[75] | 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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