANUAL_RED_SCORE_INTEG
for sheet in Const.SHEETS_MANUAL:
self.templates[sheet] = pd.read_excel(self.TEMPLATE_PATH, sheetname=sheet).fillna('')
self.store_attr = self.store_info['additional_attribute_15'].iloc[0]
self.toolbox = FunctionsToolBox(self.data_provider, self.output, self.templates, self.store_attr)
self.common_db_integ = Common(self.data_provider, self.RED_SCORE_INTEG)
self.kpi_static_data_integ = self.common_db_integ.get_kpi_static_data()
self.common_db = Common(self.data_provider, self.RED_SCORE)
self.common_db2 = common_db2
self.region = self.store_info['region_name'].iloc[0]
self.store_type = self.store_info['store_type'].iloc[0]
if self.store_type in Const.STORE_TYPES:
self.store_type = Const.STORE_TYPES[self.store_type]
self.kpi_static_data = self.common_db.get_kpi_static_data()
main_template = self.templates[Const.KPIS]
self.templates[Const.KPIS] = main_template[(main_template[Const.REGION] == self.region) &
(main_template[Const.STORE_TYPE] == self.store_type)]
self.session_results = pd.DataFrame(columns=Const.COLUMNS_OF_RESULTS)
self.all_results = pd.DataFrame(columns=Const.COLUMNS_OF_RESULTS)
self.used_scenes = []
self.red_score = 0
self.set_fk = self.common_db2.get_kpi_fk_by_kpi_name(self.RED_SCORE)
self.set_integ_fk = self.common_db2.get_kpi_fk_by_kpi_name(self.RED_SCORE_INTEG)
self.weight_factor = self.get_weight_factor()
# 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.
"""
main_template = self.templates[Const.KPIS]
if self.calculation_type == Const.SOVI:
session_template = main_template[main_template[Const.SESSION_LEVEL] == Const.V]
for i, main_line in session_template.iterrows():
self.calculate_main_kpi(main_line)
else:
for i, main_line in main_template.iterrows():
self.calculate_manual_kpi(main_line)
if not main_template.empty:
self.choose_and_write_results()
return self.red_score
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.
"""
kpi_name = main_line[Const.KPI_NAME]
relevant_scif = self.scif
scene_types = self.toolbox.does_exist(main_line, Const.SCENE_TYPE)
if scene_types:
relevant_scif = relevant_scif[relevant_scif['template_name'].isin(scene_types)]
scene_groups = self.toolbox.does_exist(main_line, Const.SCENE_TYPE_GROUP)
if scene_groups:
relevant_scif = relevant_scif[relevant_scif['template_group'].isin(scene_groups)]
if main_line[Const.SHEET] == Const.SCENE_AVAILABILITY:
result = False if relevant_scif.empty else True
else:
result = self.toolbox.calculate_kpi_by_type(main_line, relevant_scif)
self.write_to_session_level(kpi_name=kpi_name, result=result)
def calculate_manual_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.
"""
kpi_name = main_line[Const.KPI_NAME]
relevant_template = self.templates[Const.SURVEY]
relevant_template = relevant_template[relevant_template[Const.KPI_NAME] == kpi_name]
target = len(relevant_template) if main_line[Const.GROUP_TARGET] == Const.ALL \
else main_line[Const.GROUP_TARGET]
passed_counter = 0
for i, kpi_line in relevant_template.iterrows():
answer = self.toolbox.calculate_survey_specific(kpi_line)
if answer:
passed_counter += 1
result = passed_counter >= target
self.write_to_session_level(kpi_name=kpi_name, result=result)
# write in DF:
def write_to_session_level(self, kpi_name, result=0):
"""
Writes a result in the DF
:param kpi_name: string
:param result: boolean
"""
result_dict = {Const.KPI_NAME: kpi_name, Const.DB_RESULT: result * 1}
self.session_results = self.session_results.append(result_dict, ignore_index=True)
def write_to_all_levels(self, kpi_name, result, display_text, weight, scene_fk=None, reuse_scene=False):
"""
Writes the final result in the "all" DF, add the score to the red score and writes the KPI in the DB
:param kpi_name: str
:param result: int
:param display_text: str
:param weight: int/float
:param scene_fk: for the scene's kpi
:param reuse_scene: this kpi can use scenes that were used
"""
score = self.get_score(weight)
result_value = Const.PASS if result > 0 else Const.FAIL
if result_value == Const.PASS:
self.red_score += score
result_dict = {Const.KPI_NAME: kpi_name, Const.DB_RESULT: result, Const.SCORE: score}
if scene_fk:
result_dict[Const.DB_SCENE_FK] = scene_fk
if not reuse_scene:
self.used_scenes.append(scene_fk)
self.all_results = self.all_results.append(result_dict, ignore_index=True)
self.write_to_db(kpi_name, score, display_text=display_text, result_value=result_value)
def choose_and_write_results(self):
"""
writes all the KPI in the DB: first the session's ones, second the scene's ones and in the end the ones
that depends on the previous ones. After all it writes the red score
"""
main_template = self.templates[Const.KPIS]
self.write_session_kpis(main_template)
if self.calculation_type == Const.SOVI:
self.write_scene_kpis(main_template)
self.write_condition_kpis(main_template)
self.write_missings(main_template)
self.write_to_db(self.RED_SCORE, self.red_score)
def write_missings(self, main_template):
"""
write 0 in all the KPIs that didn't get score
:param main_template:
"""
for i, main_line in main_template.iterrows():
kpi_name = main_line[Const.KPI_NAME]
if not self.all_results[self.all_results[Const.KPI_NAME] == kpi_name].empty:
continue
result = 0
display_text = main_line[Const.DISPLAY_TEXT]
weight = main_line[Const.WEIGHT]
self.write_to_all_levels(kpi_name, result, display_text, weight)
def write_session_kpis(self, main_template):
"""
iterates all the session's KPIs and saves them
:param main_template: main_sheet.
"""
session_template = main_template[main_template[Const.CONDITION] == ""]
if self.calculation_type == Const.SOVI:
session_template = session_template[session_template[Const.SESSION_LEVEL] == Const.V]
for i, main_line in session_template.iterrows():
kpi_name = main_line[Const.KPI_NAME]
result = self.session_results[self.session_results[Const.KPI_NAME] == kpi_name]
if result.empty:
continue
result = result.iloc[0][Const.DB_RESULT]
display_text = main_line[Const.DISPLAY_TEXT]
weight = main_line[Const.WEIGHT]
self.write_to_all_levels(kpi_name, result, display_text, weight)
def write_incremental_kpis(self, scene_template):
"""
lets the incremental KPIs choose their scenes (if they passed).
if KPI passed some scenes, we will choose the scene that the children passed
:param scene_template: filtered main_sheet
:return: the new template (without the KPI written already)
"""
incremental_template = scene_template[scene_template[Const.INCREMENTAL] != ""]
while not incremental_template.empty:
for i, main_line in incremental_template.iterrows():
kpi_name = main_line[Const.KPI_NAME]
reuse_scene = main_line[Const.REUSE_SCENE] == Const.V
kpi_scene_fk = self.common_db2.get_kpi_fk_by_kpi_name(kpi_name + Const.SCENE_SUFFIX)
kpi_results = self.scenes_results[self.scenes_results[Const.DB_SCENE_KPI_FK] == kpi_scene_fk]
if not reuse_scene:
kpi_results = kpi_results[~(kpi_results[Const.DB_SCENE_FK].isin(self.used_scenes))]
true_results = kpi_results[kpi_results[Const.DB_RESULT] > 0]
increments = main_line[Const.INCREMENTAL]
if ', ' in increments:
first_kpi = increments.split(', ')[0]
others = increments.replace(', '.format(first_kpi), '')
scene_template.loc[scene_template[Const.KPI_NAME] == first_kpi, Const.INCREMENTAL] = others
if true_results.empty:
scene_template.loc[scene_template[Const.KPI_NAME] == kpi_name, Const.INCREMENTAL] = ""
else:
true_results = true_results.sort_values(by=Const.DB_RESULT, ascending=False)
display_text = main_line[Const.DISPLAY_TEXT]
weight = main_line[Const.WEIGHT]
scene_fk = true_results.iloc[0][Const.DB_SCENE_FK]
self.write_to_all_levels(kpi_name, true_results.iloc[0][Const.DB_RESULT], display_text,
weight, scene_fk=scene_fk, reuse_scene=reuse_scene)
scene_template = scene_template[~(scene_template[Const.KPI_NAME] == kpi_name)]
incremental_template = scene_template[scene_template[Const.INCREMENTAL] != ""]
return scene_template
def write_regular_scene_kpis(self, scene_template):
"""
lets the regular KPIs choose their scenes (if they passed).
Like in the incremental part - if KPI passed some scenes, we will choose the scene that the children passed
:param scene_template: filtered main_sheet (only scene KPIs, and without the passed incremental)
:return: the new template (without the KPI written already)
"""
for i, main_line in scene_template.iterrows():
kpi_name = main_line[Const.KPI_NAME]
reuse_scene = main_line[Const.REUSE_SCENE] == Const.V
kpi_scene_fk = self.common_db2.get_kpi_fk_by_kpi_name(kpi_name + Const.SCENE_SUFFIX)
kpi_results = self.scenes_results[self.scenes_results[Const.DB_SCENE_KPI_FK] == kpi_scene_fk]
if not reuse_scene:
kpi_results = kpi_results[~(kpi_results[Const.DB_SCENE_FK].isin(self.used_scenes))]
true_results = kpi_results[kpi_results[Const.DB_RESULT] > 0]
display_text = main_line[Const.DISPLAY_TEXT]
weight = main_line[Const.WEIGHT]
if true_results.empty:
continue
true_results = true_results.sort_values(by=Const.DB_RESULT, ascending=False)
scene_fk = true_results.iloc[0][Const.DB_SCENE_FK]
self.write_to_all_levels(kpi_name, true_results.iloc[0][Const.DB_RESULT], display_text, weight,
scene_fk=scene_fk, reuse_scene=reuse_scene)
scene_template = scene_template[~(scene_template[Const.KPI_NAME] == kpi_name)]
return scene_template
def write_not_passed_scene_kpis(self, scene_template):
"""
lets the KPIs not passed choose their scenes.
:param scene_template: filtered main_sheet (only scene KPIs, and without the passed KPIs)
"""
for i, main_line in scene_template.iterrows():
kpi_name = main_line[Const.KPI_NAME]
kpi_scene_fk = self.common_db2.get_kpi_fk_by_kpi_name(kpi_name + Const.SCENE_SUFFIX)
reuse_scene = main_line[Const.REUSE_SCENE] == Const.V
kpi_results = self.scenes_results[self.scenes_results[Const.DB_SCENE_KPI_FK] == kpi_scene_fk]
if not reuse_scene:
kpi_results = kpi_results[~(kpi_results[Const.DB_SCENE_FK].isin(self.used_scenes))]
display_text = main_line[Const.DISPLAY_TEXT]
weight = main_line[Const.WEIGHT]
if kpi_results.empty:
continue
scene_fk = kpi_results.iloc[0][Const.DB_SCENE_FK]
self.write_to_all_levels(kpi_name, 0, display_text, weight, scene_fk=scene_fk, reuse_scene=reuse_scene)
def write_scene_kpis(self, main_template):
"""
iterates every scene_kpi that does not depend on others, and choose the scene they will take:
1. the incrementals take their scene (if they passed).
2. the regular KPIs that passed choose their scenes.
3. the ones that didn't pass choose their random scenes.
:param main_template: main_sheet.
"""
scene_template = main_template[(main_template[Const.SESSION_LEVEL] != Const.V) &
(main_template[Const.CONDITION] == "")]
scene_template = self.write_incremental_kpis(scene_template)
scene_template = self.write_regular_scene_kpis(scene_template)
self.write_not_passed_scene_kpis(scene_template)
def write_condition_kpis(self, main_template):
"""
writes all the KPI that depend on other KPIs by checking if the parent KPI has passed and in which scene.
:param main_template: main_sheet
"""
condition_template = main_template[main_template[Const.CONDITION] != '']
for i, main_line in condition_template.iterrows():
condition = main_line[Const.CONDITION]
kpi_name = main_line[Const.KPI_NAME]
if self.calculation_type == Const.MANUAL or main_line[Const.SESSION_LEVEL] == Const.V:
kpi_results = self.session_results[self.session_results[Const.KPI_NAME] == kpi_name]
else:
kpi_scene_fk = self.common_db2.get_kpi_fk_by_kpi_name(kpi_name + Const.SCENE_SUFFIX)
kpi_results = self.scenes_results[self.scenes_results[Const.DB_SCENE_KPI_FK] == kpi_scene_fk]
condition_result = self.all_results[(self.all_results[Const.KPI_NAME] == condition) &
(self.all_results[Const.DB_RESULT] > 0)]
if condition_result.empty:
continue
condition_result = condition_result.iloc[0]
if Const.DB_SCENE_FK in condition_result:
condition_scene = condition_result[Const.DB_SCENE_FK]
else:
condition_scene = None
if condition_scene and Const.DB_SCENE_FK in kpi_results:
results = kpi_results[kpi_results[Const.DB_SCENE_FK] == condition_scene]
else:
results = kpi_results
if results.empty:
continue
result = results.iloc[0][Const.DB_RESULT]
display_text = main_line[Const.DISPLAY_TEXT]
weight = main_line[Const.WEIGHT]
scene_fk = results.iloc[0][Const.DB_SCENE_FK] if Const.DB_SCENE_FK in kpi_results else None
self.write_to_all_levels(kpi_name, result, display_text, weight, scene_fk=scene_fk)
def get_weight_factor(self):
sum_weights = self.templates[Const.KPIS][Const.WEIGHT].sum()
return sum_weights / 100.0
def get_score(self, weight):
return weight / self.weight_factor
def get_pks_of_result(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 get_0_1_of_result(result):
"""
converts string result to its pk (in static.kpi_result_value)
:param result: str
:return: int
"""
pk = 0 if result == Const.FAIL else 1
return pk
def write_to_db(self, kpi_name, score, display_text='', result_value=Const.FAIL):
"""
writes result in the DB
:param kpi_name: str
:param score: float, the weight of the question
:param display_text: str
:param result_value: str, Pass/Fail
"""
if kpi_name == self.RED_SCORE:
self.common_db2.write_to_db_result(
fk=self.set_fk, score=score, numerator_id=Const.MANUFACTURER_FK, denominator_id=self.store_id,
identifier_result=self.common_db2.get_dictionary(kpi_fk=self.set_fk))
self.common_db2.write_to_db_result(
fk=self.set_integ_fk, score=score, numerator_id=Const.MANUFACTURER_FK, denominator_id=self.store_id,
identifier_result=self.common_db2.get_dictionary(kpi_fk=self.set_integ_fk))
self.write_to_db_result(
self.common_db.get_kpi_fk_by_kpi_name(self.RED_SCORE, 1), score=score, level=1)
self.write_to_db_result(
self.common_db_integ.get_kpi_fk_by_kpi_name(self.RED_SCORE_INTEG, 1), score=score, level=1,
set_type=Const.MANUAL)
else:
integ_kpi_fk = self.common_db2.get_kpi_fk_by_kpi_name(kpi_name)
display_kpi_fk = self.common_db2.get_kpi_fk_by_kpi_name(display_text)
if display_kpi_fk is None:
display_kpi_fk = self.common_db2.get_kpi_fk_by_kpi_name(display_text[:100])
result = self.get_pks_of_result(result_value)
self.common_db2.write_to_db_result(
fk=display_kpi_fk, score=score, identifier_parent=self.common_db2.get_dictionary(kpi_fk=self.set_fk),
should_enter=True, result=result, numerator_id=Const.MANUFACTURER_FK, denominator_id=self.store_id)
result = self.get_0_1_of_result(result_value)
self.common_db2.write_to_db_result(
fk=integ_kpi_fk, score=score, should_enter=True, result=result,
identifier_parent=self.common_db2.get_dictionary(kpi_fk=self.set_integ_fk),
numerator_id=Const.MANUFACTURER_FK, denominator_id=self.store_id)
if result_value == Const.FAIL:
score = 0
self.write_to_db_result(
self.common_db.get_kpi_fk_by_kpi_name(kpi_name, 2), score=score, level=2)
self.write_to_db_result(
self.common_db.get_kpi_fk_by_kpi_name(kpi_name, 3), score=score, level=3, display_text=display_text)
self.write_to_db_result(self.common_db_integ.get_kpi_fk_by_kpi_name(
kpi_name, 3), score=score, level=3, display_text=kpi_name, set_type=Const.MANUAL)
def write_to_db_result(self, fk, level, score, set_type=Const.SOVI, **kwargs):
"""
This function creates the result data frame of every KPI (atomic KPI/KPI/KPI set),
and appends the insert SQL query into the queries' list, later to be written to the DB.
"""
if kwargs:
kwargs['score'] = score
attributes = self.create_attributes_dict(fk=fk, level=level, set_type=set_type, **kwargs)
else:
attributes = self.create_attributes_dict(fk=fk, score=score, set_type=set_type, level=level)
if level == self.common_db.LEVEL1:
table = self.common_db.KPS_RESULT
elif level == self.common_db.LEVEL2:
table = self.common_db.KPK_RESULT
elif level == self.common_db.LEVEL3:
table = self.common_db.KPI_RESULT
else:
return
query = insert(attributes, table)
if set_type == Const.SOVI:
self.common_db.kpi_results_queries.append(query)
else:
self.common_db_integ.kpi_results_queries.append(query)
def create_attributes_dict(self, score, fk=None, level=None, display_text=None, set_type=Const.SOVI, **kwargs):
"""
This function creates a data frame with all attributes needed for saving in KPI results tables.
or
you can send dict with all values in kwargs
"""
kpi_static_data = self.kpi_static_data if set_type == Const.SOVI else self.kpi_static_data_integ
if level == self.common_db.LEVEL1:
if kwargs:
kwargs['score'] = score
values = [val for val in kwargs.values()]
col = [col for col in kwargs.keys()]
attributes = pd.DataFrame(values, columns=col)
else:
kpi_set_name = kpi_static_data[kpi_static_data['kpi_set_fk'] == fk]['kpi_set_name'].values[0]
attributes = pd.DataFrame(
[(kpi_set_name, self.session_uid, self.store_id, self.visit_date.isoformat(),
format(score, '.2f'), fk)],
columns=['kps_name', 'session_uid', 'store_fk', 'visit_date', 'score_1', 'kpi_set_fk'])
elif level == self.common_db.LEVEL2:
if kwargs:
kwargs['score'] = score
values = [val for val in kwargs.values()]
col = [col for col in kwargs.keys()]
attributes = pd.DataFrame(values, columns=col)
else:
kpi_name = kpi_static_data[kpi_static_data['kpi_fk'] == fk]['kpi_name'].values[0].replace("'", "\\'")
attributes = pd.DataFrame(
[(self.session_uid, self.store_id, self.visit_date.isoformat(), fk, kpi_name, score)],
columns=['session_uid', 'store_fk', 'visit_date', 'kpi_fk', 'kpk_name', 'score'])
elif level == self.common_db.LEVEL3:
if kwargs:
kwargs['score'] = score
values = tuple([val for val in kwargs.values()])
col = [col for col in kwargs.keys()]
attributes = pd.DataFrame([values], columns=col)
else:
data = kpi_static_data[kpi_static_data['atomic_kpi_fk'] == fk]
kpi_fk = data['kpi_fk'].values[0]
kpi_set_name = kpi_static_data[kpi_static_data['atomic_kpi_fk'] == fk]['kpi_set_name'].values[0]
attributes = pd.DataFrame(
[(display_text, self.session_uid, kpi_set_name, self.store_id, self.visit_date.isoformat(),
datetime.utcnow().isoformat(), score, kpi_fk, fk)],
columns=['display_text', 'session_uid', 'kps_name', 'store_fk', 'visit_date',
'calculation_time', 'score', 'kpi_fk', 'atomic_kpi_fk'])
else:
attributes = pd.DataFrame()
return attributes.to_dict()
def remove_queries_of_calculation_type(self):
"""
In case that the session has no results in the SOVI KPIs we are deleting all the queries
and calculating the MANUAL
:return:
"""
self.common_db2.kpi_results = pd.DataFrame(columns=self.common_db2.COLUMNS)
def commit_results(self):
"""
committing the results in both sets
"""
self.common_db.delete_results_data_by_kpi_set()
self.common_db.commit_results_data_without_delete()
if self.common_db_integ:
self.common_db_integ.delete_results_data_by_kpi_set()
self.common_db_integ.commit_results_data_without_delete()