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platforms.py
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platforms.py
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import numpy as np
import pandas as pd
import random
import itertools
class Platform():
def __init__(self, capacity):
self.capacity = capacity #a platform has a limited capacity to handle requested tasks
def requester_info_matrix(self, tasks): #return requesters' bid information as a dataframe
preference_matrix=[]
columns = ['alpha', 'deadline', 'bid to size ratio']
for task in tasks:
preference_matrix.append([task.alpha_, task.deadline_, task.bid_/task.task_size_])
preference = pd.DataFrame(preference_matrix, columns = columns)
return preference
def provider_info_matrix(self, providers): #return providers' bid information as a dataframe
preference_matrix = []
columns=['mu','bid']
for provider in providers:
preference_matrix.append([provider.mean_, provider.bid_])
preference = pd.DataFrame(preference_matrix, columns = columns)
return preference
def WinningRequesterSelection(self, task_creatures):
preference_matrix = self.requester_info_matrix(task_creatures)
selection_metrics = preference_matrix['bid to size ratio']
#In numpy.argsort, its default sorting is in ascending manner. What we want is in descending order
selection_indices = np.argsort(selection_metrics)[::-1]
if len(task_creatures) > self.capacity:
return task_creatures[selection_indices][:self.capacity], task_creatures[selection_indices][self.capacity]
elif len(task_creatures) == self.capacity:
return task_creatures[selection_indices][:self.capacity-1], task_creatures[selection_indices][self.capacity-1]
else:
return task_creatures[selection_indices][:len(task_creatures)-1], task_creatures[selection_indices][len(task_creatures)-1]
def WinningProviderSelection(self, provider_creatures):
#columns=['mu','bid']
#the smaller mu, the better
#the smaller bid, the better
preference_matrix = self.provider_info_matrix(provider_creatures)
#selection_metrics = preference_matrix['bid'] / preference_matrix['skill']
selection_metrics = preference_matrix['bid'] #selection criterion: bid (ask value)
selection_indices = np.argsort(selection_metrics)
if len(provider_creatures) > self.capacity:
return provider_creatures[selection_indices][:self.capacity], provider_creatures[selection_indices][self.capacity]
elif len(provider_creatures) == self.capacity:
return provider_creatures[selection_indices][:self.capacity-1], provider_creatures[selection_indices][self.capacity-1]
else:
return provider_creatures[selection_indices][:len(provider_creatures)-1], provider_creatures[selection_indices][len(provider_creatures)-1]
def New_WinningRequesterSelection(self, task_creatures, constant):
preference_matrix = self.requester_info_matrix(task_creatures)
max_alpha = (max(preference_matrix['alpha'])**constant)
max_ratio = max(preference_matrix['bid to size ratio'])
#constant가 추가되었다.
selection_metrics = (preference_matrix['bid to size ratio'] / max_ratio) / (preference_matrix['alpha']**constant/max_alpha)
#In numpy.argsort, its default sorting is in ascending manner. What we want is in descending order
selection_indices = np.argsort(selection_metrics)[::-1]
#argsort: ascending order -> adding [::-1], we change it into descending order
if len(task_creatures) > self.capacity:
return task_creatures[selection_indices][:self.capacity], task_creatures[selection_indices][self.capacity]
elif len(task_creatures) == self.capacity:
return task_creatures[selection_indices][:self.capacity-1], task_creatures[selection_indices][self.capacity-1]
else:
return task_creatures[selection_indices][:len(task_creatures)-1], task_creatures[selection_indices][len(task_creatures)-1]
def New_WinningProviderSelection(self, provider_creatures, constant):
#columns=['mu','bid']
#the smaller mu, the better
#the smaller bid, the better
preference_matrix = self.provider_info_matrix(provider_creatures)
max_mu = (max(preference_matrix['mu'])**constant)
#max_ratio = max(preference_matrix['bid'] / preference_matrix['skill'])
max_ratio = max(preference_matrix['bid'])
#selection_metrics = (preference_matrix['mu'] / max_mu)*((preference_matrix['bid'] / preference_matrix['skill']) / max_ratio)
selection_metrics = ((preference_matrix['mu'])**constant / max_mu)*(preference_matrix['bid'] / max_ratio)
selection_indices = np.argsort(selection_metrics)
if len(provider_creatures) > self.capacity:
return provider_creatures[selection_indices[:self.capacity]], provider_creatures[selection_indices[self.capacity]]
elif len(provider_creatures) == self.capacity:
return provider_creatures[ selection_indices[:self.capacity-1] ], provider_creatures[selection_indices[self.capacity-1]]
else:
return provider_creatures[ selection_indices[:len(provider_creatures)-1] ], provider_creatures[selection_indices[len(provider_creatures)-1]]
def Trimming(self, w_requesters, w_providers, req_threshold, pro_threshold):
if len(w_requesters) < len(w_providers):
pro_threshold = w_providers[len(w_requesters)]
return w_requesters, w_providers[:len(w_requesters)], req_threshold, pro_threshold
elif len(w_requesters) > len(w_providers):
req_threshold = w_requesters[len(w_providers)]
return w_requesters[:len(w_providers)], w_providers, req_threshold, pro_threshold
else:
return w_requesters, w_providers, req_threshold, pro_threshold
def WPS_payment(self, winners, threshold):
#threshold_value = threshold.bid_ / threshold.skill_
threshold_value = threshold.bid_
payment = []
for i in range(0, len(winners)):
#payment.append(winners[i].skill_ * threshold_value)
payment.append(threshold_value)
return np.array(payment)
def New_WPS_payment(self, winners, threshold, constant):
#threshold_value = threshold.mean_*(threshold.bid_ / threshold.skill_)
threshold_value = ((threshold.mean_)**constant)*threshold.bid_
payment = []
for i in range(0, len(winners)):
payment.append(threshold_value / (winners[i].mean_)**constant)
return np.array(payment)
def WRS_payment(self, winners, threshold):
threshold_value = threshold.bid_ / threshold.task_size_
payment = []
for i in range(0, len(winners)):
payment.append(winners[i].task_size_*threshold_value)
return np.array(payment)
def New_WRS_payment(self, winners, threshold, constant):
threshold_value = (threshold.bid_/threshold.task_size_)* (1/(threshold.alpha_**constant))
payment = []
for i in range(0, len(winners)):
payment.append((winners[i].task_size_*(winners[i].alpha_**constant))*threshold_value)
return np.array(payment)