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{
"cells": [
{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Revenue_Log R-squared: 0.586\n",
"Model: OLS Adj. R-squared: 0.580\n",
"Method: Least Squares F-statistic: 94.86\n",
"Date: Sat, 01 Apr 2023 Prob (F-statistic): 1.85e-14\n",
"Time: 18:07:54 Log-Likelihood: -128.58\n",
"No. Observations: 69 AIC: 261.2\n",
"Df Residuals: 67 BIC: 265.6\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"----------------------------------------------------------------------------------\n",
"Intercept 14.3160 0.806 17.757 0.000 12.707 15.925\n",
"Num_Employ_Log 0.9728 0.100 9.740 0.000 0.773 1.172\n",
"==============================================================================\n",
"Omnibus: 55.594 Durbin-Watson: 1.958\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 244.671\n",
"Skew: 2.451 Prob(JB): 7.42e-54\n",
"Kurtosis: 10.815 Cond. No. 34.6\n",
"==============================================================================\n",
"\n",
"Notes:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"['1', '1.1', '1.2', '1.3', '1.4', '2', '1.1.1', '1.1.2', '1.1.3', '1.2.1', '1.2.2', '1.2.3', '1.3.1', '1.3.2', '1.3.3', '1.3.4', '1.3.5', '1.4.1', '1.4.2', '1.4.3', '1.4.4', '1.4.5', '2.1', '2.2', '2.3', '1.3.1.1', '1.3.1.2', '1.3.1.3', '1.3.1.4', '1.3.1.5', '1.3.1.6', '1.3.1.7', '1.3.2.1', '1.3.3.1', '1.3.3.2', '1.3.3.3', '1.3.3.4', '1.3.3.5', '1.3.3.6', '1.3.3.7', '1.3.4.1', '1.3.4.2', '1.3.4.3', '1.3.5.1', '1.4.1.1', '1.4.1.2', '1.4.1.3', '1.4.1.4', '1.4.1.5', '1.4.2.1', '1.4.2.2', '1.4.2.3', '1.4.2.4', '1.4.2.5', '1.4.2.6', '1.4.2.7', '1.4.3.1', '1.4.3.2', '1.4.3.3', '1.4.3.4', '1.4.3.5', '1.4.3.6', '1.4.4.1', '1.4.4.2', '1.4.4.3', '1.4.4.4', '1.4.4.5', '1.4.5.1', '1.4.5.2', '1.4.5.3', '1.4.5.4', '1.4.5.5', '1.4.5.6', '1.4.5.7', '1.4.5.8', '1.4.5.9', '2.1.1', '2.1.2', '2.1.3', '2.1.4', '2.3.1', '2.3.2', '2.3.3', '2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5', '2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4', '2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7', '2.1.4.1', '2.1.4.2', '2.1.4.1.1', '2.1.4.1.2', '2.1.4.1.3', '2.1.4.1.4', '2.1.4.2.1', '2.1.4.2.2']\n"
]
},
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{
"name": "stderr",
"output_type": "stream",
"text": [
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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()`\n",
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" Firm_copy.insert(Firm_copy.columns.get_loc('Revenue'),'Revenue_Log', series_Revenue_Log)\n",
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"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()`\n",
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" Firm_copy.insert(Firm_copy.columns.get_loc('Num_Employ'),'Num_Employ_Log', series_Num_Employ_Log)\n"
]
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},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
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}
],
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"source": [
"import pandas as pd\n",
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"import numpy as np\n",
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"import math\n",
"import statsmodels.formula.api as smf\n",
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"from sklearn.preprocessing import MinMaxScaler\n",
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"import networkx as nx\n",
"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
"plt.rcParams['font.sans-serif'] = 'SimHei'\n",
"\n",
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"# init graph bom\n",
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"BomNodes = pd.read_csv('BomNodes.csv', index_col=0)\n",
"BomNodes.set_index('Code', inplace=True)\n",
"BomCateNet = pd.read_csv('BomCateNet.csv', index_col=0)\n",
"BomCateNet.fillna(0, inplace=True)\n",
"\n",
"G = nx.from_pandas_adjacency(BomCateNet, create_using=nx.MultiDiGraph())\n",
"\n",
"labels_dict = {}\n",
"for code in G.nodes:\n",
" labels_dict[code] = BomNodes.loc[code].to_dict()\n",
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"nx.set_node_attributes(G, labels_dict)\n",
"\n",
"# load firm\n",
"Firm = pd.read_csv(\"Firm.csv\")\n",
"Firm_copy = Firm.copy()\n",
"\n",
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"# add attribute, number of firm, to graph bom\n",
"firm_num_dict = {}\n",
"for node in nx.nodes(G):\n",
" firm_num_dict[node]= sum(Firm_copy[node]==1)\n",
"nx.set_node_attributes(G, firm_num_dict, name=\"Num_Firm\")\n",
"\n",
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"# get log revenue\n",
"Firm_copy['Revenue_Log'] = Firm_copy['Revenue'].map(math.log)\n",
"Firm_copy['Num_Employ_Log'] = Firm_copy['Num_Employ'].map(math.log)\n",
"data_ols = Firm_copy[Firm_copy[['Revenue_Log', 'Num_Employ_Log']].notnull().all(axis=1)][['Revenue_Log', 'Num_Employ_Log']]\n",
"\n",
"ols_model = smf.ols('Revenue_Log ~ Num_Employ_Log', data=data_ols)\n",
"ols_results = ols_model.fit()\n",
"b = ols_results.params.Intercept\n",
"a = ols_results.params.Num_Employ_Log\n",
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"print(ols_results.summary())\n",
"sns.regplot(x='Num_Employ_Log',y='Revenue_Log',data=data_ols[data_ols.notnull().all(axis=1)])\n",
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"\n",
"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)\n",
"series_Revenue_Log = Firm_copy.pop('Revenue_Log')\n",
"Firm_copy.insert(Firm_copy.columns.get_loc('Revenue'),'Revenue_Log', series_Revenue_Log)\n",
"series_Num_Employ_Log = Firm_copy.pop('Num_Employ_Log')\n",
"Firm_copy.insert(Firm_copy.columns.get_loc('Num_Employ'),'Num_Employ_Log', series_Num_Employ_Log)\n",
"\n",
"# lift firm from neighboring tier\n",
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"node_list = list(nx.bfs_tree(G, '1'))\n",
"node_list.reverse()\n",
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"\n",
"# init criterion for ending the lifting process\n",
"node_check = [k for k, v in nx.get_node_attributes(G, 'Num_Firm').items() if v==0]\n",
"if '1' in node_check:\n",
" node_check.remove('1')\n",
"for node in node_check:\n",
" if G.out_degree(node) == 0:\n",
" node_check.remove(node)\n",
" print(f\"Warning: node, {node}, in tier 0 does not contain firm\")\n",
"\n",
"while node_check:\n",
" for node in node_list[:-1]:\n",
" if G.out_degree(node) > 0 and sum(Firm_copy[node]==1) == 0:\n",
" list_neighbors = list(G.neighbors(node))\n",
" firm_list = Firm_copy.index[(Firm_copy[list_neighbors]==1).all(axis=1)].to_list()\n",
" if firm_list: # there exist firm that produces all components\n",
" # lift firm with size above average in firm_list\n",
" average_size = Firm_copy.loc[firm_list, 'Revenue_Log'].mean()\n",
" firm_selected_list = Firm_copy.loc[firm_list].loc[Firm_copy.loc[firm_list, 'Revenue_Log'] >= average_size].index.to_list()\n",
" Firm_copy.loc[firm_selected_list, node] = 1\n",
" Firm_copy.loc[firm_selected_list, list_neighbors] = np.nan\n",
" else: # select top 15% firm in terms of size\n",
" firm_list = Firm_copy.index[(Firm_copy[list_neighbors]==1).any(axis=1)].to_list()\n",
" firm_df = Firm_copy.loc[firm_list].sort_values('Revenue_Log', ascending=False)\n",
" num_firm_selected = round(firm_df.shape[0] * 0.15)\n",
" firm_selected_list = firm_df.index[0: num_firm_selected].to_list()\n",
" Firm_copy.loc[firm_selected_list, node] = 1\n",
" Firm_copy.loc[firm_selected_list, list_neighbors] = np.nan\n",
"\n",
" # update attribute, number of firm\n",
" for node in nx.nodes(G):\n",
" firm_num_dict[node]= sum(Firm_copy[node]==1)\n",
" nx.set_node_attributes(G, firm_num_dict, name=\"Num_Firm\")\n",
"\n",
" # update criterion for ending the lifting process\n",
" node_check = [k for k, v in nx.get_node_attributes(G, 'Num_Firm').items() if v==0]\n",
" if '1' in node_check:\n",
" node_check.remove('1')\n",
" for node in node_check:\n",
" if G.out_degree(node) == 0:\n",
" node_check.remove(node)\n",
" print(f\"Warning: node, {node}, in tier 0 does not contain firm\")\n",
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"\n",
"# output\n",
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"# Firm_copy.to_csv('Firm_amended.csv', index=False, encoding='utf-8-sig')"
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]
},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 3600x3600 with 1 Axes>"
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]
},
"metadata": {},
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"output_type": "display_data"
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}
],
"source": [
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"# visualization\n",
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"pos = nx.nx_agraph.graphviz_layout(G, prog=\"twopi\", args=\"\")\n",
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"dict_num_firm = nx.get_node_attributes(G, 'Num_Firm')\n",
"dict_node_name = nx.get_node_attributes(G, 'Name')\n",
"node_labels = {}\n",
"for node in nx.nodes(G):\n",
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" node_labels[node] = f\"{node} {str(dict_node_name[node])} {str(dict_num_firm[node])}\"\n",
" # node_labels[node] = f\"{str(dict_num_firm[node])}\"\n",
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"plt.figure(figsize=(12, 12), dpi=300)\n",
"nx.draw_networkx_nodes(G, pos)\n",
"nx.draw_networkx_edges(G, pos)\n",
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"nx.draw_networkx_labels(G, pos, labels = node_labels, font_size=4)\n",
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"plt.show()"
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]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
"outputs": [
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{
"name": "stdout",
"output_type": "stream",
"text": [
"0 7393.0\n",
"1 171.0\n",
"2 113.0\n",
"3 24.0\n",
"4 242.0\n",
" ... \n",
"165 60.0\n",
"166 292.0\n",
"167 13371.0\n",
"168 5057.0\n",
"169 5173.0\n",
"Name: Num_Employ, Length: 129, dtype: float64\n"
]
},
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{
"data": {
"text/plain": [
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"<seaborn.axisgrid.FacetGrid at 0x2bd702fc820>"
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]
},
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"execution_count": 3,
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"metadata": {},
"output_type": "execute_result"
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},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 500x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
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}
],
"source": [
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"import seaborn as sns\n",
"data = Firm_copy[Firm_copy['Num_Employ'] > 0]['Num_Employ']\n",
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"print(data)\n",
"sns.displot(data)"
]
},
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 1.089000e+10\n",
"1 1.380000e+08\n",
"6 1.590000e+08\n",
"11 6.750000e+08\n",
"13 2.470000e+09\n",
" ... \n",
"162 5.880760e+07\n",
"167 4.110000e+10\n",
"168 4.519000e+09\n",
"169 3.470400e+10\n",
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"170 1.000000e+08\n",
"Name: Revenue, Length: 111, dtype: float64\n"
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]
},
{
"data": {
"text/plain": [
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"<seaborn.axisgrid.FacetGrid at 0x2bd17758a00>"
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]
},
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"execution_count": 4,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 500x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"data = Firm_copy[Firm_copy['Revenue'] > 0]['Revenue']\n",
"print(data)\n",
"sns.displot(data)"
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"version": "3.9.13"
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},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
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"hash": "a90aeebcf29d64a654773811cc170cb25061cb2498f10ac689db374c7bf325de"
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}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
