from orm import engine import pandas as pd import networkx as nx import json import matplotlib.pyplot as plt # prep data Firm = pd.read_csv("Firm_amended.csv") Firm['Code'] = Firm['Code'].astype('string') Firm.fillna(0, inplace=True) BomNodes = pd.read_csv('BomNodes.csv', index_col=0) with open('SQL_analysis_risk.sql', 'r') as f: str_sql = f.read() result = pd.read_sql(sql=str_sql, con=engine) result.to_csv('analysis\\count.csv', index=False, encoding='utf-8-sig') print(result) # G bom plt.rcParams['font.sans-serif'] = 'SimHei' exp_id = 1 G_bom_str = pd.read_sql( sql=f'select g_bom from iiabmdb.without_exp_experiment ' f'where id = {exp_id};', con=engine)['g_bom'].tolist()[0] G_bom = nx.adjacency_graph(json.loads(G_bom_str)) pos = nx.nx_agraph.graphviz_layout(G_bom, prog="twopi", args="") node_labels = nx.get_node_attributes(G_bom, 'Name') plt.figure(figsize=(12, 12), dpi=300) nx.draw_networkx_nodes(G_bom, pos) nx.draw_networkx_edges(G_bom, pos) nx.draw_networkx_labels(G_bom, pos, labels=node_labels, font_size=6) # plt.show() plt.savefig(f"analysis\\g_bom_exp_id_{exp_id}.png") plt.close() # G firm plt.rcParams['font.sans-serif'] = 'SimHei' sample_id = 1 G_firm_str = pd.read_sql( sql=f'select g_firm from iiabmdb.without_exp_sample where id = {exp_id};', con=engine)['g_firm'].tolist()[0] G_firm = nx.adjacency_graph(json.loads(G_firm_str)) pos = nx.nx_agraph.graphviz_layout(G_firm, prog="twopi", args="") node_label = nx.get_node_attributes(G_firm, 'Name') # desensitize node_label = { key: key for key in node_label.keys() } node_size = list(nx.get_node_attributes(G_firm, 'Revenue_Log').values()) node_size = list(map(lambda x: x**2, node_size)) edge_label = nx.get_edge_attributes(G_firm, "Product") edge_label = {(n1, n2): label for (n1, n2, _), label in edge_label.items()} plt.figure(figsize=(12, 12), dpi=300) nx.draw(G_firm, pos, node_size=node_size, labels=node_label, font_size=6) nx.draw_networkx_edge_labels(G_firm, pos, edge_label, font_size=4) # plt.show() plt.savefig(f"analysis\\g_firm_sample_id_{exp_id}_de.png") plt.close() # count firm product count_firm_prod = result.value_counts(subset=['id_firm', 'id_product']) count_firm_prod.name = 'count' count_firm_prod = count_firm_prod.to_frame().reset_index() count_firm_prod = pd.merge(count_firm_prod, Firm[['Code', 'Name']], how='left', left_on='id_firm', right_on='Code') count_firm_prod.drop('Code', axis=1, inplace=True) count_firm_prod.rename(columns={'Name': 'name_firm'}, inplace=True) count_firm_prod = pd.merge(count_firm_prod, BomNodes[['Code', 'Name']], how='left', left_on='id_product', right_on='Code') count_firm_prod.drop('Code', axis=1, inplace=True) count_firm_prod.rename(columns={'Name': 'name_product'}, inplace=True) count_firm_prod = count_firm_prod[[ 'id_firm', 'name_firm', 'id_product', 'name_product', 'count' ]] count_firm_prod.to_csv('analysis\\count_firm_prod.csv', index=False, encoding='utf-8-sig') print(count_firm_prod) # count firm count_firm = count_firm_prod.groupby('id_firm')['count'].sum() count_firm = count_firm.to_frame().reset_index() count_firm = pd.merge(count_firm, Firm[['Code', 'Name']], how='left', left_on='id_firm', right_on='Code') count_firm.drop('Code', axis=1, inplace=True) count_firm.sort_values('count', inplace=True, ascending=False) count_firm = count_firm[['id_firm', 'Name', 'count']] count_firm.to_csv('analysis\\count_firm.csv', index=False, encoding='utf-8-sig') print(count_firm) # count product count_prod = count_firm_prod.groupby('id_product')['count'].sum() count_prod = count_prod.to_frame().reset_index() count_prod = pd.merge(count_prod, BomNodes[['Code', 'Name']], how='left', left_on='id_product', right_on='Code') count_prod.drop('Code', axis=1, inplace=True) count_prod.sort_values('count', inplace=True, ascending=False) count_prod = count_prod[['id_product', 'Name', 'count']] count_prod.to_csv('analysis\\count_prod.csv', index=False, encoding='utf-8-sig') print(count_prod) # DCP disruption causing probability result_disrupt_ts_above_0 = result[result['ts'] > 0] print(result_disrupt_ts_above_0) result_dcp = pd.DataFrame(columns=[ 's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product' ]) for sid, group in result.groupby('s_id'): ts_start = max(group['ts']) while ts_start >= 1: ts_end = ts_start - 1 while ts_end >= 0: up = group.loc[group['ts'] == ts_end, ['id_firm', 'id_product']] down = group.loc[group['ts'] == ts_start, ['id_firm', 'id_product']] for _, up_row in up.iterrows(): for _, down_row in down.iterrows(): row = [sid] row += up_row.tolist() row += down_row.tolist() result_dcp.loc[len(result_dcp.index)] = row ts_end -= 1 ts_start -= 1 count_dcp = result_dcp.value_counts( subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product']) count_dcp.name = 'count' count_dcp = count_dcp.to_frame().reset_index() count_dcp = pd.merge(count_dcp, Firm[['Code', 'Name']], how='left', left_on='up_id_firm', right_on='Code') count_dcp.drop('Code', axis=1, inplace=True) count_dcp.rename(columns={'Name': 'up_name_firm'}, inplace=True) count_dcp = pd.merge(count_dcp, BomNodes[['Code', 'Name']], how='left', left_on='up_id_product', right_on='Code') count_dcp.drop('Code', axis=1, inplace=True) count_dcp.rename(columns={'Name': 'up_name_product'}, inplace=True) count_dcp = pd.merge(count_dcp, Firm[['Code', 'Name']], how='left', left_on='down_id_firm', right_on='Code') count_dcp.drop('Code', axis=1, inplace=True) count_dcp.rename(columns={'Name': 'down_name_firm'}, inplace=True) count_dcp = pd.merge(count_dcp, BomNodes[['Code', 'Name']], how='left', left_on='down_id_product', right_on='Code') count_dcp.drop('Code', axis=1, inplace=True) count_dcp.rename(columns={'Name': 'down_name_product'}, inplace=True) count_dcp = count_dcp[[ 'up_id_firm', 'up_name_firm', 'up_id_product', 'up_name_product', 'down_id_firm', 'down_name_firm', 'down_id_product', 'down_name_product', 'count' ]] count_dcp.to_csv('analysis\\count_dcp.csv', index=False, encoding='utf-8-sig') print(count_dcp)