552 lines
28 KiB
Python
552 lines
28 KiB
Python
import agentpy as ap
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import pandas as pd
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import numpy as np
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import networkx as nx
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from firm import FirmAgent
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from product import ProductAgent
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from orm import db_session, Result
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import platform
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import json
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class Model(ap.Model):
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def setup(self):
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self.sample = self.p.sample
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self.int_stop_times, self.int_stop_t = 0, None
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self.nprandom = np.random.default_rng(self.p.seed)
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self.int_n_iter = int(self.p.n_iter)
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self.dct_lst_remove_firm_prod = self.p.dct_lst_init_remove_firm_prod
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self.int_n_max_trial = int(self.p.n_max_trial)
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self.int_netw_sply_prf_n = int(self.p.netw_sply_prf_n)
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self.is_netw_sply_prf_size = bool(self.p.netw_sply_prf_size)
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self.int_netw_cust_prf_n = int(self.p.netw_cust_prf_n)
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self.is_netw_cust_prf_size = bool(self.p.netw_cust_prf_size)
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self.str_cap_limit_prob_type = str(self.p.cap_limit_prob_type)
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self.flt_cap_limit_level = float(self.p.cap_limit_level)
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self.flt_diff_remove = float(self.p.diff_remove)
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self.proactive_ratio = float(self.p.proactive_ratio)
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# init graph bom
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G_bom = nx.adjacency_graph(json.loads(self.p.g_bom))
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self.product_network = ap.Network(self, G_bom)
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# init graph firm
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Firm = pd.read_csv("Firm_amended.csv")
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Firm['Code'] = Firm['Code'].astype('string')
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Firm.fillna(0, inplace=True)
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Firm_attr = Firm.loc[:, ["Code", "Name", "Type_Region", "Revenue_Log"]]
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firm_product = []
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for _, row in Firm.loc[:, '1':].iterrows():
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firm_product.append(row[row == 1].index.to_list())
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Firm_attr.loc[:, 'Product_Code'] = firm_product
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Firm_attr.set_index('Code', inplace=True)
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G_Firm = nx.MultiDiGraph()
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G_Firm.add_nodes_from(Firm["Code"])
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firm_labels_dict = {}
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for code in G_Firm.nodes:
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firm_labels_dict[code] = Firm_attr.loc[code].to_dict()
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nx.set_node_attributes(G_Firm, firm_labels_dict)
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# init graph firm prod
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Firm_Prod = pd.read_csv("Firm_amended.csv")
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Firm_Prod.fillna(0, inplace=True)
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firm_prod = pd.DataFrame({'bool': Firm_Prod.loc[:, '1':].stack()})
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firm_prod = firm_prod[firm_prod['bool'] == 1].reset_index()
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firm_prod.drop('bool', axis=1, inplace=True)
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firm_prod.rename({'level_0': 'Firm_Code',
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'level_1': 'Product_Code'}, axis=1, inplace=True)
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firm_prod['Firm_Code'] = firm_prod['Firm_Code'].astype('string')
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G_FirmProd = nx.MultiDiGraph()
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G_FirmProd.add_nodes_from(firm_prod.index)
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firm_prod_labels_dict = {}
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for code in firm_prod.index:
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firm_prod_labels_dict[code] = firm_prod.loc[code].to_dict()
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nx.set_node_attributes(G_FirmProd, firm_prod_labels_dict)
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# add edge to G_firm according to G_bom
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for node in nx.nodes(G_Firm):
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lst_pred_product_code = []
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for product_code in G_Firm.nodes[node]['Product_Code']:
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lst_pred_product_code += list(G_bom.predecessors(product_code))
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lst_pred_product_code = list(set(lst_pred_product_code))
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# to generate consistant graph
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lst_pred_product_code = list(sorted(lst_pred_product_code))
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for pred_product_code in lst_pred_product_code:
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# for each product predecessor (component) the firm need
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# get a list of firm producing this component
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lst_pred_firm = \
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Firm['Code'][Firm[pred_product_code] == 1].to_list()
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# select multiple supplier (multi-sourcing)
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n_pred_firm = self.int_netw_sply_prf_n
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if n_pred_firm > len(lst_pred_firm):
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n_pred_firm = len(lst_pred_firm)
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# based on size or not
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if self.is_netw_sply_prf_size:
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lst_pred_firm_size = \
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[G_Firm.nodes[pred_firm]['Revenue_Log']
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for pred_firm in lst_pred_firm]
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lst_prob = \
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[size / sum(lst_pred_firm_size)
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for size in lst_pred_firm_size]
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lst_choose_firm = self.nprandom.choice(lst_pred_firm,
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n_pred_firm,
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replace=False,
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p=lst_prob)
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else:
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lst_choose_firm = self.nprandom.choice(lst_pred_firm,
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n_pred_firm,
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replace=False)
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lst_add_edge = [(pred_firm, node,
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{'Product': pred_product_code})
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for pred_firm in lst_choose_firm]
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G_Firm.add_edges_from(lst_add_edge)
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# graph firm prod
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set_node_prod_code = set(G_Firm.nodes[node]['Product_Code'])
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set_pred_succ_code = set(G_bom.successors(pred_product_code))
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lst_use_pred_prod_code = list(
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set_node_prod_code & set_pred_succ_code)
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for pred_firm in lst_choose_firm:
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pred_node = [n for n, v in G_FirmProd.nodes(data=True)
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if v['Firm_Code'] == pred_firm and
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v['Product_Code'] == pred_product_code][0]
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for use_pred_prod_code in lst_use_pred_prod_code:
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current_node = \
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[n for n, v in G_FirmProd.nodes(data=True)
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if v['Firm_Code'] == node and
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v['Product_Code'] == use_pred_prod_code][0]
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G_FirmProd.add_edge(pred_node, current_node)
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# nx.to_pandas_adjacency(G_Firm).to_csv('adj_g_firm.csv')
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# nx.to_pandas_adjacency(G_FirmProd).to_csv('adj_g_firm_prod.csv')
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# unconnected node
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for node in nx.nodes(G_Firm):
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if G_Firm.degree(node) == 0:
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for product_code in G_Firm.nodes[node]['Product_Code']:
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# unconnect node does not have possible suppliers
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# current node in graph firm prod
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current_node = \
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[n for n, v in G_FirmProd.nodes(data=True)
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if v['Firm_Code'] == node and
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v['Product_Code'] == product_code][0]
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lst_succ_product_code = list(
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G_bom.successors(product_code))
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# different from for different types of product,
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# finding a common supplier (the logic above),
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# for different types of product,
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# finding a custormer for each product
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for succ_product_code in lst_succ_product_code:
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# for each product successor (finished product)
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# the firm sells to,
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# get a list of firm producing this finished product
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lst_succ_firm = Firm['Code'][
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Firm[succ_product_code] == 1].to_list()
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# select multiple customer (multi-selling)
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n_succ_firm = self.int_netw_cust_prf_n
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if n_succ_firm > len(lst_succ_firm):
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n_succ_firm = len(lst_succ_firm)
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# based on size or not
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if self.is_netw_cust_prf_size:
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lst_succ_firm_size = \
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[G_Firm.nodes[succ_firm]['Revenue_Log']
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for succ_firm in lst_succ_firm]
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lst_prob = \
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[size / sum(lst_succ_firm_size)
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for size in lst_succ_firm_size]
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lst_choose_firm = \
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self.nprandom.choice(lst_succ_firm,
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n_succ_firm,
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replace=False,
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p=lst_prob)
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else:
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lst_choose_firm = \
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self.nprandom.choice(lst_succ_firm,
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n_succ_firm,
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replace=False)
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lst_add_edge = [(node, succ_firm,
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{'Product': product_code})
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for succ_firm in lst_choose_firm]
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G_Firm.add_edges_from(lst_add_edge)
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# graph firm prod
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for succ_firm in lst_choose_firm:
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succ_node = \
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[n for n, v in G_FirmProd.nodes(data=True)
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if v['Firm_Code'] == succ_firm and
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v['Product_Code'] == succ_product_code][0]
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G_FirmProd.add_edge(current_node, succ_node)
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self.sample.g_firm = json.dumps(nx.adjacency_data(G_Firm))
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self.firm_network = ap.Network(self, G_Firm)
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self.firm_prod_network = G_FirmProd
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# import matplotlib.pyplot as plt
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# nx.draw(G_FirmProd)
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# plt.show()
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# init product
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for ag_node, attr in self.product_network.graph.nodes(data=True):
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product = ProductAgent(self, code=ag_node.label, name=attr['Name'])
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self.product_network.add_agents([product], [ag_node])
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self.a_lst_total_products = ap.AgentList(self,
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self.product_network.agents)
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# init firm
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for ag_node, attr in self.firm_network.graph.nodes(data=True):
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firm_agent = FirmAgent(
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self,
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code=ag_node.label,
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name=attr['Name'],
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type_region=attr['Type_Region'],
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revenue_log=attr['Revenue_Log'],
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a_lst_product=self.a_lst_total_products.select([
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code in attr['Product_Code']
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for code in self.a_lst_total_products.code
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]))
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for product in firm_agent.a_lst_product:
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# init extra capacity based on discrete uniform distribution
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assert self.str_cap_limit_prob_type in ['uniform', 'normal'], \
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"cap_limit_prob_type other than uniform, normal"
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if self.str_cap_limit_prob_type == 'uniform':
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extra_cap_mean = \
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firm_agent.revenue_log / self.flt_cap_limit_level
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extra_cap = self.nprandom.integers(extra_cap_mean-2,
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extra_cap_mean+2)
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extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
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# print(firm_agent.name, extra_cap)
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firm_agent.dct_prod_capacity[product] = extra_cap
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elif self.str_cap_limit_prob_type == 'normal':
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extra_cap_mean = \
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firm_agent.revenue_log / self.flt_cap_limit_level
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extra_cap = self.nprandom.normal(extra_cap_mean, 1)
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extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
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# print(firm_agent.name, extra_cap)
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firm_agent.dct_prod_capacity[product] = extra_cap
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self.firm_network.add_agents([firm_agent], [ag_node])
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self.a_lst_total_firms = ap.AgentList(self, self.firm_network.agents)
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# init dct_list_remove_firm_prod (from string to agent)
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t_dct = {}
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for firm_code, lst_product in self.dct_lst_remove_firm_prod.items():
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firm = self.a_lst_total_firms.select(
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self.a_lst_total_firms.code == firm_code)[0]
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t_dct[firm] = self.a_lst_total_products.select([
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code in lst_product for code in self.a_lst_total_products.code
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])
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self.dct_lst_remove_firm_prod = t_dct
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self.dct_lst_disrupt_firm_prod = t_dct
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# init output
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self.lst_dct_lst_remove_firm_prod = []
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self.lst_dct_lst_disrupt_firm_prod = []
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# set the initial firm product that are removed
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for firm, a_lst_product in self.dct_lst_remove_firm_prod.items():
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for product in a_lst_product:
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assert product in firm.a_lst_product, \
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f"product {product.code} not in firm {firm.code}"
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firm.a_lst_product_removed.append(product)
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# proactive strategy
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# get all the firm prod affected
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for firm, a_lst_product in self.dct_lst_remove_firm_prod.items():
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for product in a_lst_product:
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init_node = \
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[n for n, v in
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self.firm_prod_network.nodes(data=True)
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if v['Firm_Code'] == firm.code and
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v['Product_Code'] == product.code][0]
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dct_affected = \
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nx.dfs_successors(self.firm_prod_network,
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init_node)
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lst_affected = set()
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for i, (u, vs) in enumerate(dct_affected.items()):
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# at least 2 hops away
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if i > 0:
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pred_node = self.firm_prod_network.nodes[u]
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for v in vs:
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succ_node = self.firm_prod_network.nodes[v]
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lst_affected.add((succ_node['Firm_Code'],
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succ_node['Product_Code']))
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lst_affected = list(lst_affected)
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lst_firm_proactive = \
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[lst_affected[i] for i in
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self.nprandom.choice(range(len(lst_affected)),
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round(len(lst_affected) *
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self.proactive_ratio))]
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for firm_code, prod_code in lst_firm_proactive:
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pro_firm_prod_code = \
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[n for n, v in
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self.firm_prod_network.nodes(data=True)
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if v['Firm_Code'] == firm_code and
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v['Product_Code'] == prod_code][0]
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pro_firm_prod_node = \
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self.firm_prod_network.nodes[pro_firm_prod_code]
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pro_firm = \
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self.a_lst_total_firms.select(
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[firm.code == pro_firm_prod_node['Firm_Code']
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for firm in self.a_lst_total_firms])[0]
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lst_shortest_path = \
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list(nx.all_shortest_paths(self.firm_prod_network,
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source=init_node,
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target=pro_firm_prod_code))
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dct_drs = {}
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for di_supp_code in self.firm_prod_network.predecessors(
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pro_firm_prod_code):
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di_supp_node = \
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self.firm_prod_network.nodes[di_supp_code]
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di_supp_prod = \
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self.a_lst_total_products.select(
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[product.code == di_supp_node['Product_Code']
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for product in self.a_lst_total_products])[0]
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di_supp_firm = \
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self.a_lst_total_firms.select(
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[firm.code == di_supp_node['Firm_Code']
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for firm in self.a_lst_total_firms])[0]
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lst_cand = self.model.a_lst_total_firms.select([
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di_supp_prod in firm.a_lst_product
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and di_supp_prod not in firm.a_lst_product_removed
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for firm in self.model.a_lst_total_firms
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])
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n2n_betweenness = \
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sum([True if di_supp_code in path else False
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for path in lst_shortest_path]) \
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/ len(lst_shortest_path)
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drs = n2n_betweenness / \
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(len(lst_cand) * di_supp_firm.revenue_log)
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dct_drs[di_supp_code] = drs
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dct_drs = dict(sorted(
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dct_drs.items(), key=lambda kv: kv[1], reverse=True))
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for di_supp_code in dct_drs.keys():
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di_supp_node = \
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self.firm_prod_network.nodes[di_supp_code]
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di_supp_prod = \
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self.a_lst_total_products.select(
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[product.code == di_supp_node['Product_Code']
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for product in self.a_lst_total_products])[0]
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# find a dfferent firm can produce the same product
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lst_cand = self.model.a_lst_total_firms.select([
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di_supp_prod in firm.a_lst_product
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and di_supp_prod not in firm.a_lst_product_removed
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and firm.code != di_supp_node['Firm_Code']
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for firm in self.model.a_lst_total_firms
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])
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if len(lst_cand) > 0:
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select_cand = self.nprandom.choice(lst_cand)
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self.firm_network.graph.add_edges_from([
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(self.firm_network.positions[select_cand],
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self.firm_network.positions[pro_firm], {
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'Product': di_supp_prod.code
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})
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])
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# print(f"proactive add {select_cand.code} to "
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# f"{pro_firm.code} "
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# f"for {di_supp_node['Firm_Code']} "
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# f"{di_supp_node['Product_Code']}")
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# change capacity
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select_cand.dct_prod_capacity[di_supp_prod] -= 1
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break
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# nx.to_pandas_adjacency(G_Firm).to_csv('adj_g_firm_proactive.csv')
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# draw network
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# self.draw_network()
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def update(self):
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self.a_lst_total_firms.clean_before_time_step()
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# output
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self.lst_dct_lst_remove_firm_prod.append(
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(self.t, self.dct_lst_remove_firm_prod))
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self.lst_dct_lst_disrupt_firm_prod.append(
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(self.t, self.dct_lst_disrupt_firm_prod))
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# stop simulation if reached terminal number of iteration
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if self.t == self.int_n_iter or len(
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self.dct_lst_remove_firm_prod) == 0:
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self.int_stop_times = self.t
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self.stop()
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def step(self):
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# print('\n', '=' * 20, 'step', self.t, '=' * 20)
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# print(
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# 'dct_list_remove_firm_prod', {
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# key.name: value.code
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# for key, value in self.dct_lst_remove_firm_prod.items()
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# })
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# remove_edge_to_cus_and_cus_up_prod
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for firm, a_lst_product in self.dct_lst_remove_firm_prod.items():
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for product in a_lst_product:
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firm.remove_edge_to_cus_remove_cus_up_prod(product)
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for n_trial in range(self.int_n_max_trial):
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# print('=' * 10, 'trial', n_trial, '=' * 10)
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# seek_alt_supply
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# shuffle self.a_lst_total_firms
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self.a_lst_total_firms = self.a_lst_total_firms.shuffle()
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for firm in self.a_lst_total_firms:
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if len(firm.a_lst_up_product_removed) > 0:
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firm.seek_alt_supply()
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# handle_request
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# shuffle self.a_lst_total_firms
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self.a_lst_total_firms = self.a_lst_total_firms.shuffle()
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for firm in self.a_lst_total_firms:
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if len(firm.dct_request_prod_from_firm) > 0:
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firm.handle_request()
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# reset dct_request_prod_from_firm
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self.a_lst_total_firms.clean_before_trial()
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# do not use:
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# self.a_lst_total_firms.dct_request_prod_from_firm = {} why?
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# based on a_lst_up_product_removed
|
|
# update a_lst_product_disrupted / a_lst_product_removed
|
|
# update dct_lst_disrupt_firm_prod / dct_lst_remove_firm_prod
|
|
self.dct_lst_remove_firm_prod = {}
|
|
self.dct_lst_disrupt_firm_prod = {}
|
|
for firm in self.a_lst_total_firms:
|
|
if len(firm.a_lst_up_product_removed) > 0:
|
|
# print(firm.name, 'a_lst_up_product_removed', [
|
|
# product.code for product in firm.a_lst_up_product_removed
|
|
# ])
|
|
for product in firm.a_lst_product:
|
|
n_up_product_removed = 0
|
|
for up_product_removed in firm.a_lst_up_product_removed:
|
|
if product in up_product_removed.a_successors():
|
|
n_up_product_removed += 1
|
|
if n_up_product_removed == 0:
|
|
continue
|
|
else:
|
|
# update a_lst_product_disrupted
|
|
# update dct_lst_disrupt_firm_prod
|
|
if product not in firm.a_lst_product_disrupted:
|
|
firm.a_lst_product_disrupted.append(product)
|
|
if firm in self.dct_lst_disrupt_firm_prod.keys():
|
|
self.dct_lst_disrupt_firm_prod[firm].append(
|
|
product)
|
|
else:
|
|
self.dct_lst_disrupt_firm_prod[
|
|
firm] = ap.AgentList(
|
|
self.model, [product])
|
|
# update a_lst_product_removed
|
|
# update dct_list_remove_firm_prod
|
|
# mark disrupted firm as removed based conditionally
|
|
lost_percent = n_up_product_removed / len(
|
|
product.a_predecessors())
|
|
lst_size = self.a_lst_total_firms.revenue_log
|
|
lst_size = [firm.revenue_log for firm
|
|
in self.a_lst_total_firms
|
|
if product in firm.a_lst_product
|
|
and product
|
|
not in firm.a_lst_product_removed
|
|
]
|
|
std_size = (firm.revenue_log - min(lst_size) +
|
|
1) / (max(lst_size) - min(lst_size) + 1)
|
|
prob_remove = 1 - std_size * (1 - lost_percent)
|
|
# damp prod
|
|
prob_remove = prob_remove ** self.flt_diff_remove
|
|
# sample prob
|
|
prob_remove = self.nprandom.uniform(
|
|
prob_remove - 0.1, prob_remove + 0.1)
|
|
prob_remove = 1 if prob_remove > 1 else prob_remove
|
|
prob_remove = 0 if prob_remove < 0 else prob_remove
|
|
if self.nprandom.choice([True, False],
|
|
p=[prob_remove,
|
|
1 - prob_remove]):
|
|
firm.a_lst_product_removed.append(product)
|
|
if firm in self.dct_lst_remove_firm_prod.keys():
|
|
self.dct_lst_remove_firm_prod[firm].append(
|
|
product)
|
|
else:
|
|
self.dct_lst_remove_firm_prod[
|
|
firm] = ap.AgentList(
|
|
self.model, [product])
|
|
|
|
# print(
|
|
# 'dct_list_remove_firm_prod', {
|
|
# key.name: value.code
|
|
# for key, value in self.dct_lst_remove_firm_prod.items()
|
|
# })
|
|
|
|
def end(self):
|
|
# print('/' * 20, 'output', '/' * 20)
|
|
# print('dct_list_remove_firm_prod')
|
|
# for t, dct in self.lst_dct_lst_remove_firm_prod:
|
|
# for firm, a_lst_product in dct.items():
|
|
# for product in a_lst_product:
|
|
# print(t, firm.name, product.code)
|
|
# print('dct_lst_disrupt_firm_prod')
|
|
# for t, dct in self.lst_dct_lst_disrupt_firm_prod:
|
|
# for firm, a_lst_product in dct.items():
|
|
# for product in a_lst_product:
|
|
# print(t, firm.name, product.code)
|
|
|
|
qry_result = db_session.query(Result).filter_by(s_id=self.sample.id)
|
|
if qry_result.count() == 0:
|
|
lst_result_info = []
|
|
for t, dct in self.lst_dct_lst_disrupt_firm_prod:
|
|
for firm, a_lst_product in dct.items():
|
|
for product in a_lst_product:
|
|
db_r = Result(s_id=self.sample.id,
|
|
id_firm=firm.code,
|
|
id_product=product.code,
|
|
ts=t,
|
|
is_disrupted=True)
|
|
lst_result_info.append(db_r)
|
|
db_session.bulk_save_objects(lst_result_info)
|
|
db_session.commit()
|
|
for t, dct in self.lst_dct_lst_remove_firm_prod:
|
|
for firm, a_lst_product in dct.items():
|
|
for product in a_lst_product:
|
|
# only firm disrupted can be removed theoretically
|
|
qry_f_p = db_session.query(Result).filter(
|
|
Result.s_id == self.sample.id,
|
|
Result.id_firm == firm.code,
|
|
Result.id_product == product.code)
|
|
if qry_f_p.count() == 1:
|
|
qry_f_p.update({"is_removed": True})
|
|
db_session.commit()
|
|
self.sample.is_done_flag = 1
|
|
self.sample.computer_name = platform.node()
|
|
self.sample.stop_t = self.int_stop_times
|
|
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="")
|
|
node_label = nx.get_node_attributes(self.firm_network.graph, 'Name')
|
|
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")
|