import agentpy as ap import pandas as pd import networkx as nx from firm import FirmAgent from product import ProductAgent from orm import db_session, Result import platform import json class Model(ap.Model): def setup(self): # self parameter self.sample = self.p.sample self.int_stop_ts = 0 self.int_n_iter = int(self.p.n_iter) self.product_network = None # agentpy network self.firm_network = None # agentpy network self.firm_prod_network = None # networkx self.dct_lst_init_disrupt_firm_prod = \ self.p.dct_lst_init_disrupt_firm_prod # external variable self.int_n_max_trial = int(self.p.n_max_trial) self.is_prf_size = bool(self.p.prf_size) # self.proactive_ratio = float(self.p.proactive_ratio) # dropped self.remove_t = int(self.p.remove_t) self.int_netw_prf_n = int(self.p.netw_prf_n) # initialize graph bom G_bom = nx.adjacency_graph(json.loads(self.p.g_bom)) self.product_network = ap.Network(self, G_bom) # initialize graph firm 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 = [] 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', inplace=True) 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) # initialize graph firm prod 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') G_FirmProd = nx.MultiDiGraph() G_FirmProd.add_nodes_from(firm_prod.index) firm_prod_labels_dict = {} for code in firm_prod.index: firm_prod_labels_dict[code] = firm_prod.loc[code].to_dict() nx.set_node_attributes(G_FirmProd, firm_prod_labels_dict) # add edge to G_firm according to G_bom for node in nx.nodes(G_Firm): lst_pred_product_code = [] for product_code in G_Firm.nodes[node]['Product_Code']: lst_pred_product_code += list(G_bom.predecessors(product_code)) lst_pred_product_code = list(set(lst_pred_product_code)) # to generate consistant graph lst_pred_product_code = list(sorted(lst_pred_product_code)) for pred_product_code in lst_pred_product_code: # for each product predecessor (component) the firm need # get a list of firm producing this component lst_pred_firm = \ Firm['Code'][Firm[pred_product_code] == 1].to_list() # select multiple supplier (multi-sourcing) n_pred_firm = self.int_netw_prf_n if n_pred_firm > len(lst_pred_firm): n_pred_firm = len(lst_pred_firm) # based on size or not if self.is_prf_size: lst_pred_firm_size = \ [G_Firm.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.nprandom.choice(lst_pred_firm, n_pred_firm, replace=False, p=lst_prob) else: lst_choose_firm = self.nprandom.choice(lst_pred_firm, n_pred_firm, replace=False) lst_add_edge = [(pred_firm, node, {'Product': pred_product_code}) for pred_firm in lst_choose_firm] G_Firm.add_edges_from(lst_add_edge) # graph firm prod set_node_prod_code = set(G_Firm.nodes[node]['Product_Code']) set_pred_succ_code = set(G_bom.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 G_FirmProd.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 G_FirmProd.nodes(data=True) if v['Firm_Code'] == node and v['Product_Code'] == use_pred_prod_code][0] G_FirmProd.add_edge(pred_node, current_node) # nx.to_pandas_adjacency(G_Firm).to_csv('adj_g_firm.csv') # nx.to_pandas_adjacency(G_FirmProd).to_csv('adj_g_firm_prod.csv') # connect unconnected nodes for node in nx.nodes(G_Firm): if G_Firm.degree(node) == 0: for product_code in G_Firm.nodes[node]['Product_Code']: # unconnected node does not have possible suppliers, # therefore find possible customer instead # current node in graph firm prod current_node = \ [n for n, v in G_FirmProd.nodes(data=True) if v['Firm_Code'] == node and v['Product_Code'] == product_code][0] lst_succ_product_code = list( G_bom.successors(product_code)) # different from: for different types of product, # finding a common supplier (the logic above), # instead: for different types of product, # finding a customer for each product for succ_product_code in lst_succ_product_code: # for each product successor (finished product) # the firm sells to, # get a list of firm producing this finished product lst_succ_firm = Firm['Code'][ Firm[succ_product_code] == 1].to_list() # select multiple customer (multi-selling) n_succ_firm = self.int_netw_prf_n if n_succ_firm > len(lst_succ_firm): n_succ_firm = len(lst_succ_firm) # based on size or not if self.is_prf_size: lst_succ_firm_size = \ [G_Firm.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.nprandom.choice(lst_succ_firm, n_succ_firm, replace=False, p=lst_prob) else: lst_choose_firm = \ self.nprandom.choice(lst_succ_firm, n_succ_firm, replace=False) lst_add_edge = [(node, succ_firm, {'Product': product_code}) for succ_firm in lst_choose_firm] G_Firm.add_edges_from(lst_add_edge) # graph firm prod for succ_firm in lst_choose_firm: succ_node = \ [n for n, v in G_FirmProd.nodes(data=True) if v['Firm_Code'] == succ_firm and v['Product_Code'] == succ_product_code][0] G_FirmProd.add_edge(current_node, succ_node) self.sample.g_firm = json.dumps(nx.adjacency_data(G_Firm)) self.firm_network = ap.Network(self, G_Firm) self.firm_prod_network = G_FirmProd # import matplotlib.pyplot as plt # nx.draw(G_FirmProd) # plt.show() # initialize product for ag_node, attr in self.product_network.graph.nodes(data=True): product = ProductAgent(self, code=ag_node.label, name=attr['Name']) self.product_network.add_agents([product], [ag_node]) self.a_lst_total_products = ap.AgentList(self, self.product_network.agents) # initialize firm for ag_node, attr in self.firm_network.graph.nodes(data=True): firm_agent = FirmAgent( self, code=ag_node.label, type_region=attr['Type_Region'], revenue_log=attr['Revenue_Log'], a_lst_product=self.a_lst_total_products.select([ code in attr['Product_Code'] for code in self.a_lst_total_products.code ])) self.firm_network.add_agents([firm_agent], [ag_node]) self.a_lst_total_firms = ap.AgentList(self, self.firm_network.agents) # initialize dct_lst_init_disrupt_firm_prod (from string to agent) t_dct = {} for firm_code, lst_product in \ self.dct_lst_init_disrupt_firm_prod.items(): firm = self.a_lst_total_firms.select( self.a_lst_total_firms.code == firm_code)[0] t_dct[firm] = self.a_lst_total_products.select([ code in lst_product for code in self.a_lst_total_products.code ]) self.dct_lst_init_disrupt_firm_prod = t_dct # set the initial firm product that are disrupted # print('\n', '=' * 20, 'step', self.t, '=' * 20) for firm, a_lst_product in self.dct_lst_init_disrupt_firm_prod.items(): for product in a_lst_product: assert product in firm.dct_prod_up_prod_stat.keys(), \ f"product {product.code} not in firm {firm.code}" firm.dct_prod_up_prod_stat[ product]['p_stat'].append(('D', self.t)) # print(f"initial disruption {firm.name} {product.code}") # draw network # self.draw_network() def update(self): self.a_lst_total_firms.clean_before_time_step() # reduce the size of disrupted firm for firm in self.a_lst_total_firms: 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': size = firm.size_stat[-1][0] - \ firm.size_stat[0][0] \ / len(firm.dct_prod_up_prod_stat.keys()) \ / self.remove_t firm.size_stat.append((size, self.t)) # print(f'in ts {self.t}, reduce {firm.name} size ' # f'to {firm.size_stat[-1][0]} due to {prod.code}') lst_is_disrupt = \ [stat == 'D' for stat, _ in firm.dct_prod_up_prod_stat[prod]['p_stat'] [-1 * self.remove_t:]] if all(lst_is_disrupt): # turn disrupted firm into removed firm # when last self.remove_t times status is all disrupted firm.dct_prod_up_prod_stat[ prod]['p_stat'].append(('R', self.t)) # stop simulation if any firm still in disrupted except initial removal if self.t > 0: for firm in self.a_lst_total_firms: for prod in firm.dct_prod_up_prod_stat.keys(): status, _ = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1] is_init = \ firm in self.dct_lst_init_disrupt_firm_prod.keys() \ and prod in self.dct_lst_init_disrupt_firm_prod[firm] if status == 'D' and not is_init: # print("not stop because", firm.name, prod.code) break else: continue break else: self.int_stop_ts = self.t self.stop() if self.t == self.int_n_iter: self.stop() def step(self): # print('\n', '=' * 20, 'step', self.t, '=' * 20) # remove edge to customer and disrupt customer up product for firm in self.a_lst_total_firms: for prod in firm.dct_prod_up_prod_stat.keys(): # repetition of disrupted firm that last for multiple ts is ok, # as their edge has already been removed 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.a_lst_total_firms: 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) for n_trial in range(self.int_n_max_trial): # print('=' * 10, 'trial', n_trial, '=' * 10) # seek_alt_supply # shuffle self.a_lst_total_firms self.a_lst_total_firms = self.a_lst_total_firms.shuffle() is_stop_trial = True for firm in self.a_lst_total_firms: 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) # commmon supply only seek once 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_request # shuffle self.a_lst_total_firms self.a_lst_total_firms = self.a_lst_total_firms.shuffle() for firm in self.a_lst_total_firms: if len(firm.dct_request_prod_from_firm) > 0: firm.handle_request() # reset dct_request_prod_from_firm self.a_lst_total_firms.clean_before_trial() def end(self): # print('/' * 20, 'output', '/' * 20) qry_result = db_session.query(Result).filter_by(s_id=self.sample.id) if qry_result.count() == 0: lst_result_info = [] for firm in self.a_lst_total_firms: for prod, dct_status_supply in \ firm.dct_prod_up_prod_stat.items(): lst_is_normal = [stat == 'N' for stat, _ in dct_status_supply['p_stat']] if not all(lst_is_normal): # print(f"{firm.name} {prod.code}:") # print(dct_status_supply['p_stat']) for status, ts in dct_status_supply['p_stat']: db_r = Result(s_id=self.sample.id, id_firm=firm.code, id_product=prod.code, ts=ts, status=status) lst_result_info.append(db_r) db_session.bulk_save_objects(lst_result_info) db_session.commit() self.sample.is_done_flag = 1 self.sample.computer_name = platform.node() self.sample.stop_t = self.int_stop_ts db_session.commit() 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="") # desensitize node_label = nx.get_node_attributes(self.firm_network.graph, 'Revenue_Log') node_label = { key: key for key in node_label.keys() } node_degree = dict(self.firm_network.graph.out_degree()) node_label = { key: f"{key} {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")