IIabm/model.py

import agentpy as ap
import pandas as pd
import numpy as np
import networkx as nx
from firm import FirmAgent

sample = 0
seed = 0
n_iter = 3
dct_list_init_remove_firm_prod = {0: ['1.4.4'], 2: ['1.1.3']}
dct_sample_para = {
    'sample': sample,
    'seed': seed,
    'n_iter': n_iter,
    'dct_list_init_remove_firm_prod': dct_list_init_remove_firm_prod
}


class Model(ap.Model):
    def setup(self):
        self.sample = self.p.sample
        self.nprandom = np.random.default_rng(self.p.seed)
        self.dct_list_remove_firm_prod = self.p.dct_list_init_remove_firm_prod
        self.int_n_iter = int(self.p.n_iter)

        # init graph bom
        BomNodes = pd.read_csv('BomNodes.csv', index_col=0)
        BomNodes.set_index('Code', inplace=True)
        BomCateNet = pd.read_csv('BomCateNet.csv', index_col=0)
        BomCateNet.fillna(0, inplace=True)

        G_bom = nx.from_pandas_adjacency(BomCateNet.T,
                                         create_using=nx.MultiDiGraph())

        bom_labels_dict = {}
        for code in G_bom.nodes:
            bom_labels_dict[code] = BomNodes.loc[code].to_dict()
        nx.set_node_attributes(G_bom, bom_labels_dict)

        # init graph firm
        Firm = pd.read_csv("Firm_amended.csv")
        Firm.fillna(0, inplace=True)
        Firm_attr = Firm.loc[:, ["Code", "Name", "Type_Region", "Revenue_Log"]]
        firm_product = []
        for _, row in Firm.loc[:, '1':].iterrows():
            firm_product.append(row[row == 1].index.to_list())
        Firm_attr.loc[:, 'Product_Code'] = firm_product
        Firm_attr.set_index('Code')
        G_Firm = nx.MultiDiGraph()
        G_Firm.add_nodes_from(Firm["Code"])

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

        # add edge to G_firm according to G_bom
        for node in nx.nodes(G_Firm):
            # print(node, '-' * 20)
            for product_code in G_Firm.nodes[node]['Product_Code']:
                # print(product_code)
                for succ_product_code in list(G_bom.successors(product_code)):
                    # print(succ_product_code)
                    list_succ_firms = Firm.index[Firm[succ_product_code] ==
                                                 1].to_list()
                    list_revenue_log = [
                        G_Firm.nodes[succ_firm]['Revenue_Log']
                        for succ_firm in list_succ_firms
                    ]
                    list_prob = [
                        (v - min(list_revenue_log) + 1) /
                        (max(list_revenue_log) - min(list_revenue_log) + 1)
                        for v in list_revenue_log
                    ]
                    list_flag = [
                        self.nprandom.choice([1, 0], p=[prob, 1 - prob])
                        for prob in list_prob
                    ]
                    # print(list(zip(list_succ_firms,list_flag,list_prob)))
                    list_added_edges = [(node, succ_firm, {
                        'Product': product_code
                    }) for succ_firm, flag in zip(list_succ_firms, list_flag)
                                        if flag == 1]
                    G_Firm.add_edges_from(list_added_edges)
                    # print('-' * 20)

        self.firm_network = ap.Network(self, G_Firm)
        # print([node.label for node in self.firm_network.nodes])
        # print([list(self.firm_network.graph.predecessors(node))
        #        for node in self.firm_network.nodes])
        # print([self.firm_network.graph.nodes[node.label]['Name']
        #        for node in self.firm_network.nodes])
        # print([v for v in self.firm_network.graph.nodes(data=True)])

        # init firm
        for ag_node, attr in self.firm_network.graph.nodes(data=True):
            firm_agent = FirmAgent(
                self,
                code=attr['Code'],
                name=attr['Name'],
                type_region=attr['Type_Region'],
                revenue_log=attr['Revenue_Log'],
                list_product=attr['Product_Code'],
                # init capacity as the degree of out edges
                capacity=self.firm_network.graph.out_degree(ag_node))
            self.firm_network.add_agents([firm_agent], [ag_node])
        self.a_list_total_firms = ap.AgentList(self, self.firm_network.agents)
        # print(list(zip(self.a_list_total_firms.code,
        #                self.a_list_total_firms.name,
        #                self.a_list_total_firms.capacity)))

        # set the initial firm product that are removed
        for firm_code, list_product in self.dct_list_remove_firm_prod.items():
            firm = self.a_list_total_firms.select(
                self.a_list_total_firms.code == firm_code)[0]
            for product in list_product:
                assert product in firm.list_product, \
                    f"product {product} not in firm {firm_code}"
                firm.dct_product_is_removed[product] = True

    def update(self):
        # Update the firm that is removed
        self.dct_list_remove_firm_prod = {}
        for firm in self.a_list_total_firms:
            for product, flag in firm.dct_product_is_removed.items():
                if flag is True:
                    if firm.code in self.dct_list_remove_firm_prod.keys():
                        self.dct_list_remove_firm_prod[firm.code].append(
                            product)
                    else:
                        self.dct_list_remove_firm_prod[firm.code] = [product]
        # print(self.dct_list_remove_firm_prod)

        # Stop simulation if reached terminal number of iteration
        if self.t == self.int_n_iter or len(
                self.dct_list_remove_firm_prod) == 0:
            self.stop()

    def step(self):
        # shuffle self.dct_list_remove_firm_prod
        dct_key_list = list(self.dct_list_remove_firm_prod.keys())
        self.nprandom.shuffle(dct_key_list)
        self.dct_list_remove_firm_prod = {
            key: self.dct_list_remove_firm_prod[key]
            for key in dct_key_list
        }
        for firm_code, list_product in self.dct_list_remove_firm_prod.items():
            firm = self.a_list_total_firms.select(
                self.a_list_total_firms.code == firm_code)[0]
            for product in list_product:
                firm.remove_edge_to_customer_if_removed(product)

    def end(self):
        pass

    def draw_network(self):
        import matplotlib.pyplot as plt
        plt.rcParams['font.sans-serif'] = 'SimHei'
        pos = nx.nx_agraph.graphviz_layout(self.firm_network.graph,
                                           prog="twopi",
                                           args="")
        node_label = nx.get_node_attributes(self.firm_network.graph, 'Name')
        # print(node_label)
        node_degree = dict(self.firm_network.graph.out_degree())
        node_label = {
            key: f"{node_label[key]} {node_degree[key]}"
            for key in node_label.keys()
        }
        node_size = list(
            nx.get_node_attributes(self.firm_network.graph,
                                   'Revenue_Log').values())
        node_size = list(map(lambda x: x**2, node_size))
        edge_label = nx.get_edge_attributes(self.firm_network.graph, "Product")
        # multi(di)graphs, the keys are 3-tuples
        edge_label = {(n1, n2): label
                      for (n1, n2, _), label in edge_label.items()}
        plt.figure(figsize=(12, 12), dpi=300)
        nx.draw(self.firm_network.graph,
                pos,
                node_size=node_size,
                labels=node_label,
                font_size=6)
        nx.draw_networkx_edge_labels(self.firm_network.graph,
                                     pos,
                                     edge_label,
                                     font_size=4)
        plt.savefig("network.png")


model = Model(dct_sample_para)
model.setup()
model.update()
model.step()
# model.draw_network()