140 lines
5.0 KiB
Python
140 lines
5.0 KiB
Python
from sqlalchemy import text
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from orm import engine, connection
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import pandas as pd
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import networkx as nx
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import json
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import matplotlib.pyplot as plt
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# prep data
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Firm = pd.read_csv("input_data/input_firm_data/Firm_amended.csv")
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Firm['Code'] = Firm['Code'].astype('string')
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Firm.fillna(0, inplace=True)
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BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0)
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with open('SQL_analysis_risk.sql', 'r') as f:
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str_sql = text(f.read())
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result = pd.read_sql(sql=str_sql,
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con=connection)
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result.to_csv('output_result\\risk\\count.csv',
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index=False,
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encoding='utf-8-sig')
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print(result)
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# G bom
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plt.rcParams['font.sans-serif'] = 'SimHei'
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exp_id = 1
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G_bom_str = pd.read_sql(
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sql=text(f'select g_bom from iiabmdb.without_exp_experiment '
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f'where id = {exp_id};'),
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con=connection)['g_bom'].tolist()[0]
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G_bom = nx.adjacency_graph(json.loads(G_bom_str))
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pos = nx.nx_agraph.graphviz_layout(G_bom, prog="twopi", args="")
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node_labels = nx.get_node_attributes(G_bom, 'Name')
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# rename node 1
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# node_labels['1'] = '工业互联网'
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# node_labels['1.1'] = '工业自动化硬件'
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# node_labels['1.4'] = '工业互联网安全管理'
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# node_labels['1.2.1'] = '网络互联服务'
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# node_labels['1.2.2'] = '标识解析服务'
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# node_labels['1.2.3'] = '数据互通服务'
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# node_labels['1.3.1'] = '设计研发软件'
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# node_labels['1.3.2'] = '采购供应软件'
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# node_labels['1.3.3'] = '生产制造软件'
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# node_labels['1.3.4'] = '企业运营软件'
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# node_labels['1.3.5'] = '仓储物流软件'
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plt.figure(figsize=(12, 12), dpi=300)
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nx.draw_networkx_nodes(G_bom, pos)
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nx.draw_networkx_edges(G_bom, pos)
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nx.draw_networkx_labels(G_bom, pos, labels=node_labels, font_size=6)
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# plt.show()
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plt.savefig(f"output_result\\risk\\g_bom_exp_id_{exp_id}.png")
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plt.close()
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# G firm
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plt.rcParams['font.sans-serif'] = 'SimHei'
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sample_id = 1
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G_firm_str = pd.read_sql(
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sql=text(f'select g_firm from iiabmdb.without_exp_sample '
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f'where id = {exp_id};'),
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con=connection)['g_firm'].tolist()[0]
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G_firm = nx.adjacency_graph(json.loads(G_firm_str))
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pos = nx.nx_agraph.graphviz_layout(G_firm, prog="twopi", args="")
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# desensitize
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node_label = nx.get_node_attributes(G_firm, 'Revenue_Log')
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node_label = {
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key: key
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for key in node_label.keys()
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}
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node_size = list(nx.get_node_attributes(G_firm, '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(G_firm, "Product")
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edge_label = {(n1, n2): label 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(G_firm, pos, node_size=node_size, labels=node_label, font_size=6)
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nx.draw_networkx_edge_labels(G_firm, pos, edge_label, font_size=4)
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# plt.show()
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plt.savefig(f"output_result\\risk\\g_firm_sample_id_{exp_id}_de.png")
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plt.close()
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# count firm product
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count_firm_prod = result.value_counts(subset=['id_firm', 'id_product'])
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count_firm_prod.name = 'count'
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count_firm_prod = count_firm_prod.to_frame().reset_index()
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count_firm_prod.to_csv('output_result\\risk\\count_firm_prod.csv',
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index=False,
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encoding='utf-8-sig')
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print(count_firm_prod)
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# count firm
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count_firm = count_firm_prod.groupby('id_firm')['count'].sum()
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count_firm = count_firm.to_frame().reset_index()
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count_firm.sort_values('count', inplace=True, ascending=False)
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count_firm.to_csv('output_result\\risk\\count_firm.csv',
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index=False,
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encoding='utf-8-sig')
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print(count_firm)
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# count product
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count_prod = count_firm_prod.groupby('id_product')['count'].sum()
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count_prod = count_prod.to_frame().reset_index()
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count_prod.sort_values('count', inplace=True, ascending=False)
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count_prod.to_csv('output_result\\risk\\count_prod.csv',
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index=False,
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encoding='utf-8-sig')
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print(count_prod)
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# DCP disruption causing probability
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result_disrupt_ts_above_0 = result[result['ts'] > 0]
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print(result_disrupt_ts_above_0)
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result_dcp = pd.DataFrame(columns=[
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's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'
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])
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for sid, group in result.groupby('s_id'):
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ts_start = max(group['ts'])
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while ts_start >= 1:
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ts_end = ts_start - 1
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while ts_end >= 0:
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up = group.loc[group['ts'] == ts_end, ['id_firm', 'id_product']]
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down = group.loc[group['ts'] == ts_start,
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['id_firm', 'id_product']]
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for _, up_row in up.iterrows():
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for _, down_row in down.iterrows():
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row = [sid]
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row += up_row.tolist()
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row += down_row.tolist()
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result_dcp.loc[len(result_dcp.index)] = row
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ts_end -= 1
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ts_start -= 1
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count_dcp = result_dcp.value_counts(
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subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'])
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count_dcp.name = 'count'
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count_dcp = count_dcp.to_frame().reset_index()
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count_dcp.to_csv('output_result\\risk\\count_dcp.csv',
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index=False, encoding='utf-8-sig')
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print(count_dcp)
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