salary02/env.py

import agentpy as ap
import numpy as np
from random import uniform
import math

from worker import WorkerAgent
from firm import FirmAgent

# plt.ion()


class Env(ap.Model):
    float_market_size: float
    # percent_rh: float
    percent_search: float

    is_RH_ratio: float
    is_FH_ratio: float

    n_worker: int
    n_firm: int
    e_revenue: float

    a_lst_worker: ap.AgentList
    a_lst_firm: ap.AgentList

    """
        Worker: Mean(s), Gini(s)
        Firm: Mean((pi_{j,t})), Gini(pi_{j,t}), Mean(s_a_yield)
        Env: Percent(IsHired)
    """
    out_w_avg_salary: float
    out_w_gini_salary: float
    out_f_avg_profit: float
    out_f_avg_yield: float
    out_f_gini_profit: float
    out_w_percent_hired: float

    def __init__(self, dct_all, _run_id=None):
        super().__init__()

        # 工作人员、企业数量、搜寻企业数量赋值
        self.n_worker = int(dct_all['n_worker'])
        self.n_firm = int(dct_all['n_firm'])
        self.percent_search = float(dct_all['percent_search'])
        self.is_RH_ratio = float(dct_all['is_RH_ratio'])
        self.is_FH_ratio = float(dct_all['is_FH_ratio'])
        # 工人、企业列表
        self.a_lst_worker = ap.AgentList(self)
        self.a_lst_firm = ap.AgentList(self)
        self.e_revenue = 5815100000000

        # 在工人列表中添加工人
        for i in range(self.n_worker):
            # 初始化 worker agent，并把alpha属性传过去
            w = WorkerAgent(self, float(dct_all['alpha']))
            self.a_lst_worker.append(w)

        # 在企业列表中添加企业，放入一个is_RH_ratio, 即有多大比例的企业是属于RH类型的
        for i in range(self.n_firm):
            # 对于企业属性true or false 的判断， 影响到firm 板块下， self.s_IsRH = is_RH 语句的判断
            f = FirmAgent(self, self.is_RH_ratio >= uniform(0, 1), self.is_FH_ratio >= uniform(0, 1))
            self.a_lst_firm.append(f)

        self.running = True
        self.t = 0

    def update_e_revenue(self):
        self.e_revenue += 0.0027 * self.e_revenue

    def step(self):
        self.update_e_revenue()
        # 先清空每次的选择列表
        self.a_lst_firm.empty_apply()
        # 一开始worker要去选择很多firm
        self.a_lst_worker.select_firm()
        # 第二步， firm 去选 worker
        self.a_lst_firm.select_worker()

        self.a_lst_firm.update_yields()

        self.provide_logit_share()

        self.a_lst_firm.update_s_profit()

        self.create_and_destroy_bankrupt_firms()

        self.a_lst_worker.update_wd_by_is_hired()

        # self.picture_out()
        self.update()

        if self.t >= 200:
            self.running = False
            self.stop()
        else:
            self.t += 1
            # print(f"running the {self.t} step")

    def update(self):
        lst_salary = []
        n_hired = 0
        for w in self.a_lst_worker:
            lst_salary.append(w.s_salary)
            if w.s_is_hired:
                n_hired += 1
        n_workers = len(lst_salary)
        self.out_w_avg_salary = sum(lst_salary) / n_workers
        self.out_w_gini_salary = self.gini(lst_salary)

        lst_profit = []
        lst_yield = []
        # n_w_firm = 0
        for f in self.a_lst_firm:
            lst_profit.append(f.s_profit)
            lst_yield.append(f.s_a_yield)
            # if f.s_profit > 0:
            #     n_w_firm += 1
        n_firms = len(lst_profit)
        self.out_f_avg_profit = sum(lst_profit) / n_firms
        self.out_f_avg_yield = sum(lst_yield) / n_firms
        self.out_f_gini_profit = self.gini(lst_profit)
        self.out_w_percent_hired = n_hired / n_workers

        self.record('out_w_avg_salary')
        self.record('out_w_gini_salary')
        self.record('out_f_avg_profit')
        self.record('out_f_avg_yield')
        self.record('out_f_gini_profit')
        self.record('out_w_percent_hired')

    def create_and_destroy_bankrupt_firms(self):
        for f in self.a_lst_firm:
            if f.s_value < 0:
                # 直接淘汰企业，并新增一个企业
                for worker in f.l_senior_workers:
                    worker.s_is_hired = False
                    worker.working_firm = None
                for worker in f.l_junior_workers:
                    worker.s_is_hired = False
                    worker.working_firm = None
                self.a_lst_firm.remove(f)
                del f
                new_f = FirmAgent(self, self.is_RH_ratio >= uniform(0, 1), self.is_FH_ratio >= uniform(0, 1))
                self.a_lst_firm.append(new_f)
            else:
                if f.s_profit < 0:
                    f.l_all_w = f.l_junior_workers + f.l_senior_workers
                    min_unit_yield_salary, worst_worker = float('inf'), None
                    for worker in f.l_all_w:
                        unit_yield_salary = worker.s_yield if f.s_IsFH else worker.s_yield * 10000 / worker.s_salary
                        if unit_yield_salary < min_unit_yield_salary:
                            min_unit_yield_salary = unit_yield_salary
                            worst_worker = worker
                    worst_worker.s_is_hired = False
                    worst_worker.working_firm = None

        assert len(self.a_lst_firm) == self.n_firm, \
            f'current num firm {len(self.a_lst_firm)} != expected num firm {self.n_firm}'

    def provide_lst_random_firms(self, the_worker: WorkerAgent):
        """
        选择企业数量 = 企业总数*百分比
        选择企业的列表 = 随机选择的企业的个数
        如果员工处于被雇佣的状态：
           如果员工工作的企业在随机选定的企业列表中
             打开列表中的企业
             移除该企业
             返回值：移除后，再重新选择随机选择企业
           否则：
            返回值：选择企业列表
        """
        n_select_firms = int(self.percent_search * self.n_firm)
        a_lst_select_firms = self.a_lst_firm.random(n_select_firms)
        if the_worker.s_is_hired:
            if the_worker.working_firm in a_lst_select_firms:
                # 转换为 list
                lst_f = list(self.a_lst_firm)
                lst_f.remove(the_worker.working_firm)
                return ap.AgentList(self, lst_f).random(n_select_firms)
        # 假如以上都不满足， 直接返回
        return ap.AgentList(self, a_lst_select_firms)

    def provide_logit_share(self):
        fen_mu = 0
        for f in self.a_lst_firm:
            fen_mu += math.exp(f.s_a_yield)
        for f in self.a_lst_firm:
            f.s_revenue = self.e_revenue * math.exp(f.s_a_yield) / fen_mu

    # def picture_out(self, the_worker: WorkerAgent):
    #     a = self.t
    #     b = len(the_worker.s_salary)
    #     plt.plot(a, b, 'ro')
    #     return plt.show()

    @staticmethod
    def gini(x):
        x = [ele for ele in x if ele > 0]
        if len(x) == 0:
            return 0
        if sum(x) == 0:
            return 0
        x = np.array(x)
        mad = np.abs(np.subtract.outer(x, x)).mean()  # Mean absolute difference
        r_mad = mad / np.mean(x)  # Relative mean absolute difference
        return 0.5 * r_mad


if __name__ == '__main__':
    # dict_para = {'n_worker': 1000,
    #              'n_firm': 100,
    #              'percent_search': 0.2,
    #              'alpha': 0.5,
    #              'is_RH_ratio': 0.5}
    # my_model = Env(dict_para)
    #
    # print(my_model.gini([10000, 0, 0, 0, 0, 0, 0]))
    # my_model.run()
    # # a = range(0, 101)
    # # b = len(WorkerAgent.s_yield)
    # # plt.plot(a,b)
    # # plt.show()
    # plt.show()

    parameters = {
        'n_worker': 1000,
        'n_firm': 100,
        'alpha': 0.5,
        'percent_search': 0.2,
        'is_RH_ratio': 0.5,
        'is_FH_ratio': 0.5,
     }
    sample = ap.Sample(parameters)
    # sample = ap.Sample(parameters, n=3)
    #
    exp = ap.Experiment(Env, sample, iterations=10, record=True)
    results = exp.run()
    results['variables']['Env'].to_excel('env_data.xlsx', engine='openpyxl')