152 lines
5.7 KiB
Python
152 lines
5.7 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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sample = 0
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seed = 0
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n_iter = 3
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list_init_remove_firm_code = [0, 2]
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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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'list_init_remove_firm_code': list_init_remove_firm_code
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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.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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# 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,
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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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list_pred_product_code = []
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for product_code in G_Firm.nodes[node]['Product_Code']:
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list_pred_product_code += list(
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G_bom.predecessors(product_code))
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list_pred_product_code = list(set(list_pred_product_code))
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for pred_product_code in list_pred_product_code:
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# print(pred_product_code)
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list_pred_firms = Firm.index[Firm[pred_product_code] ==
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1].to_list()
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list_revenue_log = [
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G_Firm.nodes[pred_firm]['Revenue_Log']
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for pred_firm in list_pred_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_pred_firms,list_flag, list_prob)))
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list_added_edges = [
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(node, pred_firm)
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for pred_firm, flag in zip(list_pred_firms, list_flag)
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if flag == 1
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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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# print([node.label for node in self.firm_network.nodes])
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# print([list(self.firm_network.graph.predecessors(node)) for node in self.firm_network.nodes])
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# print([self.firm_network.graph.nodes[node.label]['Name'] 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 firm
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for ag_node, attr in self.firm_network.graph.nodes(data=True):
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firm_agent = FirmAgent(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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self.firm_network.add_agents([firm_agent], [ag_node])
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a_list_total_firms = ap.AgentList(self, self.firm_network.agents)
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# set the initial firm that is removed
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list_b_is_removed = list(
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map(lambda x: x in self.p.list_init_remove_firm_code,
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self.firm_network.agents.code))
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self.a_list_firms_removed = a_list_total_firms.select(
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list_b_is_removed)
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self.a_list_firms_removed.is_removed = True
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def update(self):
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# Update list of unhappy people
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# self.agents.update_happiness()
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# self.unhappy = self.agents.select(self.agents.happy == False)
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# Stop simulation if reached terminal number of iteration
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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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# Move unhappy people to new location
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self.unhappy.find_new_home()
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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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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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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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plt.savefig("network.png")
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model = Model(dct_sample_para)
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model.setup()
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# model.draw_network()
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