def Plot_Social_Welfare(task_n, provider_n, max_value, max_alpha, max_deadline, max_task_size, max_mu, max_provider_bid, max_provider_skill, task_columns, provider_columns):
tasks = TaskCreator(task_n, max_value, max_alpha, max_deadline, max_task_size)
providers = ProviderCreator(provider_n, max_mu, max_provider_bid, max_provider_skill)
pro_exp_social = []
exp_social = []
pro_real_social = []
real_social = []
for i in range(1,11):
capacity = 20*i
auctioneer = Platform(capacity)
#platform에서 winners를 선택한다.
W_requesters, req_threshold = auctioneer.WinningRequesterSelection(tasks)
W_providers, pro_threshold = auctioneer.WinningProviderSelection(providers)
#혹시 모르니깐 requesters와 providers의 숫자를 맞춘다.
W_requesters, W_providers = auctioneer.Trimming(W_requesters, W_providers)
#requesters와 providers의 preference ordering을 만든다.
requester_preference_ordering = auctioneer.ordering_matrix(W_providers,provider_columns)
provider_preference_ordering = auctioneer.ordering_matrix(W_requesters,task_columns)
b_rank, b_mean_rank = benchmark_satisfaction_level(requester_preference_ordering)
b_pdf, b_cdf = bench_distribution(b_rank)
match = auctioneer.SMA(requester_preference_ordering, provider_preference_ordering)
#In match, requester: keys, providers: values
#expected social welfare comparison
social_welfare = SocialWelfare(W_requesters, W_providers, 0.01, match)
benchmark = SystemExpectedSocialwelfare(W_requesters, W_providers, 0.01)
pro_exp_social.append(social_welfare)
exp_social.append(benchmark)
rank, mean_rank = auctioneer.satisfaction_level(match, requester_preference_ordering)
pdf, cdf = auctioneer.satisfaction_distribution(rank)
#real social welfare
welfare, t_sub = Proposed_SocialWelfare(W_requesters, W_providers, 0.01, match)
bench_welfare = Existing_SocialWelfare(W_requesters, W_providers, 0.01)
pro_real_social.append(welfare)
real_social.append(bench_welfare)
x_axis = np.array(range(1,11))*20
bar_width = 10
fig, ax = plt.subplots(nrows = 1, ncols = 2)
ax[0].bar(x_axis, pro_exp_social, width = bar_width, label = 'proposed exp.SW', color = 'b')
ax[0].bar(x_axis+bar_width, exp_social, width = bar_width, label = 'benchmark exp.SW', color = 'r')
ax[0].legend(loc = 'best')
ax[1].bar(x_axis, pro_real_social, width = bar_width, label = 'proposed real.SW', color = 'b')
ax[1].bar(x_axis+bar_width, real_social, width = bar_width, label = 'benchmark real.SW', color = 'r')
ax[1].legend(loc = 'best')
plt.show()
def SW2(tasks, providers, capacity_unit, capacity_range, power4requester, power4provider):
result = []
for idx in range(1, capacity_range + 1):
#create an auctioneer
capacity = capacity_unit*idx
auctioneer = Platform(capacity)
start_time = time.process_time()
#winner selection process with consideration to alpha and mu
W_requesters, req_threshold = auctioneer.New_WinningRequesterSelection(tasks, power4requester)
W_providers, pro_threshold = auctioneer.New_WinningProviderSelection(providers, power4provider)
#trimming process
W_requesters, W_providers, req_threshold, pro_threshold = auctioneer.Trimming(W_requesters, W_providers, req_threshold, pro_threshold)
#cost calculation
task_size = TaskSizer(W_requesters)
cost = CostCalculator(W_providers, task_size)
#payment calculation
payment = auctioneer.New_WPS_payment(W_providers, pro_threshold, power4provider)
payment = payment*task_size
#fee calculation
fee = auctioneer.New_WRS_payment(W_requesters, req_threshold, power4requester)
#mere social welfare
mere_SW = MereSW(W_requesters) - cost
#expected social welfare
expected_SW = ExpectedSW(W_requesters, W_providers) - cost
#actually realized social welfare
realized_SW, t_sub = PostSW(W_requesters, W_providers)
realized_SW = realized_SW - cost
#budget balance check before submission
pre_budget = BudgetBalanceCheck(fee, payment)
#budget balance after submission
changed_fee, changed_payment = TimeVariantMoney(t_sub, W_requesters, W_providers, fee, payment)
post_budget = BudgetBalanceCheck(changed_fee, changed_payment)
end_time = time.process_time()
running_time = end_time - start_time
#budget balance check
if pre_budget < 0: #budget balance not met
result.append([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, running_time]) #return all zeros
else: #budget balance met
result.append([mere_SW, expected_SW, realized_SW, pre_budget, post_budget, payment.sum(), fee.sum(), changed_payment.sum(), changed_fee.sum(), cost, running_time])#proposed heuristic approach
return np.array(result)
def SW1(tasks, providers, capacity_unit, capacity_range): #simple greedy approach
result = []
for idx in range(1, capacity_range + 1):
#create an auctioneer
capacity = capacity_unit*idx
auctioneer = Platform(capacity)
start_time = time.process_time()
#winner selection process
W_requesters, req_threshold = auctioneer.WinningRequesterSelection(tasks)
W_providers, pro_threshold = auctioneer.WinningProviderSelection(providers)
#trimming process: make the # of selected requesters and providers equal
W_requesters, W_providers, req_threshold, pro_threshold = auctioneer.Trimming(W_requesters, W_providers, req_threshold, pro_threshold)
#cost calculation
task_size = TaskSizer(W_requesters)
cost = CostCalculator(W_providers, task_size)
#calculate the payment to providers which guarantees truthfulness of providers
payment = auctioneer.WPS_payment(W_providers, pro_threshold) #unit payment
payment = payment*task_size #effective payment
#calculate the fee for requesters which guarantees their truthfulness
fee = auctioneer.WRS_payment(W_requesters, req_threshold)
#mere social welfare: simple summation of task values without considering task depreciation after deadline
mere_SW = MereSW(W_requesters) - cost
#expected social welfare: summation of task values considering task depreciation after deadline
expected_SW = ExpectedSW(W_requesters, W_providers) - cost
#actually realized social welfare
realized_SW, t_sub = PostSW(W_requesters, W_providers)
realized_SW = realized_SW - cost
#budget balance check before submission
pre_budget = BudgetBalanceCheck(fee, payment)
#budget balance after submission
changed_fee, changed_payment = TimeVariantMoney(t_sub, W_requesters, W_providers, fee, payment)
post_budget = BudgetBalanceCheck(changed_fee, changed_payment) #without consideration to alpha, mu and preference
end_time = time.process_time()
running_time = end_time - start_time
#budget balance check
if pre_budget < 0: #budget balance not met. Note that we can only check the budget balance before providers' actual submission
result.append([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, running_time]) #return all zeros
else: #budget balance met
result.append([mere_SW, expected_SW, realized_SW, pre_budget, post_budget, payment.sum(), fee.sum(), changed_payment.sum(), changed_fee.sum(), cost, running_time])
return np.array(result)
def SW(time_unit, task_n, provider_n, max_value, max_deadline, max_alpha, max_task_size, max_provider_bid, max_provider_skill, max_mu, task_columns, provider_columns, iteration, capacity, output):
#mere social welfare
mere1 = []
mere2 = []
#expected social welfare
expected1 = []
expected2 = []
expected3 = []
#real social welfare
real1 = []
real2 = []
real3 = []
#budget before submission
before1 = []
before2 = []
before3 = []
#budget after submission
after1 = []
after2 = []
after3 = []
#payment before submission
p_before1 = []
p_before2 = []
#fee before submission
f_before1 = []
f_before2 = []
#payment after submission
p_after1 = []
p_after2 = []
p_after3 = []
#fee after submission
f_after1 = []
f_after2 = []
f_after3 = []
#cost
cost1 = []
cost2 = []
for _ in range(iteration):
#participants creation
tasks = TaskCreator(task_n, max_value, max_alpha, max_deadline, max_task_size)
providers = ProviderCreator(provider_n, max_mu, max_provider_bid, max_provider_skill)
auctioneer = Platform(capacity)
#winner selection
#without consideration to alpha and mu
W_requesters, req_threshold = auctioneer.WinningRequesterSelection(tasks)
W_providers, pro_threshold = auctioneer.WinningProviderSelection(providers)
#with consideration to alpha and mu
New_W_requesters, New_req_threshold = auctioneer.New_WinningRequesterSelection(tasks)
New_W_providers, New_pro_threshold = auctioneer.New_WinningProviderSelection(providers)
#trimming process
W_requesters, W_providers = auctioneer.Trimming(W_requesters, W_providers)
New_W_requesters, New_W_providers = auctioneer.Trimming(New_W_requesters, New_W_providers)
c1 = cost_calculator(W_providers)
c2 = cost_calculator(New_W_providers)
#payment
#temporary payment without consideration to alpha, mu
payment1 = auctioneer.WPS_payment(W_providers, pro_threshold)
#temporary fee
fee1 = auctioneer.WRS_payment(W_requesters, req_threshold)
#temporary payment with consideration to alpha, mu
payment2 = auctioneer.New_WPS_payment(New_W_providers, New_pro_threshold)
#temporary fee
fee2 = auctioneer.New_WRS_payment(New_W_requesters, New_req_threshold)
#1. preference ordering
#satisfaction level without consideration of preference but with alpha, mu: 2
New_requester_preference_ordering = auctioneer.ordering_matrix(New_W_providers,provider_columns)
New_provider_preference_ordering = auctioneer.ordering_matrix(New_W_requesters,task_columns)
New_r_rank, New_r_mean_rank = benchmark_satisfaction_level(New_requester_preference_ordering)
New_p_rank, New_p_mean_rank = benchmark_satisfaction_level(New_provider_preference_ordering)
New_r_pdf, New_r_cdf = bench_distribution(New_r_rank)
New_p_pdf, New_p_cdf = bench_distribution(New_p_rank)
#Run Stable Marriage Algorithm: In match, requester: keys, providers: values
match = auctioneer.SMA(New_requester_preference_ordering, New_provider_preference_ordering)
#mere social welfare---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
result = mere_social_welfare(W_requesters) #without consideration to alpha, mu
result1 = mere_social_welfare(New_W_requesters) #with consideration to alpha, mu
#print('mere social welfare 1: %f' %result)
#print('mere social welfare 2: %f' %result1)
#expected social welfare---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
social_welfare1 = SystemExpectedSocialwelfare(W_requesters, W_providers, time_unit) #without consideration to preference & alpha, mu
social_welfare2 = SystemExpectedSocialwelfare(New_W_requesters, New_W_providers, time_unit) #with consideration to alpha, mu but without preference
social_welfare3 = SocialWelfare(New_W_requesters, New_W_providers, time_unit, match) #With consideration to preference & alpha, mu
#print('expected social welfare 1: %f' %social_welfare1)
#print('expected social welfare 2: %f' %social_welfare2)
#print('expected social welfare 3: %f' %social_welfare3)
#print('D 1:%f' %(result-social_welfare1))
#print('D 2:%f' %(result1-social_welfare2))
#print('D 3:%f' %(result1-social_welfare3))
#real social welfare with submission time---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
post_social_welfare1, t_sub1 = Existing_SocialWelfare(W_requesters, W_providers, time_unit) #without consideration to preference, alpha, mu
post_social_welfare2, t_sub2 = Existing_SocialWelfare(New_W_requesters, New_W_providers, time_unit) #with consideration to alpha, mu
post_social_welfare3, t_sub3 = Proposed_SocialWelfare(New_W_requesters, New_W_providers, time_unit, match) #with consideration to preference & alpha, mu
#print('real social welfare 1:%f' %post_social_welfare1)
#print('real social welfare 2:%f' %post_social_welfare2)
#print('real social welfare 3:%f' %post_social_welfare3)
#difference between expected social welfare and real social welfare
#print('difference 1: %f' %(social_welfare1-post_social_welfare1))
#print('difference 2: %f' %(social_welfare2-post_social_welfare2))
#print('difference 3: %f' %(social_welfare3-post_social_welfare3))
#budget balance check before submission---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
net_budget1 = budget_balance_check(fee1, payment1) #without consideration to alpha, mu
net_budget2 = budget_balance_check(fee2, payment2) #with consideration to alpha, mu
net_budget3 = budget_balance_check(fee2, payment2) #with consideration to alpha, mu and preference
#print('budget before submission(without alpha, mu, preference): %f' %net_budget1)
#print('budget before submission(with alpha and mu): %f' %net_budget2)
#print('budget before submission(with alpha, mu and preference): %f' %net_budget3)
#budget balance after submission---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
changed_fee1, changed_payment1 = bench_time_variant_money(t_sub1, W_requesters, W_providers, fee1, payment1, time_unit)
changed_fee2, changed_payment2 = bench_time_variant_money(t_sub2, New_W_requesters, New_W_providers, fee2, payment2, time_unit)
changed_fee3, changed_payment3 = time_variant_money(t_sub3, New_W_requesters, New_W_providers, fee2, payment2, match, time_unit)
changed_budget1 = budget_balance_check(changed_fee1, changed_payment1) #without consideration to alpha, mu and preference
changed_budget2 = budget_balance_check(changed_fee2, changed_payment2) #with consideration to alpha, mu
changed_budget3 = budget_balance_check(changed_fee3, changed_payment3) #with consideration to alpha, mu and preference
#print('budget after submission(without alpha,mu, preference): %f' %changed_budget1)
#print('budget after submission(with alpha,mu): %f' %changed_budget2)
#print('budget after submission(with alpha,mu, preference): %f' %changed_budget3)
#print('iteration: %f, capacity: %f' %(it, unit*(ran+1)))
#budget balance check
if net_budget1 < 0:
result = 0
social_welfare1 = 0
post_social_welfare1 = 0
net_budget1 = 0
changed_budget1 = 0
payment1 = 0
fee1 = 0
changed_payment1 = 0
changed_fee1 = 0
c1 = 0
if net_budget2 < 0:
result1 = 0
social_welfare2 = 0; social_welfare3 = 0
post_social_welfare2 = 0; post_social_welfare3 = 0
net_budget2 = 0; net_budget3 = 0
changed_budget2 = 0; changed_budget3 = 0
payment2 = 0; fee2 = 0
changed_payment2 = 0; changed_payment3 = 0
changed_fee2 = 0; changed_fee3 = 0
c2 = 0
#mere social welfare
mere1.append(result)
mere2.append(result1)
#expected social welfare
expected1.append(social_welfare1)
expected2.append(social_welfare2)
expected3.append(social_welfare3)
#real social welfare
real1.append(post_social_welfare1)
real2.append(post_social_welfare2)
real3.append(post_social_welfare3)
#budget before submission
before1.append(net_budget1)
before2.append(net_budget2)
before3.append(net_budget3)
#budget after submission
after1.append(changed_budget1)
after2.append(changed_budget2)
after3.append(changed_budget3)
#payment before submission
p_before1.append(np.sum(payment1))
p_before2.append(np.sum(payment2))
#fee before submission
f_before1.append(np.sum(fee1))
f_before2.append(np.sum(fee2))
#payment after submission
p_after1.append(np.sum(changed_payment1))
p_after2.append(np.sum(changed_payment2))
p_after3.append(np.sum(changed_payment3))
#fee after submission
f_after1.append(np.sum(changed_fee1))
f_after2.append(np.sum(changed_fee2))
f_after3.append(np.sum(changed_fee3))
#cost
cost1.append(c1)
cost2.append(c2)
#mere social welfare
mere1 = [x for x in mere1 if x != 0]
mere2 = [x for x in mere2 if x != 0]#
if len(mere2) == 0:
mere2 = 0
#expected social welfare
expected1 = [x for x in expected1 if x != 0]
expected2 = [x for x in expected2 if x != 0]#
expected3 = [x for x in expected3 if x != 0]#
if len(expected2) == 0:
expected2 = 0
if len(expected3) == 0:
expected3 = 0
#real social welfare
real1 = [x for x in real1 if x != 0]
real2 = [x for x in real2 if x != 0]#
real3 = [x for x in real3 if x != 0]#
if len(real2) == 0:
real2 = 0
if len(real3) == 0:
real3 = 0
#budget before submission
before1 = [x for x in before1 if x != 0]
before2 = [x for x in before2 if x != 0]#
before3 = [x for x in before3 if x != 0]#
if len(before2) == 0:
before2 = 0
if len(before3) == 0:
before3 = 0
#budget after submission
after1 = [x for x in after1 if x != 0]
after2 = [x for x in after2 if x != 0]#
after3 = [x for x in after3 if x != 0]#
if len(after2) == 0:
after2 = 0
if len(after3) == 0:
after3 = 0
#payment before submission
p_before1 = [x for x in p_before1 if x != 0]
p_before2 = [x for x in p_before2 if x != 0]#
if len(p_before2) == 0:
p_before2 = 0
#fee before submission
f_before1 = [x for x in f_before1 if x != 0]
f_before2 = [x for x in f_before2 if x != 0]#
if len(f_before2) == 0:
f_before2 = 0
#payment after submission
p_after1 = [x for x in p_after1 if x != 0]
p_after2 = [x for x in p_after2 if x != 0]#
p_after3 = [x for x in p_after3 if x != 0]#
if len(p_after3) == 0:
p_after3 = 0
if len(p_after2) == 0:
p_after2 = 0
#fee after submission
f_after1 = [x for x in f_after1 if x != 0]
f_after2 = [x for x in f_after2 if x != 0]#
f_after3 = [x for x in f_after3 if x != 0]#
if len(f_after3) == 0:
f_after3 = 0
if len(f_after2) == 0:
f_after2 = 0
#cost
cost1 = [x for x in cost1 if x != 0]
cost2 = [x for x in cost2 if x != 0]#
if len(cost2) == 0:
cost2 = 0
#output.put((mere1,mere2,expected1,expected2,expected3,real1,real2,real3,before1,before2, before3, after1, after2, after3, p_before1, p_before2, f_before1, f_before2, p_after1, p_after2, p_after3,
#f_after1, f_after2, f_after3, cost1, cost2))
output.put((np.mean(mere1), np.mean(mere2), np.mean(expected1), np.mean(expected2), np.mean(expected3), np.mean(real1), np.mean(real2), np.mean(real3),
np.mean(before1), np.mean(before2), np.mean(before3), np.mean(after1), np.mean(after2), np.mean(after3), np.mean(p_before1), np.mean(p_before2), np.mean(f_before1),
np.mean(f_before2), np.mean(p_after1), np.mean(p_after2), np.mean(p_after3), np.mean(f_after1), np.mean(f_after2), np.mean(f_after3), np.mean(cost1), np.mean(cost2)))
def preference_cdf(time_unit, task_n, provider_n, capacity, max_value, max_deadline, max_alpha, max_task_size, max_provider_bid, max_provider_skill, max_mu, task_columns, provider_columns, iteration, output):
r_distribution1 = []
r_distribution2 = []
r_distribution3 = []
r_cumulative1 = []
r_cumulative2 = []
r_cumulative3 = []
p_distribution1 = []
p_distribution2 = []
p_distribution3 = []
p_cumulative1 = []
p_cumulative2 = []
p_cumulative3 = []
r_mean1=[]
r_mean2=[]
r_mean3=[]
p_mean1=[]
p_mean2=[]
p_mean3=[]
for _ in range(iteration):
tasks = TaskCreator(task_n, max_value, max_alpha, max_deadline, max_task_size)
providers = ProviderCreator(provider_n, max_mu, max_provider_bid, max_provider_skill)
auctioneer = Platform(capacity)
#without consideration to alpha and mu '''winner selection'''
W_requesters, req_threshold = auctioneer.WinningRequesterSelection(tasks)
W_providers, pro_threshold = auctioneer.WinningProviderSelection(providers)
#with consideration to alpha and mu
New_W_requesters, New_req_threshold = auctioneer.New_WinningRequesterSelection(tasks)
New_W_providers, New_pro_threshold = auctioneer.New_WinningProviderSelection(providers)
#trimming process
W_requesters, W_providers = auctioneer.Trimming(W_requesters, W_providers)
New_W_requesters, New_W_providers = auctioneer.Trimming(New_W_requesters, New_W_providers)
#1. preference ordering
#satisfaction level without consideration of preference, alpha and mu: 1
requester_preference_ordering = auctioneer.ordering_matrix(W_providers,provider_columns)
provider_preference_ordering = auctioneer.ordering_matrix(W_requesters,task_columns)
r_rank, r_mean_rank = benchmark_satisfaction_level(requester_preference_ordering)
p_rank, p_mean_rank = benchmark_satisfaction_level(provider_preference_ordering)
r_pdf1, r_cdf1 = bench_distribution(r_rank)
p_pdf1, p_cdf1 = bench_distribution(p_rank)
r_distribution1.append(r_pdf1)
r_cumulative1.append(r_cdf1)
p_distribution1.append(p_pdf1)
p_cumulative1.append(p_cdf1)
r_mean1.append(r_mean_rank)
p_mean1.append(p_mean_rank)
#satisfaction level without consideration of preference but with alpha, mu: 2
New_requester_preference_ordering = auctioneer.ordering_matrix(New_W_providers,provider_columns)
New_provider_preference_ordering = auctioneer.ordering_matrix(New_W_requesters,task_columns)
New_r_rank, New_r_mean_rank = benchmark_satisfaction_level(New_requester_preference_ordering)
New_p_rank, New_p_mean_rank = benchmark_satisfaction_level(New_provider_preference_ordering)
r_pdf2, r_cdf2 = bench_distribution(New_r_rank)
p_pdf2, p_cdf2 = bench_distribution(New_p_rank)
r_distribution2.append(r_pdf2)
p_distribution2.append(p_pdf2)
r_cumulative2.append(r_cdf2)
p_cumulative2.append(p_cdf2)
r_mean2.append(New_r_mean_rank)
p_mean2.append(New_p_mean_rank)
#Run Stable Marriage Algorithm: In match, requester: keys, providers: values
match = auctioneer.SMA(New_requester_preference_ordering, New_provider_preference_ordering)
#satisfaction with consideration to preference, alpha, mu:3
#requesters
r_rank3, r_mean_rank3 = auctioneer.satisfaction_level(match, New_requester_preference_ordering)
#providers
p_rank3, p_mean_rank3 = auctioneer.satisfaction_level(match, New_provider_preference_ordering)
r_pdf3, r_cdf3 = auctioneer.satisfaction_distribution(r_rank3)
p_pdf3, p_cdf3 = auctioneer.satisfaction_distribution(p_rank3)
r_distribution3.append(r_pdf3)
p_distribution3.append(p_pdf3)
r_cumulative3.append(r_cdf3)
p_cumulative3.append(p_cdf3)
r_mean3.append(r_mean_rank3)
p_mean3.append(p_mean_rank3)
r_distribution1 = np.array(r_distribution1).mean(axis = 0)
r_distribution2 = np.array(r_distribution2).mean(axis = 0)
r_distribution3 = np.array(r_distribution3).mean(axis = 0)
r_cumulative1 = np.array(r_cumulative1).mean(axis = 0)
r_cumulative2 = np.array(r_cumulative2).mean(axis = 0)
r_cumulative3 = np.array(r_cumulative3).mean(axis = 0)
p_distribution1 = np.array(p_distribution1).mean(axis = 0)
p_distribution2 = np.array(p_distribution2).mean(axis = 0)
p_distribution3 = np.array(p_distribution3).mean(axis = 0)
p_cumulative1 = np.array(p_cumulative1).mean(axis = 0)
p_cumulative2 = np.array(p_cumulative2).mean(axis = 0)
p_cumulative3 = np.array(p_cumulative3).mean(axis = 0)
r_mean1= np.array(r_mean1).mean()
r_mean2= np.array(r_mean2).mean()
r_mean3= np.array(r_mean3).mean()
p_mean1= np.array(p_mean1).mean()
p_mean2= np.array(p_mean2).mean()
p_mean3= np.array(p_mean3).mean()
output.put((r_distribution1, r_distribution2, r_distribution3, r_cumulative1, r_cumulative2, r_cumulative3, p_distribution1, p_distribution2, p_distribution3, p_cumulative1, p_cumulative2, p_cumulative3,
r_mean1, r_mean2, r_mean3, p_mean1, p_mean2, p_mean3))