1.3 MiB
1.3 MiB
In [1]:
import pandas as pd
import numpy as np
import math
import statsmodels.formula.api as smf
from sklearn.preprocessing import MinMaxScaler
import networkx as nx
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['font.sans-serif'] = 'SimHei'
# 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 = nx.from_pandas_adjacency(BomCateNet, create_using=nx.MultiDiGraph())
labels_dict = {}
for code in G.nodes:
labels_dict[code] = BomNodes.loc[code].to_dict()
nx.set_node_attributes(G, labels_dict)
# load firm
Firm = pd.read_csv("Firm.csv")
Firm_copy = Firm.copy()
# add attribute, number of firm, to graph bom
firm_num_dict = {}
for node in nx.nodes(G):
firm_num_dict[node]= sum(Firm_copy[node]==1)
nx.set_node_attributes(G, firm_num_dict, name="Num_Firm")
# get log revenue
Firm_copy['Revenue_Log'] = Firm_copy['Revenue'].map(math.log)
Firm_copy['Num_Employ_Log'] = Firm_copy['Num_Employ'].map(math.log)
data_ols = Firm_copy[Firm_copy[['Revenue_Log', 'Num_Employ_Log']].notnull().all(axis=1)][['Revenue_Log', 'Num_Employ_Log']]
ols_model = smf.ols('Revenue_Log ~ Num_Employ_Log', data=data_ols)
ols_results = ols_model.fit()
b = ols_results.params.Intercept
a = ols_results.params.Num_Employ_Log
print(ols_results.summary())
sns.regplot(x='Num_Employ_Log',y='Revenue_Log',data=data_ols[data_ols.notnull().all(axis=1)])
Firm_copy.loc[Firm_copy['Revenue_Log'].isnull(), 'Revenue_Log'] = Firm_copy[Firm_copy['Revenue_Log'].isnull()]['Num_Employ_Log'].map(lambda x: a*x + b)
series_Revenue_Log = Firm_copy.pop('Revenue_Log')
Firm_copy.insert(Firm_copy.columns.get_loc('Revenue'),'Revenue_Log', series_Revenue_Log)
series_Num_Employ_Log = Firm_copy.pop('Num_Employ_Log')
Firm_copy.insert(Firm_copy.columns.get_loc('Num_Employ'),'Num_Employ_Log', series_Num_Employ_Log)
# lift firm from neighboring tier
node_list = list(nx.bfs_tree(G, '1'))
node_list.reverse()
# init criterion for ending the lifting process
node_check = [k for k, v in nx.get_node_attributes(G, 'Num_Firm').items() if v==0]
if '1' in node_check:
node_check.remove('1')
for node in node_check:
if G.out_degree(node) == 0:
node_check.remove(node)
print(f"Warning: node, {node}, in tier 0 does not contain firm")
while node_check:
for node in node_list[:-1]:
if G.out_degree(node) > 0 and sum(Firm_copy[node]==1) == 0:
list_neighbors = list(G.neighbors(node))
firm_list = Firm_copy.index[(Firm_copy[list_neighbors]==1).all(axis=1)].to_list()
if firm_list: # there exist firm that produces all components
# lift firm with size above average in firm_list
average_size = Firm_copy.loc[firm_list, 'Revenue_Log'].mean()
firm_selected_list = Firm_copy.loc[firm_list].loc[Firm_copy.loc[firm_list, 'Revenue_Log'] >= average_size].index.to_list()
Firm_copy.loc[firm_selected_list, node] = 1
Firm_copy.loc[firm_selected_list, list_neighbors] = np.nan
else: # select top 15% firm in terms of size
firm_list = Firm_copy.index[(Firm_copy[list_neighbors]==1).any(axis=1)].to_list()
firm_df = Firm_copy.loc[firm_list].sort_values('Revenue_Log', ascending=False)
num_firm_selected = round(firm_df.shape[0] * 0.15)
firm_selected_list = firm_df.index[0: num_firm_selected].to_list()
Firm_copy.loc[firm_selected_list, node] = 1
Firm_copy.loc[firm_selected_list, list_neighbors] = np.nan
# update attribute, number of firm
for node in nx.nodes(G):
firm_num_dict[node]= sum(Firm_copy[node]==1)
nx.set_node_attributes(G, firm_num_dict, name="Num_Firm")
# update criterion for ending the lifting process
node_check = [k for k, v in nx.get_node_attributes(G, 'Num_Firm').items() if v==0]
if '1' in node_check:
node_check.remove('1')
for node in node_check:
if G.out_degree(node) == 0:
node_check.remove(node)
print(f"Warning: node, {node}, in tier 0 does not contain firm")
# output
# Firm_copy.to_csv('Firm_amended.csv', index=False, encoding='utf-8-sig') OLS Regression Results
==============================================================================
Dep. Variable: Revenue_Log R-squared: 0.586
Model: OLS Adj. R-squared: 0.580
Method: Least Squares F-statistic: 94.86
Date: Sat, 01 Apr 2023 Prob (F-statistic): 1.85e-14
Time: 18:07:54 Log-Likelihood: -128.58
No. Observations: 69 AIC: 261.2
Df Residuals: 67 BIC: 265.6
Df Model: 1
Covariance Type: nonrobust
==================================================================================
coef std err t P>|t| [0.025 0.975]
----------------------------------------------------------------------------------
Intercept 14.3160 0.806 17.757 0.000 12.707 15.925
Num_Employ_Log 0.9728 0.100 9.740 0.000 0.773 1.172
==============================================================================
Omnibus: 55.594 Durbin-Watson: 1.958
Prob(Omnibus): 0.000 Jarque-Bera (JB): 244.671
Skew: 2.451 Prob(JB): 7.42e-54
Kurtosis: 10.815 Cond. No. 34.6
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
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C:\Users\ASUS\AppData\Local\Temp\ipykernel_17316\1464358194.py:49: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
Firm_copy.insert(Firm_copy.columns.get_loc('Revenue'),'Revenue_Log', series_Revenue_Log)
C:\Users\ASUS\AppData\Local\Temp\ipykernel_17316\1464358194.py:51: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
Firm_copy.insert(Firm_copy.columns.get_loc('Num_Employ'),'Num_Employ_Log', series_Num_Employ_Log)
In [5]:
# visualization
pos = nx.nx_agraph.graphviz_layout(G, prog="twopi", args="")
dict_num_firm = nx.get_node_attributes(G, 'Num_Firm')
dict_node_name = nx.get_node_attributes(G, 'Name')
node_labels = {}
for node in nx.nodes(G):
node_labels[node] = f"{node} {str(dict_node_name[node])} {str(dict_num_firm[node])}"
# node_labels[node] = f"{str(dict_num_firm[node])}"
plt.figure(figsize=(12, 12), dpi=300)
nx.draw_networkx_nodes(G, pos)
nx.draw_networkx_edges(G, pos)
nx.draw_networkx_labels(G, pos, labels = node_labels, font_size=4)
plt.show()
In [3]:
import seaborn as sns
data = Firm_copy[Firm_copy['Num_Employ'] > 0]['Num_Employ']
print(data)
sns.displot(data)Out [3]:
0 7393.0
1 171.0
2 113.0
3 24.0
4 242.0
...
165 60.0
166 292.0
167 13371.0
168 5057.0
169 5173.0
Name: Num_Employ, Length: 129, dtype: float64
<seaborn.axisgrid.FacetGrid at 0x2bd702fc820>
In [4]:
import seaborn as sns
data = Firm_copy[Firm_copy['Revenue'] > 0]['Revenue']
print(data)
sns.displot(data)Out [4]:
0 1.089000e+10
1 1.380000e+08
6 1.590000e+08
11 6.750000e+08
13 2.470000e+09
...
162 5.880760e+07
167 4.110000e+10
168 4.519000e+09
169 3.470400e+10
170 1.000000e+08
Name: Revenue, Length: 111, dtype: float64
<seaborn.axisgrid.FacetGrid at 0x2bd17758a00>
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