320 lines
15 KiB
Python
320 lines
15 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 random
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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.random = random.Random(self.p.seed)
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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.int_n_max_trial = int(self.p.n_max_trial)
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self.dct_lst_remove_firm_prod = self.p.dct_lst_init_remove_firm_prod
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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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# add edge to G_firm according to G_bom
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for node in nx.nodes(G_Firm):
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for product_code in G_Firm.nodes[node]['Product_Code']:
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for succ_product_code in list(G_bom.successors(product_code)):
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# for each product of a certain firm
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# get each successor (finished product) of this product
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# get a list of firm producing this successor
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lst_succ_firm = Firm['Code'][Firm[succ_product_code] ==
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1].to_list()
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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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]
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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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]
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# select multiple successors based on relative size of this firm
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lst_same_prod_firm = Firm['Code'][Firm[product_code] ==
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1].to_list()
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lst_same_prod_firm_size = [
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G_Firm.nodes[f]['Revenue_Log']
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for f in lst_same_prod_firm
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]
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share = G_Firm.nodes[node]['Revenue_Log'] / sum(
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lst_same_prod_firm_size)
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n_succ_firm = round(share * len(lst_succ_firm)) if round(
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share * len(lst_succ_firm)) > 0 else 1 # at least one
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lst_choose_firm = self.nprandom.choice(lst_succ_firm,
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n_succ_firm,
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p=lst_prob)
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lst_choose_firm = list(
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set(lst_choose_firm
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)) # nprandom.choice may have duplicates
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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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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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# 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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# init extra capacity based on discrete uniform distribution
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for product in firm_agent.a_lst_product:
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firm_agent.dct_prod_capacity[product] = self.nprandom.integers(
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firm_agent.revenue_log / 5, firm_agent.revenue_log / 5 + 2)
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# print(firm_agent.name, firm_agent.dct_prod_capacity)
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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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# draw network
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# self.draw_network()
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out_file = open("myfile.json", "w")
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json.dump(nx.adjacency_data(G_Firm), out_file)
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out_file.close()
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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
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# update a_lst_product_disrupted / a_lst_product_removed
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# update dct_lst_disrupt_firm_prod / dct_lst_remove_firm_prod
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self.dct_lst_remove_firm_prod = {}
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self.dct_lst_disrupt_firm_prod = {}
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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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print(firm.name, 'a_lst_up_product_removed', [
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product.code for product in firm.a_lst_up_product_removed
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])
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for product in firm.a_lst_product:
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n_up_product_removed = 0
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for up_product_removed in firm.a_lst_up_product_removed:
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if product in up_product_removed.a_successors():
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n_up_product_removed += 1
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if n_up_product_removed == 0:
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continue
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else:
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# update a_lst_product_disrupted / dct_lst_disrupt_firm_prod
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if product not in firm.a_lst_product_disrupted:
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firm.a_lst_product_disrupted.append(product)
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if firm in self.dct_lst_disrupt_firm_prod.keys():
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self.dct_lst_disrupt_firm_prod[firm].append(
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product)
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else:
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self.dct_lst_disrupt_firm_prod[
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firm] = ap.AgentList(
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self.model, [product])
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# update a_lst_product_removed / dct_list_remove_firm_prod
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# mark disrupted firm as removed based conditionally
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lost_percent = n_up_product_removed / len(
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product.a_predecessors())
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lst_size = self.a_lst_total_firms.revenue_log
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std_size = (firm.revenue_log - min(lst_size) +
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1) / (max(lst_size) - min(lst_size) + 1)
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prod_remove = 1 - std_size * (1 - lost_percent)
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if self.nprandom.choice(
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[True, False], p=[prod_remove, 1 - prod_remove]):
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firm.a_lst_product_removed.append(product)
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if firm in self.dct_lst_remove_firm_prod.keys():
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self.dct_lst_remove_firm_prod[firm].append(
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product)
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else:
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self.dct_lst_remove_firm_prod[
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firm] = ap.AgentList(
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self.model, [product])
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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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def end(self):
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print('/' * 20, 'output', '/' * 20)
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print('dct_list_remove_firm_prod')
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for t, dct in self.lst_dct_lst_remove_firm_prod:
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for firm, a_lst_product in dct.items():
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for product in a_lst_product:
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print(t, firm.name, product.code)
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print('dct_lst_disrupt_firm_prod')
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for t, dct in self.lst_dct_lst_disrupt_firm_prod:
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for firm, a_lst_product in dct.items():
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for product in a_lst_product:
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print(t, firm.name, product.code)
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qry_result = db_session.query(Result).filter_by(s_id=self.sample.id)
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if qry_result.count() == 0:
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lst_result_info = []
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for t, dct in self.lst_dct_lst_disrupt_firm_prod:
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for firm, a_lst_product in dct.items():
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for product in a_lst_product:
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db_r = Result(s_id=self.sample.id,
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id_firm=firm.code,
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id_product=product.code,
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ts=t,
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is_disrupted=True)
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lst_result_info.append(db_r)
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db_session.bulk_save_objects(lst_result_info)
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db_session.commit()
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for t, dct in self.lst_dct_lst_remove_firm_prod:
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for firm, a_lst_product in dct.items():
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for product in a_lst_product:
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# only firm disrupted can be removed theoretically
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qry_f_p = db_session.query(Result).filter(
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Result.s_id == self.sample.id,
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Result.id_firm == firm.code,
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Result.id_product == product.code)
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if qry_f_p.count() == 1:
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qry_f_p.update({"is_removed": True})
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db_session.commit()
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self.sample.is_done_flag = 1
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self.sample.computer_name = platform.node()
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self.sample.stop_t = self.int_stop_times
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db_session.commit()
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def draw_network(self):
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import matplotlib.pyplot as plt
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plt.rcParams['font.sans-serif'] = 'SimHei'
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pos = nx.nx_agraph.graphviz_layout(self.firm_network.graph,
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prog="twopi",
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args="")
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node_label = nx.get_node_attributes(self.firm_network.graph, 'Name')
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# print(node_label)
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node_degree = dict(self.firm_network.graph.out_degree())
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node_label = {
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key: f"{node_label[key]} {node_degree[key]}"
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for key in node_label.keys()
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}
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node_size = list(
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nx.get_node_attributes(self.firm_network.graph,
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'Revenue_Log').values())
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node_size = list(map(lambda x: x**2, node_size))
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edge_label = nx.get_edge_attributes(self.firm_network.graph, "Product")
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# multi(di)graphs, the keys are 3-tuples
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edge_label = {(n1, n2): label
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for (n1, n2, _), label in edge_label.items()}
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plt.figure(figsize=(12, 12), dpi=300)
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nx.draw(self.firm_network.graph,
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pos,
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node_size=node_size,
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labels=node_label,
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font_size=6)
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nx.draw_networkx_edge_labels(self.firm_network.graph,
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pos,
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edge_label,
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font_size=4)
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plt.savefig("network.png")
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