import random
from datetime import date
from app.core.database import get_db
from app.core.models.profile import Profile
from app.core.models.scoring_enums import ProfileMilitaryServiceStatusEnum
from app.core.services.data_service import DataService
if __name__ == '__main__':
ds = DataService(get_db())
ds.delete_profiles({})
for i in range(2):
p = Profile(user_id=int(100 + i))
p.has_kyc = 1
p.military_service_status = ProfileMilitaryServiceStatusEnum.FINISHED
p.sim_card_ownership = 1
p.address_verification = 1
p.membership_date = date.today()
p.recommended_to_others_count = 23
p.star_count_average = 1
p.identities_score = random.randint(0, 100) # 10%
p.histories_score = random.randint(0, 300) # 30%
p.volumes_score = random.randint(0, 250) # 25%
p.timeliness_score = random.randint(0, 350) # 35%
p.score = p.identities_score + p.histories_score + p.volumes_score + p.timeliness_score
ds.insert_profile(p)
# Test aggregation pipeline run performance
# pipeline = generate_scores_distributions_pipeline(0, 1000, 20)
# ref: CommandCursor = ds.db.profiles.aggregate(pipeline)
def calculate_score(scenarios_dicts: [], user_id: int = None):
# mongoengine_api.launch_app()
ds = DataService()
rds = RedisCaching(ds)
cs = ScoreCalculationService_2(rds, ds)
# crm = RedisCachingRulesMasters(rds.rds)
for scn_dict in scenarios_dicts:
expected_score = scn_dict['Vscore']
# DoneTrade Score Calculation ..................................................
dt = DoneTrade(user_id=user_id)
dt.timely_trades_count_of_last_3_months = scn_dict['Last3MSD']
dt.timely_trades_count_between_last_3_to_12_months = scn_dict[
'Last1YSD']
dt.past_due_trades_count_of_last_3_months = scn_dict[
'B30DayDelayLast3M']
dt.past_due_trades_count_between_last_3_to_12_months = scn_dict[
'B30DayDelayLast3-12M']
dt.arrear_trades_count_of_last_3_months = scn_dict['A30DayDelayLast3M']
dt.arrear_trades_count_between_last_3_to_12_months = scn_dict[
'A30DayDelay3-12M']
dt.total_delay_days = scn_dict['AverageDelayRatio']
# todo: 100000000 is fix Denominator that is all_other_users_done_trades_amount, it should be change later
dt.trades_total_balance = round(
float(scn_dict['SDealAmountRatio']) *
ALL_USERS_AVERAGE_DEAL_AMOUNT)
done_trades_score = cs.calculate_user_done_trades_score_2(
user_id=0, modified_done_trade=dt)
ds.delete_done_trades({USER_ID: user_id})
ds.insert_done_trade(dt)
# UndoneTrade Score Calculation ..................................................
udt = UndoneTrade(user_id=user_id)
udt.undue_trades_count = scn_dict['NumNotDueDeal']
udt.past_due_trades_count = scn_dict['UnfinishedB30DayDelay']
udt.arrear_trades_count = scn_dict['UnfinishedA30DayDelay']
udt.undue_trades_total_balance_of_last_year = round(
float(scn_dict['NotDueDealAmountRatio']) * dt.trades_total_balance)
udt.past_due_trades_total_balance_of_last_year = round(
float(scn_dict['UnfinishedB30Din1YRatio']) *
dt.trades_total_balance)
udt.arrear_trades_total_balance_of_last_year = round(
float(scn_dict['UnfinishedA30Din1YRatio']) *
dt.trades_total_balance)
undone_trades_score = cs.calculate_user_undone_trades_score_2(
user_id=0, undone_trade_object=udt, done_trade_object=dt)
ds.delete_undone_trades({USER_ID: user_id})
ds.insert_undone_trade(udt)
# Loan Score Calculation ..................................................
ln = Loan(user_id=user_id)
ln.loans_total_count = scn_dict['Loans']
ln.loans_total_balance = ALL_USERS_AVERAGE_PRINCIPAL_INTEREST_AMOUNT
ln.past_due_loans_total_count = int(scn_dict['PastDueLoans'])
ln.arrear_loans_total_count = int(scn_dict['DelayedLoans'])
ln.suspicious_loans_total_count = int(
scn_dict['DoubfulCollectionLoans'])
ln.monthly_installments_total_balance = float(
scn_dict['MonthlyInstallments'])
ln.overdue_loans_total_balance = round(
float(scn_dict['CurrentLoanAmountRatio']) * ln.loans_total_balance)
ln.past_due_loans_total_balance = round(
float(scn_dict['PastDueLoanAmountRatio']) * ln.loans_total_balance)
ln.arrear_loans_total_balance = round(
float(scn_dict['DelayedLoanAmountRatio']) * ln.loans_total_balance)
ln.suspicious_loans_total_balance = round(
float(scn_dict['DoubtfulCollectionAmountRatio']) *
ln.loans_total_balance)
loan_score = cs.calculate_user_loans_score_2(user_id=0, loan_object=ln)
ds.delete_loans({USER_ID: user_id})
ds.insert_loan(ln)
# Cheque Score Calculation ..................................................
ch = Cheque(user_id=user_id)
ch.unfixed_returned_cheques_count_of_last_3_months = scn_dict[
'DishonouredChequesL3M']
ch.unfixed_returned_cheques_count_between_last_3_to_12_months = scn_dict[
'DishonouredChequesL3-12M']
ch.unfixed_returned_cheques_count_of_more_12_months = scn_dict[
'DishonouredChequesA12M']
ch.unfixed_returned_cheques_count_of_last_5_years = scn_dict[
'AllDishonouredCheques']
ch.unfixed_returned_cheques_total_balance = round(
float(scn_dict['DCAmountRatio']) *
ALL_USERS_AVERAGE_UNFIXED_RETURNED_CHEQUES_AMOUNT)
cheque_score: [] = cs.calculate_user_cheques_score_2(user_id=0,
cheque_object=ch)
ds.delete_cheques({USER_ID: user_id})
ds.insert_cheque(ch)
# Profile Score Calculation ..................................................
if user_id is not None:
ds.delete_profiles({USER_ID: user_id})
p = Profile(user_id=user_id)
p.has_kyc = scn_dict['KYC']
p.military_service_status = ProfileMilitaryServiceStatusEnum.__getitem__(
scn_dict['Military'])
p.sim_card_ownership = scn_dict['SimCard']
p.address_verification = scn_dict['Address']
p.membership_date = date.today() - timedelta(
days=int(scn_dict['Membership']))
p.recommended_to_others_count = scn_dict['Recommendation']
p.star_count_average = scn_dict['WeightedAveStars']
profile_score = cs.calculate_user_profile_score_2(user_id=0,
profile_object=p)
# total_pure_score = int(profile_score) + int(done_trades_score) + int(undone_trades_score) + int(loan_score) + int(cheque_score)
identities_pure_score = cs.scores_dict.get(IDENTITIES_SCORE)
identities_normalized_score = cs.calculate_identities_normalized_score_2(
identities_pure_score)
histories_pure_score = cs.scores_dict.get(HISTORIES_SCORE)
histories_normalized_score = cs.calculate_histories_normalized_score_2(
histories_pure_score)
volumes_pure_score = cs.scores_dict.get(VOLUMES_SCORE)
volumes_normalized_score = cs.calculate_volumes_normalized_score_2(
volumes_pure_score)
timeliness_pure_score = cs.scores_dict.get(TIMELINESS_SCORE)
timeliness_normalized_score = cs.calculate_timeliness_normalized_score_2(
timeliness_pure_score)
total_pure_score = identities_pure_score + histories_pure_score + volumes_pure_score + timeliness_pure_score
total_normalized_score = identities_normalized_score + histories_normalized_score + volumes_normalized_score + timeliness_normalized_score
print(
'<><><><><><><> expected-score= {} , total_pure_score = {} and total_normalized_score = {} '
'<><><><><><><>'.format(expected_score, total_pure_score,
total_normalized_score))
# Profile Score insertion
p.score = total_normalized_score
p.identities_score = identities_normalized_score
p.histories_score = histories_normalized_score
p.volumes_score = volumes_normalized_score
p.timeliness_score = timeliness_normalized_score
ds.delete_profiles({USER_ID: user_id})
ds.insert_profile(p)