1 | import json
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2 | import random
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3 | import pandas as pd
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4 | import lightgbm as lgb
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5 |
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6 | from geniusweb.bidspace.AllBidsList import AllBidsList
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7 | from geniusweb.issuevalue.Bid import Bid
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8 |
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9 |
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10 | class Pinar_Agent_Brain:
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11 | def __init__(self):
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12 |
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13 | self.acceptance_condition = 0
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14 | self.my_offered_number_of_time_from_ai = 0
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15 | self.sorted_bids_agent_that_greater_than_065_df = pd.DataFrame()
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16 | self.sorted_bids_agent_that_greater_than_065 = []
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17 |
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18 | self.reservationBid_utility = float(0)
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19 | self.eva_util_val_acc_to_lgb_m_with_max_bids_for_agent = []
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20 | self.sorted_bids_agent_df = None
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21 | self.reservationBid: Bid = None
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22 | self.sorted_bids_agent = None
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23 | self.sorted_bids_agent_that_greater_than_goal_of_utility = []
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24 | self.all_bid_list = None
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25 |
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26 | self.param = None
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27 |
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28 | self.lgb_model = None
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29 |
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30 | self.X = pd.DataFrame()
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31 | self.Y = pd.DataFrame()
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32 |
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33 | self.domain = None
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34 | self.profile = None
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35 | self.issue_name_list = None
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36 | self.temEnumDict = None
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37 |
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38 | self.offers = []
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39 | self.offers_unique = []
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40 | self.offers_unique_sorted = None
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41 |
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42 | self.number_of_bid_greater_than95 = 0
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43 | self.percentage_of_greater_than95 = 0
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44 |
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45 | self.number_of_bid_greater_than85 = 0
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46 | self.percentage_of_greater_than85 = 0
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47 |
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48 | self.goal_of_utility = 0.80
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49 | self.number_of_goal_of_utility = None
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50 |
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51 | @staticmethod
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52 | def get_goal_of_negoation_utility(x):
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53 | if 0 <= x <= 0.05:
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54 | a = float(-57.57067183) * float(x) * float(x)
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55 | b = float(x) * float(7.50261378)
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56 | c = float(1.59499339)
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57 | d = a + b + c
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58 | return float(d / 2)
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59 | elif x > 0.05:
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60 | return float(0.94)
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61 | return float(0.80)
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62 |
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63 | def keep_opponent_offer_in_a_list(self, bid: Bid, progress_time: float):
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64 | # keep track of all bids received
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65 | self.offers.append(bid)
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66 |
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67 | if bid not in self.offers_unique:
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68 | self.offers_unique.append(bid)
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69 | if progress_time >= 0.9:
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70 | self.offers_unique_sorted = sorted(self.offers_unique, key=lambda x: self.profile.getUtility(x),
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71 | reverse=True)
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72 |
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73 | def add_opponent_offer_to_self_x_and_self_y(self, bid, progress_time):
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74 | bid_value_array = self.get_bid_value_array_for_data_frame_usage(bid)
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75 | df = pd.DataFrame(bid_value_array)
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76 | df = self.enumerate(df)
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77 | self.X = pd.concat([self.X, df])
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78 | if progress_time < 0.81:
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79 | val = (float(0.99) - (float(0.14) * (float(progress_time))))
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80 | """Y tarafına öyle bir değişken atamalıyım ki adamın utilitisi olmalı (kendi utilitime göre olsa daha mantıklı olabilir gibi şimdilik)"""
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81 | new = pd.DataFrame([val])
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82 | self.Y = pd.concat([self.Y, new])
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83 |
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84 | def fill_domain_and_profile(self, domain, profile):
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85 | self.domain = domain
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86 | self.profile = profile
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87 | self.reservationBid = self.profile.getReservationBid()
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88 | if self.reservationBid is not None:
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89 | self.reservationBid_utility = self.profile.getUtility(self.reservationBid)
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90 | self.issue_name_list = self.domain.getIssues()
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91 | self.X = pd.DataFrame()
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92 | self.Y = pd.DataFrame()
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93 | self.temEnumDict = self.enumerate_enum_dict()
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94 | self.all_bid_list = AllBidsList(domain)
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95 |
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96 | self.sorted_bids_agent = sorted(self.all_bid_list,
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97 | key=lambda x: self.profile.getUtility(x),
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98 | reverse=True)
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99 | self.calculate_percantage_and_number()
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100 | self.add_agent_first_n_bid_to_machine_learning_with_low_utility(self.sorted_bids_agent)
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101 |
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102 | def calculate_percantage_and_number(self):
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103 | numb_95 = 0
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104 | numb_85 = 0
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105 | for i in self.sorted_bids_agent:
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106 | utility = float(self.profile.getUtility(i))
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107 | if utility > float(0.95):
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108 | numb_95 = numb_95 + 1
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109 | if utility > float(0.85):
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110 | numb_85 = numb_85 + 1
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111 | else:
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112 | break
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113 | self.number_of_bid_greater_than95 = numb_95
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114 | self.number_of_bid_greater_than85 = numb_85
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115 |
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116 | self.percentage_of_greater_than95 = float(self.number_of_bid_greater_than95) / float(
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117 | len(self.sorted_bids_agent))
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118 | self.percentage_of_greater_than85 = float(self.number_of_bid_greater_than85) / float(
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119 | len(self.sorted_bids_agent))
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120 |
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121 | self.goal_of_utility = self.get_goal_of_negoation_utility(float(self.percentage_of_greater_than85)) + float(
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122 | 0.01)
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123 | numb_goal_util = 0
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124 | self.sorted_bids_agent_df = pd.DataFrame()
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125 | self.sorted_bids_agent_that_greater_than_065_df = pd.DataFrame()
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126 | for i in self.sorted_bids_agent:
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127 | utility = float(self.profile.getUtility(i))
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128 | if utility > float(self.goal_of_utility):
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129 | numb_goal_util = numb_goal_util + 1
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130 | if utility > (float(self.goal_of_utility) - float(0.1)):
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131 | self.sorted_bids_agent_that_greater_than_goal_of_utility.append(i)
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132 | df_temp = pd.DataFrame(self.get_bid_value_array_for_data_frame_usage(i))
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133 | df_temp = self.enumerate(df_temp)
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134 | self.sorted_bids_agent_df = pd.concat([self.sorted_bids_agent_df, df_temp])
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135 | if utility > 0.65:
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136 | self.sorted_bids_agent_that_greater_than_065.append(i)
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137 | df_temp = pd.DataFrame(self.get_bid_value_array_for_data_frame_usage(i))
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138 | df_temp = self.enumerate(df_temp)
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139 | self.sorted_bids_agent_that_greater_than_065_df = pd.concat(
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140 | [self.sorted_bids_agent_that_greater_than_065_df, df_temp])
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141 | else:
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142 | break
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143 | self.number_of_goal_of_utility = numb_goal_util
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144 |
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145 | def evaluate_opponent_utility_for_all_my_important_bid(self, progress_time):
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146 | self.eva_util_val_acc_to_lgb_m_with_max_bids_for_agent = []
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147 | self.my_offered_number_of_time_from_ai = 0
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148 | util_of_opponent = self.lgb_model.predict(self.sorted_bids_agent_that_greater_than_065_df)
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149 |
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150 | for index, i in enumerate(self.sorted_bids_agent_that_greater_than_065):
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151 | util = float(self.profile.getUtility(i))
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152 | if float(self.reservationBid_utility) <= util \
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153 | and (((float(0.93) - (
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154 | (float(0.95) - (self.goal_of_utility - float(0.18))) * float(progress_time))) < util)
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155 | and float(0.40) < util_of_opponent[index] < util - float(0.10)):
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156 | self.eva_util_val_acc_to_lgb_m_with_max_bids_for_agent.append(i)
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157 |
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158 | def evaluate_data_according_to_lig_gbm(self, progress_time):
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159 | length = len(self.offers_unique)
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160 | if length >= 1 and (length % 2) == 0:
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161 | self.train_machine_learning_model()
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162 | self.evaluate_opponent_utility_for_all_my_important_bid(progress_time)
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163 |
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164 | def train_machine_learning_model(self):
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165 | issue_list = []
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166 | for issue in self.domain.getIssues():
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167 | issue_list.append(issue)
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168 | for col in issue_list:
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169 | self.X[col] = self.X[col].astype('int')
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170 | self.Y = self.Y.astype('float')
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171 | train_data = lgb.Dataset(self.X, label=self.Y, feature_name=issue_list)
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172 | if self.param is None:
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173 | self.param = {
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174 | 'objective': 'cross_entropy',
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175 | 'learning_rate': 0.01,
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176 | 'force_row_wise': True,
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177 | 'feature_fraction': 1,
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178 | 'max_depth': 3,
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179 | 'num_leaves': 4,
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180 | 'boosting': 'gbdt',
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181 | 'min_data': 1,
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182 | 'verbose': -1
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183 | }
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184 | self.lgb_model = lgb.train(self.param, train_data)
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185 |
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186 | def call_model_lgb(self, bid):
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187 | if self.lgb_model:
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188 | prediction = self.lgb_model.predict(self._bid_for_model_prediction_to_df(bid))
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189 | return float(prediction[0])
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190 | else:
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191 | return float(1)
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192 |
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193 | def get_bid_value_array_for_data_frame_usage(self, bid):
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194 | bid_value_array = {}
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195 | for issue in self.issue_name_list:
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196 | bid_value_array[issue] = [bid.getValue(issue)]
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197 | return bid_value_array
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198 |
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199 | def _bid_for_model_prediction_to_df(self, bid):
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200 | df_temp = pd.DataFrame(self.get_bid_value_array_for_data_frame_usage(bid))
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201 | df_temp = self.enumerate(df_temp)
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202 | return df_temp
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203 |
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204 | def enumerate_enum_dict(self):
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205 | issue_enums_dict = {}
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206 | for issue in self.domain.getIssues():
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207 | temp_enums = dict((y, x) for x, y in enumerate(set(self.domain.getIssuesValues()[issue])))
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208 | issue_enums_dict[issue] = temp_enums
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209 | return issue_enums_dict
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210 |
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211 | def enumerate(self, df):
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212 | for issue in self.domain.getIssues():
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213 | df[issue] = df[issue].map(self.temEnumDict[issue])
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214 | return df
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215 |
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216 | def model_feature_importance(self):
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217 | if self.lgb_model is not None:
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218 | df = pd.DataFrame({'Value': self.lgb_model.feature_importance(), 'Feature': self.X.columns})
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219 | result = df.to_json(orient="split")
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220 | parsed = json.loads(result)
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221 | return parsed
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222 | return ""
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223 |
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224 | def util_add_agent_first_n_bid_to_machine_learning_with_low_utility(self, bid, ratio):
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225 | bid_value_array = self.get_bid_value_array_for_data_frame_usage(bid)
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226 | df = pd.DataFrame(bid_value_array)
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227 | df = self.enumerate(df)
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228 | self.X = pd.concat([self.X, df])
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229 | util = float(float(0.2) + (float(ratio) * float(0.35)))
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230 | new = pd.DataFrame([util])
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231 |
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232 | self.Y = pd.concat([self.Y, new])
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233 |
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234 | def add_agent_first_n_bid_to_machine_learning_with_low_utility(self, sorted_bids_agent):
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235 |
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236 | if self.number_of_goal_of_utility > 150:
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237 | bid_number = 40
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238 | elif self.number_of_goal_of_utility > 100:
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239 | bid_number = int(float(self.number_of_goal_of_utility) / float(3.4))
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240 | elif self.number_of_goal_of_utility > 80:
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241 | bid_number = int(float(self.number_of_goal_of_utility) / float(3.1))
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242 | elif self.number_of_goal_of_utility > 50:
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243 | bid_number = int(float(self.number_of_goal_of_utility) / float(3))
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244 | elif self.number_of_goal_of_utility > 30:
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245 | bid_number = 9
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246 | elif self.number_of_goal_of_utility > 18:
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247 | bid_number = 7
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248 | elif 16 > self.number_of_goal_of_utility > 8:
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249 | bid_number = int(float(self.number_of_goal_of_utility) / float(2))
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250 | else:
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251 | bid_number = 4
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252 | for i in range(0, bid_number + 1):
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253 | bid = sorted_bids_agent[i]
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254 | self.util_add_agent_first_n_bid_to_machine_learning_with_low_utility(bid, float(float(i) / float(bid_number)))
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255 |
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256 | def is_acceptable(self, bid: Bid, progress):
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257 | util = float(self.profile.getUtility(bid))
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258 | if util >= float(self.reservationBid_utility):
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259 | if util >= 0.94:
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260 | self.acceptance_condition = 1
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261 | return True
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262 | elif util >= 0.91 and 0.76 > float(self.call_model_lgb(bid)) > 0.6:
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263 | self.acceptance_condition = 2
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264 | return True
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265 | elif float(0.85) >= float(progress) > 0.82 and util > self.goal_of_utility - float(0.1) and util - float(0.28) > float(self.call_model_lgb(bid)):
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266 | self.acceptance_condition = 3
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267 | return True
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268 | elif float(0.94) >= float(progress) > 0.85 and util > self.goal_of_utility - float(0.14) and util - float(0.23) > float(self.call_model_lgb(bid)):
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269 | self.acceptance_condition = 4
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270 | return True
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271 | elif float(1.0) >= float(progress) > 0.93 and util > self.goal_of_utility - float(0.2) and util - float(0.18) > float(self.call_model_lgb(bid)):
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272 | self.acceptance_condition = 5
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273 | return True
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274 | elif float(1.0) >= float(progress) > 0.97 and util - float(0.12) > float(self.call_model_lgb(bid)):
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275 | self.acceptance_condition = 6
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276 | return True
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277 | return False
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278 |
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279 | def find_bid(self, progress_time):
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280 | progress_time = float(progress_time)
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281 | if float(self.my_offered_number_of_time_from_ai) < float(len(self.eva_util_val_acc_to_lgb_m_with_max_bids_for_agent)) * float(2) \
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282 | and ((0 < progress_time < 0.17) or (0.23 < progress_time < 0.37) or (0.45 < progress_time < 0.93) or (
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283 | 0.97 < progress_time <= 0.985)) and self.lgb_model is not None \
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284 | and len(self.eva_util_val_acc_to_lgb_m_with_max_bids_for_agent) >= 1:
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285 | index = random.randint(0,
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286 | len(self.eva_util_val_acc_to_lgb_m_with_max_bids_for_agent) - 1)
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287 | if float(self.reservationBid_utility) < float(self.profile.getUtility(self.eva_util_val_acc_to_lgb_m_with_max_bids_for_agent[index])):
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288 | self.my_offered_number_of_time_from_ai = self.my_offered_number_of_time_from_ai + 1
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289 | return self.eva_util_val_acc_to_lgb_m_with_max_bids_for_agent[index]
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290 | elif ((0.25 < progress_time < 0.30) or (0.58 < progress_time < 0.64) or (0.82 < progress_time < 0.86) or (
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291 | 0.965 < progress_time <= 0.995)) and self.lgb_model is not None and len(
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292 | self.eva_util_val_acc_to_lgb_m_with_max_bids_for_agent) >= 1:
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293 | index = random.randint(0, len(self.eva_util_val_acc_to_lgb_m_with_max_bids_for_agent) - 1)
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294 | if float(self.reservationBid_utility) < float(
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295 | self.profile.getUtility(self.eva_util_val_acc_to_lgb_m_with_max_bids_for_agent[index])):
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296 | return self.eva_util_val_acc_to_lgb_m_with_max_bids_for_agent[index]
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297 | elif progress_time < 0.4:
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298 | if self.number_of_bid_greater_than95 >= 8:
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299 | index = random.randint(self.number_of_bid_greater_than95 - 4, self.number_of_bid_greater_than95)
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300 | if float(self.reservationBid_utility) < float(self.profile.getUtility(self.sorted_bids_agent[index])):
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301 | return self.sorted_bids_agent[index]
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302 |
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303 | elif self.number_of_bid_greater_than95 >= 4:
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304 | index = random.randint(3, self.number_of_bid_greater_than95)
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305 | if float(self.reservationBid_utility) < float(self.profile.getUtility(self.sorted_bids_agent[index])):
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306 | return self.sorted_bids_agent[index]
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307 |
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308 | elif self.number_of_bid_greater_than95 >= 1:
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309 | index = random.randint(1, self.number_of_bid_greater_than95)
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310 | if float(self.reservationBid_utility) < float(self.profile.getUtility(self.sorted_bids_agent[index])):
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311 | return self.sorted_bids_agent[index]
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312 |
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313 | elif self.number_of_bid_greater_than85 >= 1:
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314 | index = random.randint(1, self.number_of_bid_greater_than85)
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315 | if float(self.reservationBid_utility) < float(self.profile.getUtility(self.sorted_bids_agent[index])):
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316 | return self.sorted_bids_agent[index]
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317 |
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318 | elif progress_time < 0.85:
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319 | if self.number_of_bid_greater_than95 > 1 and self.number_of_bid_greater_than85 > 2:
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320 | index = random.randint(self.number_of_bid_greater_than95, self.number_of_bid_greater_than85)
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321 | if float(self.reservationBid_utility) < float(self.profile.getUtility(self.sorted_bids_agent[index])):
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322 | return self.sorted_bids_agent[index]
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323 |
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324 | elif self.number_of_bid_greater_than85 >= 1:
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325 | index = random.randint(1, self.number_of_bid_greater_than85)
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326 | if float(self.reservationBid_utility) < float(self.profile.getUtility(self.sorted_bids_agent[index])):
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327 | return self.sorted_bids_agent[index]
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328 |
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329 | elif progress_time <= 0.975:
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330 | if self.number_of_goal_of_utility > self.number_of_bid_greater_than85:
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331 | index = random.randint(self.number_of_bid_greater_than85, self.number_of_goal_of_utility)
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332 | if float(self.reservationBid_utility) < float(self.profile.getUtility(self.sorted_bids_agent[index])):
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333 | return self.sorted_bids_agent[index]
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334 | elif self.number_of_goal_of_utility > self.number_of_bid_greater_than95:
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335 | index = random.randint(self.number_of_bid_greater_than95, self.number_of_goal_of_utility)
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336 | if float(self.reservationBid_utility) < float(self.profile.getUtility(self.sorted_bids_agent[index])):
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337 | return self.sorted_bids_agent[index]
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338 | elif self.number_of_goal_of_utility > 1:
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339 | index = random.randint(1, self.number_of_goal_of_utility)
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340 | if float(self.reservationBid_utility) < float(self.profile.getUtility(self.sorted_bids_agent[index])):
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341 | return self.sorted_bids_agent[index]
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342 | elif 0.91 <= progress_time <= 0.995:
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343 | if self.offers_unique_sorted is not None and not len(self.offers_unique_sorted) == 0:
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344 | bid = self.offers_unique_sorted[0]
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345 | util_of_bid = float(self.profile.getUtility(bid))
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346 | if float(self.reservationBid_utility) < float(util_of_bid) and float(util_of_bid) >= float(self.goal_of_utility) - float(0.03) and float(
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347 | self.call_model_lgb(bid)) < util_of_bid:
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348 | return bid
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349 | elif float(self.reservationBid_utility) < float(self.profile.getUtility(self.sorted_bids_agent[3])):
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350 | return self.sorted_bids_agent[3]
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351 | return self.sorted_bids_agent[0]
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