106 lines
3.9 KiB
Python
106 lines
3.9 KiB
Python
import agentpy as ap
|
|
import pandas as pd
|
|
import numpy as np
|
|
import networkx as nx
|
|
|
|
sample = 0
|
|
sample = 0
|
|
dct_sample_para = {'sample': sample, 'seed': sample}
|
|
|
|
|
|
class Model(ap.Model):
|
|
def setup(self):
|
|
self.sample = self.p.sample
|
|
self.nprandom = np.random.default_rng(self.p.seed)
|
|
|
|
# init graph bom
|
|
BomNodes = pd.read_csv('BomNodes.csv', index_col=0)
|
|
BomNodes.set_index('Code', inplace=True)
|
|
BomCateNet = pd.read_csv('BomCateNet.csv', index_col=0)
|
|
BomCateNet.fillna(0, inplace=True)
|
|
|
|
G_bom = nx.from_pandas_adjacency(BomCateNet,
|
|
create_using=nx.MultiDiGraph())
|
|
|
|
bom_labels_dict = {}
|
|
for code in G_bom.nodes:
|
|
bom_labels_dict[code] = BomNodes.loc[code].to_dict()
|
|
nx.set_node_attributes(G_bom, bom_labels_dict)
|
|
|
|
# init graph firm
|
|
Firm = pd.read_csv("Firm_amended.csv")
|
|
Firm.fillna(0, inplace=True)
|
|
Firm_attr = Firm.loc[:, ["Code", "Name", "Type_Region", "Revenue_Log"]]
|
|
firm_product = []
|
|
for _, row in Firm.loc[:, '1':].iterrows():
|
|
firm_product.append(row[row == 1].index.to_list())
|
|
Firm_attr.loc[:, 'Product_Code'] = firm_product
|
|
Firm_attr.set_index('Code')
|
|
G_Firm = nx.MultiDiGraph()
|
|
G_Firm.add_nodes_from(Firm["Code"])
|
|
|
|
firm_labels_dict = {}
|
|
for code in G_Firm.nodes:
|
|
firm_labels_dict[code] = Firm_attr.loc[code].to_dict()
|
|
nx.set_node_attributes(G_Firm, firm_labels_dict)
|
|
|
|
# add edge to G_firm according to G_bom
|
|
for node in nx.nodes(G_Firm):
|
|
# print(node, '-'*20)
|
|
list_pred_product_code = []
|
|
for product_code in G_Firm.nodes[node]['Product_Code']:
|
|
list_pred_product_code += list(
|
|
G_bom.predecessors(product_code))
|
|
list_pred_product_code = list(set(list_pred_product_code))
|
|
for pred_product_code in list_pred_product_code:
|
|
# print(pred_product_code)
|
|
list_pred_firms = Firm.index[Firm[pred_product_code] ==
|
|
1].to_list()
|
|
list_revenue_log = [
|
|
G_Firm.nodes[pred_firm]['Revenue_Log']
|
|
for pred_firm in list_pred_firms
|
|
]
|
|
list_prob = [
|
|
(v - min(list_revenue_log) + 1) /
|
|
(max(list_revenue_log) - min(list_revenue_log) + 1)
|
|
for v in list_revenue_log
|
|
]
|
|
list_flag = [
|
|
self.nprandom.choice([1, 0], p=[prob, 1 - prob])
|
|
for prob in list_prob
|
|
]
|
|
# print(list(zip(list_pred_firms,list_flag, list_prob)))
|
|
list_added_edges = [
|
|
(node, pred_firm)
|
|
for pred_firm, flag in zip(list_pred_firms, list_flag)
|
|
if flag == 1
|
|
]
|
|
G_Firm.add_edges_from(list_added_edges)
|
|
# print('-'*20)
|
|
|
|
self.firm_network = ap.Network(self, G_Firm)
|
|
|
|
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_size = list(
|
|
nx.get_node_attributes(self.firm_network.graph,
|
|
'Revenue_Log').values())
|
|
node_size = list(map(lambda x: x**2, node_size))
|
|
plt.figure(figsize=(12, 12), dpi=300)
|
|
nx.draw(self.firm_network.graph,
|
|
pos,
|
|
node_size=node_size,
|
|
labels=node_label,
|
|
font_size=6)
|
|
plt.savefig("network.png")
|
|
|
|
|
|
model = Model(dct_sample_para)
|
|
model.setup()
|
|
model.draw_network()
|