329 lines
14 KiB
Python
329 lines
14 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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sample = 0
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seed = 0
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n_iter = 10
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# dct_list_init_remove_firm_prod = {133: ['1.4.4.1'], 2: ['1.1.3']}
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# dct_list_init_remove_firm_prod = {
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# 135: ['1.3.2.1'],
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# 133: ['1.4.4.1'],
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# 2: ['1.1.3']
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# }
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dct_list_init_remove_firm_prod = {
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140: ['1.4.5.1'],
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135: ['1.3.2.1'],
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133: ['1.4.4.1'],
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2: ['1.1.3']
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}
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n_max_trial = 5
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dct_sample_para = {
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'sample': sample,
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'seed': seed,
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'n_iter': n_iter,
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'n_max_trial': n_max_trial,
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'dct_list_init_remove_firm_prod': dct_list_init_remove_firm_prod,
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}
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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.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_list_remove_firm_prod = self.p.dct_list_init_remove_firm_prod
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# init graph bom
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BomNodes = pd.read_csv('BomNodes.csv', index_col=0)
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BomNodes.set_index('Code', inplace=True)
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BomCateNet = pd.read_csv('BomCateNet.csv', index_col=0)
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BomCateNet.fillna(0, inplace=True)
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G_bom = nx.from_pandas_adjacency(BomCateNet.T,
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create_using=nx.MultiDiGraph())
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bom_labels_dict = {}
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for code in G_bom.nodes:
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bom_labels_dict[code] = BomNodes.loc[code].to_dict()
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nx.set_node_attributes(G_bom, bom_labels_dict)
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# init graph firm
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Firm = pd.read_csv("Firm_amended.csv")
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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')
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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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# print(node, '-' * 20)
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for product_code in G_Firm.nodes[node]['Product_Code']:
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# print(product_code)
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for succ_product_code in list(G_bom.successors(product_code)):
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# print(succ_product_code)
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list_succ_firms = Firm.index[Firm[succ_product_code] ==
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1].to_list()
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list_revenue_log = [
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G_Firm.nodes[succ_firm]['Revenue_Log']
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for succ_firm in list_succ_firms
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]
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# list_prob = [
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# (v - min(list_revenue_log) + 1) /
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# (max(list_revenue_log) - min(list_revenue_log) + 1)
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# for v in list_revenue_log
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# ]
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# list_flag = [
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# self.nprandom.choice([1, 0], p=[prob, 1 - prob])
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# for prob in list_prob
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# ]
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# # print(list(zip(list_succ_firms,list_flag,list_prob)))
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# list_added_edges = [(node, succ_firm, {
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# 'Product': product_code
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# }) for succ_firm, flag in zip(list_succ_firms, list_flag)
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# if flag == 1]
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list_prob = [
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size / sum(list_revenue_log)
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for size in list_revenue_log
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]
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succ_firm = self.nprandom.choice(list_succ_firms,
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p=list_prob)
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list_added_edges = [(node, succ_firm, {
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'Product': product_code
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})]
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G_Firm.add_edges_from(list_added_edges)
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# print('-' * 20)
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self.firm_network = ap.Network(self, G_Firm)
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self.product_network = ap.Network(self, G_bom)
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# print([node.label for node in self.firm_network.nodes])
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# print([list(self.firm_network.graph.predecessors(node))
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# for node in self.firm_network.nodes])
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# print([self.firm_network.graph.nodes[node.label]['Name']
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# for node in self.firm_network.nodes])
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# print([v for v in self.firm_network.graph.nodes(data=True)])
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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_agent = ProductAgent(self,
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code=ag_node.label,
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name=attr['Name'])
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self.product_network.add_agents([product_agent], [ag_node])
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self.a_list_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=attr['Code'],
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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_list_product=self.a_list_total_products.select([
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code in attr['Product_Code']
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for code in self.a_list_total_products.code
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]))
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# init capacity based on discrete uniform distribution
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# list_out_edges = list(
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# self.firm_network.graph.out_edges(ag_node,
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# keys=True,
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# data='Product'))
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# for product in firm_agent.a_list_product:
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# capacity = len([
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# edge for edge in list_out_edges if edge[-1] ==
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# product.code])
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# firm_agent.dct_prod_capacity[product] = capacity
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for product in firm_agent.a_list_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_list_total_firms = ap.AgentList(self, self.firm_network.agents)
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# print(list(zip(self.a_list_total_firms.code,
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# self.a_list_total_firms.name,
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# self.a_list_total_firms.capacity)))
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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, list_product in self.dct_list_remove_firm_prod.items():
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firm = self.a_list_total_firms.select(
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self.a_list_total_firms.code == firm_code)[0]
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t_dct[firm] = self.a_list_total_products.select([
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code in list_product
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for code in self.a_list_total_products.code
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])
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self.dct_list_remove_firm_prod = t_dct
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# set the initial firm product that are removed
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for firm, a_list_product in self.dct_list_remove_firm_prod.items():
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for product in a_list_product:
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assert product in firm.a_list_product, \
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f"product {product.code} not in firm {firm.code}"
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firm.a_list_product_removed.append(product)
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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_list_total_firms.clean_before_time_step()
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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_list_remove_firm_prod) == 0:
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self.stop()
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def step(self):
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# shuffle self.dct_list_remove_firm_prod
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# dct_key_list = list(self.dct_list_remove_firm_prod.keys())
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# self.nprandom.shuffle(dct_key_list)
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# self.dct_list_remove_firm_prod = {
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# key: self.dct_list_remove_firm_prod[key].shuffle()
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# for key in dct_key_list
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# }
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# print(self.dct_list_remove_firm_prod)
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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_list_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_list_product in self.dct_list_remove_firm_prod.items():
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for product in a_list_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_list_total_firms
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self.a_list_total_firms = self.a_list_total_firms.shuffle()
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for firm in self.a_list_total_firms:
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if len(firm.a_list_up_product_removed) > 0:
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# print(firm.name)
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# print(firm.a_list_up_product_removed.code)
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firm.seek_alt_supply()
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# handle_request
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# shuffle self.a_list_total_firms
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self.a_list_total_firms = self.a_list_total_firms.shuffle()
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for firm in self.a_list_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_list_total_firms.clean_before_trial()
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# do not use:
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# self.a_list_total_firms.dct_request_prod_from_firm = {} why?
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# based on a_list_up_product_removed,
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# update a_list_product_disrupted / a_list_product_removed / dct_list_remove_firm_prod
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self.dct_list_remove_firm_prod = {}
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for firm in self.a_list_total_firms:
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if len(firm.a_list_up_product_removed) > 0:
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print(firm.name, 'a_list_up_product_removed', [product.code for product in firm.a_list_up_product_removed])
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for product in firm.a_list_product:
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n_up_product_removed = 0
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for up_product_removed in firm.a_list_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_list_product_disrupted
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if product not in firm.a_list_product_disrupted:
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firm.a_list_product_disrupted.append(product)
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# update a_list_product_removed / dct_list_remove_firm_prod
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lost_percent = n_up_product_removed / len(
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product.a_predecessors())
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list_revenue_log = self.a_list_total_firms.revenue_log
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std_size = (firm.revenue_log - min(list_revenue_log) +
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1) / (max(list_revenue_log) -
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min(list_revenue_log) + 1)
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p_remove = 1 - std_size * (1 - lost_percent)
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# flag = self.nprandom.choice([1, 0],
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# p=[p_remove, 1 - p_remove])
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flag = 1
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if flag == 1:
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firm.a_list_product_removed.append(product)
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# if firm in
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# self.dct_list_remove_firm_prod[firm] = firm.a_list_product_removed
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if firm in self.dct_list_remove_firm_prod.keys():
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self.dct_list_remove_firm_prod[firm].append(
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product)
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else:
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self.dct_list_remove_firm_prod[
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firm] = ap.AgentList(
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self.model, [product])
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# # update the firm that is removed
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# self.dct_list_remove_firm_prod = {}
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# for firm in self.a_list_total_firms:
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# if len(firm.a_list_product_removed) > 0:
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# self.dct_list_remove_firm_prod[
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# firm] = firm.a_list_product_removed
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# print(self.dct_list_remove_firm_prod)
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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_list_remove_firm_prod.items()
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})
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def end(self):
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pass
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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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# model = Model(dct_sample_para)
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# model.run()
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