mesa/my_model.py

import json
from random import shuffle

import networkx as nx
import pandas as pd
from mesa import Model
from mesa.space import MultiGrid, NetworkGrid
from mesa.datacollection import DataCollector
from mesa.time import RandomActivation

from firm import FirmAgent
from product import ProductAgent


class MyModel(Model):
    def __init__(self, params):

        # self.num_agents = N
        self.is_prf_size = params['prf_size']
        self.prf_conn = params['prf_conn']
        self.cap_limit_prob_type = params['cap_limit_prob_type']
        self.cap_limit_level = params['cap_limit_level']
        self.diff_new_conn = params['diff_new_conn']

        self.firm_network = nx.MultiDiGraph()  # 有向多重图
        self.firm_prod_network = nx.MultiDiGraph()
        self.product_network = nx.MultiDiGraph()  # 有向多重图

        # NetworkGrid 用于管理网格
        # NetworkX 图对象
        self.t = 0
        self.network_graph = nx.MultiDiGraph()
        self.grid = NetworkGrid(self.network_graph)

        self.data_collector = DataCollector(
            agent_reporters={"Product": "name"}
        )

        self.company_agents = []
        self.product_agents = []

        # Initialize parameters from `params`
        self.sample = params['sample']
        self.int_stop_ts = 0
        self.int_n_iter = int(params['n_iter'])
        self.dct_lst_init_disrupt_firm_prod = params['dct_lst_init_disrupt_firm_prod']
        # external variable
        self.int_n_max_trial = int(params['n_max_trial'])
        self.is_prf_size = bool(params['prf_size'])

        self.remove_t = int(params['remove_t'])
        self.int_netw_prf_n = int(params['netw_prf_n'])

        self.initialize_product_network(params)
        self.initialize_firm_network()
        self.initialize_firm_product_network()
        self.add_edges_to_firm_network()
        self.connect_unconnected_nodes()
        self.initialize_agents()
        self.initialize_disruptions()

    def initialize_product_network(self, params):
        try:
            self.product_network = nx.adjacency_graph(json.loads(params['g_bom']))
            print(
                f"Product network initialized with {self.product_network.number_of_nodes()} nodes and {self.product_network.number_of_edges()} edges.")
        except Exception as e:
            print(f"Failed to initialize product network: {e}")

    def initialize_firm_network(self):
        """ Initialize the firm network and add it to the model. """
        Firm = pd.read_csv("input_data/Firm_amended.csv")
        Firm['Code'] = Firm['Code'].astype('string')
        Firm.fillna(0, inplace=True)
        Firm_attr = Firm.loc[:, ["Code", "Type_Region", "Revenue_Log"]]
        firm_product = [row[row == 1].index.to_list() for _, row in Firm.loc[:, '1':].iterrows()]
        Firm_attr.loc[:, 'Product_Code'] = firm_product
        Firm_attr.set_index('Code', inplace=True)

        self.firm_network = nx.MultiDiGraph()
        self.firm_network.add_nodes_from(Firm["Code"])

        firm_labels_dict = {code: Firm_attr.loc[code].to_dict() for code in self.firm_network.nodes}
        nx.set_node_attributes(self.firm_network, firm_labels_dict)

    def initialize_firm_product_network(self):
        """ Initialize the firm-product network and add it to the model. """
        Firm_Prod = pd.read_csv("input_data/Firm_amended.csv")
        Firm_Prod.fillna(0, inplace=True)
        firm_prod = pd.DataFrame({'bool': Firm_Prod.loc[:, '1':].stack()})
        firm_prod = firm_prod[firm_prod['bool'] == 1].reset_index()
        firm_prod.drop('bool', axis=1, inplace=True)
        firm_prod.rename({'level_0': 'Firm_Code', 'level_1': 'Product_Code'}, axis=1, inplace=True)
        firm_prod['Firm_Code'] = firm_prod['Firm_Code'].astype('string')

        self.firm_prod_network = nx.MultiDiGraph()
        self.firm_prod_network.add_nodes_from(firm_prod.index)

        firm_prod_labels_dict = {code: firm_prod.loc[code].to_dict() for code in firm_prod.index}
        nx.set_node_attributes(self.firm_prod_network, firm_prod_labels_dict)

        self.add_edges_to_firm_network()
        self.connect_unconnected_nodes()

        print(
            f"Firm network has {self.firm_network.number_of_nodes()} nodes and {self.firm_network.number_of_edges()} edges.")
        print(
            f"Product network has {self.product_network.number_of_nodes()} nodes and {self.product_network.number_of_edges()} edges.")

    def add_edges_to_firm_network(self):
        """ Add edges to the firm network based on product BOM. """
        Firm = pd.read_csv("input_data/Firm_amended.csv")
        Firm['Code'] = Firm['Code'].astype('string')
        Firm.fillna(0, inplace=True)
        for node in nx.nodes(self.firm_network):
            lst_pred_product_code = []
            for product_code in self.firm_network.nodes[node]['Product_Code']:
                lst_pred_product_code += list(self.product_network.predecessors(product_code))
            lst_pred_product_code = list(set(lst_pred_product_code))
            lst_pred_product_code = list(sorted(lst_pred_product_code))

            for pred_product_code in lst_pred_product_code:
                lst_pred_firm = Firm['Code'][Firm[pred_product_code] == 1].to_list()
                n_pred_firm = self.int_netw_prf_n
                if n_pred_firm > len(lst_pred_firm):
                    n_pred_firm = len(lst_pred_firm)
                if self.is_prf_size:
                    lst_pred_firm_size = [self.firm_network.nodes[pred_firm]['Revenue_Log'] for pred_firm in
                                          lst_pred_firm]
                    lst_prob = [size / sum(lst_pred_firm_size) for size in lst_pred_firm_size]
                    lst_choose_firm = self.random.choices(lst_pred_firm, k=n_pred_firm, weights=lst_prob)
                else:
                    lst_choose_firm = self.random.choices(lst_pred_firm, k=n_pred_firm)
                lst_add_edge = [(pred_firm, node, {'Product': pred_product_code}) for pred_firm in lst_choose_firm]
                self.firm_network.add_edges_from(lst_add_edge)

                # Add edges to firm-prod network
                set_node_prod_code = set(self.firm_network.nodes[node]['Product_Code'])
                set_pred_succ_code = set(self.product_network.successors(pred_product_code))
                lst_use_pred_prod_code = list(set_node_prod_code & set_pred_succ_code)

                for pred_firm in lst_choose_firm:
                    pred_node = [n for n, v in self.firm_prod_network.nodes(data=True) if
                                 v['Firm_Code'] == pred_firm and v['Product_Code'] == pred_product_code][0]
                    for use_pred_prod_code in lst_use_pred_prod_code:
                        current_node = [n for n, v in self.firm_prod_network.nodes(data=True) if
                                        v['Firm_Code'] == node and v['Product_Code'] == use_pred_prod_code][0]
                        self.firm_prod_network.add_edge(pred_node, current_node)

    def connect_unconnected_nodes(self):
        """ Connect unconnected nodes in the firm network. """
        Firm = pd.read_csv("input_data/Firm_amended.csv")
        Firm['Code'] = Firm['Code'].astype('string')
        Firm.fillna(0, inplace=True)
        for node in nx.nodes(self.firm_network):
            if self.firm_network.degree(node) == 0:
                for product_code in self.firm_network.nodes[node]['Product_Code']:
                    current_node = [n for n, v in self.firm_prod_network.nodes(data=True) if
                                    v['Firm_Code'] == node and v['Product_Code'] == product_code][0]
                    lst_succ_product_code = list(self.product_network.successors(product_code))
                    for succ_product_code in lst_succ_product_code:
                        lst_succ_firm = Firm['Code'][Firm[succ_product_code] == 1].to_list()
                        n_succ_firm = self.int_netw_prf_n
                        if n_succ_firm > len(lst_succ_firm):
                            n_succ_firm = len(lst_succ_firm)
                        if self.is_prf_size:
                            lst_succ_firm_size = [self.firm_network.nodes[succ_firm]['Revenue_Log'] for succ_firm in
                                                  lst_succ_firm]
                            lst_prob = [size / sum(lst_succ_firm_size) for size in lst_succ_firm_size]
                            lst_choose_firm = self.random.choices(lst_succ_firm, k=n_succ_firm, weights=lst_prob)
                        else:
                            lst_choose_firm = self.random.choices(lst_succ_firm, k=n_succ_firm)
                        lst_add_edge = [(node, succ_firm, {'Product': product_code}) for succ_firm in lst_choose_firm]
                        self.firm_network.add_edges_from(lst_add_edge)

                        for succ_firm in lst_choose_firm:
                            succ_node = [n for n, v in self.firm_prod_network.nodes(data=True) if
                                         v['Firm_Code'] == succ_firm and v['Product_Code'] == succ_product_code][0]
                            self.firm_prod_network.add_edge(current_node, succ_node)

    def initialize_agents(self):
        """ Initialize agents and add them to the model. """
        for ag_node, attr in self.product_network.nodes(data=True):
            product = ProductAgent(ag_node, self, name=attr['Name'])
            self.add_agent(product)
            # self.grid.place_agent(product, ag_node)

            ##print(f"Product agent created: {product.name}, ID: {product.unique_id}")

        for ag_node, attr in self.firm_network.nodes(data=True):
            a_lst_product = [agent for agent in self.product_agents if agent.unique_id in attr['Product_Code']]
            print(a_lst_product)
            firm_agent = FirmAgent(
                ag_node, self,
                type_region=attr['Type_Region'],
                revenue_log=attr['Revenue_Log'],
                a_lst_product=a_lst_product,
            )
            self.add_agent(firm_agent)

            ##print(f"Firm agent created: {firm_agent.unique_id}, Products: {[p.name for p in a_lst_product]}")
            # self.grid.place_agent(firm_agent, ag_node)

    def initialize_disruptions(self):
        # 初始化一部字典，用于存储每个公司及其对应的受干扰产品列表
        t_dct = {}

        # 遍历初始公司-产品干扰数据，将其转化为基于公司和产品的映射
        for firm_code, lst_product in self.dct_lst_init_disrupt_firm_prod.items():
            # 从 company_agents 列表中选择指定公司
            firm = next(firm for firm in self.company_agents if firm.unique_id == firm_code)

            # 从总产品列表中选择该公司受干扰的产品
            disrupted_products = [product for product in self.product_agents if product.unique_id in lst_product]

            # 将公司与其受干扰的产品映射到字典中
            t_dct[firm] = disrupted_products

        # 更新 self.dct_lst_init_disrupt_firm_prod 字典，存储公司及其受干扰的产品
        self.dct_lst_init_disrupt_firm_prod = t_dct

        # 设置初始受干扰的公司产品状态
        for firm, a_lst_product in self.dct_lst_init_disrupt_firm_prod.items():
            for product in a_lst_product:
                # 确保产品存在于公司的生产状态字典中
                assert product in firm.dct_prod_up_prod_stat.keys(), \
                    f"Product {product.code} not in firm {firm.code}"

                # 将产品状态更新为干扰状态，并记录干扰时间
                firm.dct_prod_up_prod_stat[product]['p_stat'].append(('D', self.t))

    def add_agent(self, agent):
        if isinstance(agent, FirmAgent):
            self.company_agents.append(agent)
        elif isinstance(agent, ProductAgent):
            self.product_agents.append(agent)

    def step(self):
        print(f"Running step {self.t}")
        # 1. Remove edge to customer and disrupt customer up product
        for firm in self.company_agents:
            for prod in firm.dct_prod_up_prod_stat.keys():
                status, ts = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1]
                if status == 'D' and ts == self.t - 1:
                    firm.remove_edge_to_cus(prod)

        for firm in self.company_agents:
            for prod in firm.dct_prod_up_prod_stat.keys():
                for up_prod in firm.dct_prod_up_prod_stat[prod]['s_stat'].keys():
                    if firm.dct_prod_up_prod_stat[prod]['s_stat'][up_prod]['set_disrupt_firm']:
                        firm.disrupt_cus_prod(prod, up_prod)

        # 2. Trial Process
        for n_trial in range(self.int_n_max_trial):
            shuffle(self.company_agents)  # 手动打乱代理顺序

            is_stop_trial = True
            for firm in self.company_agents:
                lst_seek_prod = []
                for prod in firm.dct_prod_up_prod_stat.keys():
                    status = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1][0]
                    if status == 'D':
                        for supply in firm.dct_prod_up_prod_stat[prod]['s_stat'].keys():
                            if not firm.dct_prod_up_prod_stat[prod]['s_stat'][supply]['stat']:
                                lst_seek_prod.append(supply)
                lst_seek_prod = list(set(lst_seek_prod))
                if len(lst_seek_prod) > 0:
                    is_stop_trial = False
                for supply in lst_seek_prod:
                    firm.seek_alt_supply(supply)
            if is_stop_trial:
                break

            # Handle requests
            shuffle(self.company_agents)  # 手动打乱代理顺序
            for firm in self.company_agents:
                if len(firm.dct_request_prod_from_firm) > 0:
                    firm.handle_request()

            # Reset dct_request_prod_from_firm
            for firm in self.company_agents:
                firm.clean_before_trial()

        # Increment the time step
        self.t += 1