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 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))