500 lines
24 KiB
Python
500 lines
24 KiB
Python
import agentpy as ap
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import pandas as pd
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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 para
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self.sample = self.p.sample
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self.int_stop_ts = 0
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self.int_n_iter = int(self.p.n_iter)
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self.product_network = None # agentpy network
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self.firm_network = None # agentpy network
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self.firm_prod_network = None # networkx
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self.dct_lst_init_disrupt_firm_prod = \
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self.p.dct_lst_init_disrupt_firm_prod
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# external variable
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self.int_n_max_trial = int(self.p.n_max_trial)
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self.is_prf_size = bool(self.p.prf_size)
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self.proactive_ratio = float(self.p.proactive_ratio)
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self.remove_t = int(self.p.remove_t)
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self.int_netw_prf_n = int(self.p.netw_prf_n)
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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_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_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_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_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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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_lst_init_disrupt_firm_prod (from string to agent)
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t_dct = {}
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for firm_code, lst_product in \
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self.dct_lst_init_disrupt_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_init_disrupt_firm_prod = t_dct
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# set the initial firm product that are disrupted
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# print('\n', '=' * 20, 'step', self.t, '=' * 20)
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for firm, a_lst_product in self.dct_lst_init_disrupt_firm_prod.items():
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for product in a_lst_product:
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assert product in firm.dct_prod_up_prod_stat.keys(), \
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f"product {product.code} not in firm {firm.code}"
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firm.dct_prod_up_prod_stat[
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product]['status'].append(('D', self.t))
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# print(f"initial disruption {firm.name} {product.code}")
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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_init_disrupt_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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replace=False)]
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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.a_lst_total_firms.select([
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firm.is_prod_in_current_normal(di_supp_prod)
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for firm in self.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.size_stat[-1][0])
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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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# and is not a current supplier for the same product
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lst_current_supp_code = \
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[self.firm_prod_network.nodes[code]['Firm_Code']
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for code in self.firm_prod_network.predecessors(
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pro_firm_prod_code)
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if self.firm_prod_network.nodes[code][
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'Product_Code'] == di_supp_prod.code]
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lst_cand = self.model.a_lst_total_firms.select([
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firm.is_prod_in_current_normal(di_supp_prod)
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and firm.code not in lst_current_supp_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.name} to "
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# f"{pro_firm.name} "
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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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# reduce the size of disrupted firm
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for firm in self.a_lst_total_firms:
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for prod in firm.dct_prod_up_prod_stat.keys():
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status, ts = firm.dct_prod_up_prod_stat[prod]['status'][-1]
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if status == 'D':
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size = firm.size_stat[-1][0] - \
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firm.size_stat[0][0] \
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/ len(firm.dct_prod_up_prod_stat.keys()) \
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/ self.remove_t
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firm.size_stat.append((size, self.t))
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# print(f'in ts {self.t}, reduce {firm.name} size '
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# f'to {firm.size_stat[-1][0]} due to {prod.code}')
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lst_is_disrupt = \
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[stat == 'D' for stat, _ in
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firm.dct_prod_up_prod_stat[prod]['status']
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[-1 * self.remove_t:]]
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if all(lst_is_disrupt):
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# turn disrupted firm into removed firm
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# when last self.remove_t times status is all disrupted
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firm.dct_prod_up_prod_stat[
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prod]['status'].append(('R', self.t))
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# stop simulation if any firm still in disrupted except inital removal
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if self.t > 0:
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for firm in self.a_lst_total_firms:
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for prod in firm.dct_prod_up_prod_stat.keys():
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status, _ = firm.dct_prod_up_prod_stat[prod]['status'][-1]
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is_init = \
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firm in self.dct_lst_init_disrupt_firm_prod.keys() \
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and prod in self.dct_lst_init_disrupt_firm_prod[firm]
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if status == 'D' and not is_init:
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# print("not stop because", firm.name, prod.code)
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break
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else:
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continue
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break
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else:
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self.int_stop_ts = self.t
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self.stop()
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if self.t == self.int_n_iter:
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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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# remove edge to customer and disrupt customer up product
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for firm in self.a_lst_total_firms:
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for prod in firm.dct_prod_up_prod_stat.keys():
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# repetition of disrupted firm that last for multiple ts is ok,
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# as their edge has already been removed
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status, ts = firm.dct_prod_up_prod_stat[prod]['status'][-1]
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if status == 'D' and ts == self.t-1:
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firm.remove_edge_to_cus_disrupt_cus_up_prod(prod)
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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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is_stop_trial = True
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for firm in self.a_lst_total_firms:
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lst_seek_prod = []
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for prod in firm.dct_prod_up_prod_stat.keys():
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status = firm.dct_prod_up_prod_stat[prod]['status'][-1][0]
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if status == 'D':
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for supply in firm.dct_prod_up_prod_stat[
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prod]['supply'].keys():
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if not firm.dct_prod_up_prod_stat[
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prod]['supply'][supply]:
|
|
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()
|
|
# do not use:
|
|
# self.a_lst_total_firms.dct_request_prod_from_firm = {} why?
|
|
|
|
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['status']]
|
|
if not all(lst_is_normal):
|
|
# print(f"{firm.name} {prod.code}:")
|
|
# print(dct_status_supply['status'])
|
|
for status, ts in dct_status_supply['status']:
|
|
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="")
|
|
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")
|