From 0da29d58d69c142034d25f4c0dba249180caa78d Mon Sep 17 00:00:00 2001 From: Cricial <2911646453@qq.com> Date: Mon, 3 Feb 2025 22:23:18 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E5=9B=BE=E5=BD=A2=E4=BB=A3?= =?UTF-8?q?=E7=A0=81=E6=BC=8F=E6=B4=9E?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- main.py | 2 +- output_result/risk/count_dcp.csv | 55133 +++++++++++++++- output_result/risk/count_dcp_prod.csv | 1126 +- output_result/risk/count_dcp_prod_network.png | Bin 0 -> 311245 bytes output_result/risk/count_firm.csv | 208 +- output_result/risk/count_firm_prod.csv | 415 +- output_result/risk/count_prod.csv | 106 +- output_result/risk/count_prod_network.png | Bin 1127658 -> 1987416 bytes output_result/risk/g_bom_exp_id_1.png | Bin 2133743 -> 1925585 bytes output_result/risk/g_firm_sample_id_1_de.png | Bin 3205437 -> 2286658 bytes risk_analysis_firm_network.py | 3 +- risk_analysis_prod_network.py | 47 +- risk_analysis_sum_result.py | 132 +- 13 files changed, 56882 insertions(+), 290 deletions(-) create mode 100644 output_result/risk/count_dcp_prod_network.png diff --git a/main.py b/main.py index 95fa88d..4a82ecb 100644 --- a/main.py +++ b/main.py @@ -54,7 +54,7 @@ def do_computation(c_db): if __name__ == '__main__': # 输入参数 parser = argparse.ArgumentParser(description='setting') - parser.add_argument('--exp', type=str, default='with_exp') + parser.add_argument('--exp', type=str, default='without_exp') parser.add_argument('--job', type=int, default='4') parser.add_argument('--reset_sample', type=int, default='0') parser.add_argument('--reset_db', type=bool, default=False) diff --git a/output_result/risk/count_dcp.csv b/output_result/risk/count_dcp.csv index a0f222c..dd41b06 100644 --- a/output_result/risk/count_dcp.csv +++ b/output_result/risk/count_dcp.csv @@ -1,46 +1,55089 @@ up_id_firm,up_id_product,down_id_firm,down_id_product,count -115,1.1.3,94,1.1,1 -45,1.3.4.2,80,1.3.4,1 -49,1.3.1.4,99,1.3.1,1 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font_size=8, + font_size=6, width=3, edge_color=colors, edge_cmap=cmap, diff --git a/risk_analysis_prod_network.py b/risk_analysis_prod_network.py index 073c5a0..64dcce7 100644 --- a/risk_analysis_prod_network.py +++ b/risk_analysis_prod_network.py @@ -11,39 +11,44 @@ print(count_prod) print(count_prod.describe()) # prod_networkx -BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0) -BomNodes.set_index('Code', inplace=True) -BomCateNet = pd.read_csv('input_data/input_product_data/BomCateNet.csv', index_col=0) -BomCateNet.fillna(0, inplace=True) +# BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0) +# BomNodes.set_index('Code', inplace=True) +# BomCateNet = pd.read_csv('input_data/input_product_data/BomCateNet.csv', index_col=0) +# BomCateNet.fillna(0, inplace=True) -G = nx.from_pandas_adjacency(BomCateNet.T, create_using=nx.MultiDiGraph()) +bom_nodes = pd.read_csv('input_data/input_product_data/BomNodes.csv') +bom_nodes['Code'] = bom_nodes['Code'].astype(str) +bom_nodes.set_index('Index', inplace=True) + +bom_cate_net = pd.read_csv('input_data/input_product_data/合成结点.csv') +g_bom = nx.from_pandas_edgelist(bom_cate_net, source='UPID', target='ID', create_using=nx.MultiDiGraph()) labels_dict = {} -for code in G.nodes: - node_attr = BomNodes.loc[code].to_dict() +for code in g_bom.nodes: + node_attr = bom_nodes.loc[code].to_dict() index_list = count_prod[count_prod['id_product'] == code].index.tolist() index = index_list[0] if len(index_list) == 1 else -1 node_attr['count'] = count_prod['count'].get(index, 0) node_attr['node_size'] = count_prod['count'].get(index, 0) node_attr['node_color'] = count_prod['count'].get(index, 0) labels_dict[code] = node_attr -nx.set_node_attributes(G, labels_dict) +nx.set_node_attributes(g_bom, labels_dict) # print(labels_dict) -pos = nx.nx_agraph.graphviz_layout(G, prog="twopi", args="") -dict_node_name = nx.get_node_attributes(G, 'Name') +pos = nx.nx_agraph.graphviz_layout(g_bom, prog="twopi", args="") +dict_node_name = nx.get_node_attributes(g_bom, 'Name') node_labels = {} -for node in nx.nodes(G): +for node in nx.nodes(g_bom): node_labels[node] = f"{node} {str(dict_node_name[node])}" # node_labels[node] = f"{str(dict_node_name[node])}" -colors = list(nx.get_node_attributes(G, 'node_color').values()) +colors = list(nx.get_node_attributes(g_bom, 'node_color').values()) vmin = min(colors) vmax = max(colors) cmap = plt.cm.Blues fig = plt.figure(figsize=(10, 10), dpi=300) -nx.draw(G, +nx.draw(g_bom, pos, - node_size=list(nx.get_node_attributes(G, 'node_size').values()), + node_size=list(nx.get_node_attributes(g_bom, 'node_size').values()), labels=node_labels, font_size=6, node_color=colors, @@ -55,7 +60,7 @@ sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax)) sm._A = [] position = fig.add_axes([0.01, 0.05, 0.01, 0.3]) cb = plt.colorbar(sm, fraction=0.01, cax=position) -cb.ax.tick_params(labelsize=8) +cb.ax.tick_params(labelsize=3) cb.outline.set_visible(False) plt.savefig("output_result\\risk\\count_prod_network") plt.close() @@ -83,8 +88,10 @@ list_prod = list(set(list_prod)) # init graph bom -BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0) -BomNodes.set_index('Code', inplace=True) +BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv') +BomNodes.set_index('Index', inplace=True) + +BomNodes.loc[13].to_dict() g_bom = nx.MultiDiGraph() g_bom.add_nodes_from(list_prod) @@ -116,7 +123,7 @@ node_labels = nx.get_node_attributes(g_bom, 'Name') # node_labels['1'] = '解决方案' temp = {} for key, value in node_labels.items(): - temp[key] = key + " " + value + temp[key] = str(key) + " " + value node_labels = temp colors = nx.get_edge_attributes(g_bom, "count") colors = [w for (n1, n2, _), w in colors.items()] @@ -132,7 +139,7 @@ fig = plt.figure(figsize=(6, 10), dpi=300) # plt.subplots_adjust(right=0.7) nx.draw(g_bom, pos_new, - node_size=50, + node_size=25, labels=node_labels, font_size=5, width=1.5, @@ -149,7 +156,7 @@ sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax)) sm._A = [] position = fig.add_axes([0.75, 0.1, 0.01, 0.2]) cb = plt.colorbar(sm, fraction=0.01, cax=position) -cb.ax.tick_params(labelsize=8) +cb.ax.tick_params(labelsize=3) cb.outline.set_visible(False) plt.savefig("output_result\\risk\\count_dcp_prod_network") plt.close() diff --git a/risk_analysis_sum_result.py b/risk_analysis_sum_result.py index c290b4d..1b4bcc1 100644 --- a/risk_analysis_sum_result.py +++ b/risk_analysis_sum_result.py @@ -1,3 +1,5 @@ +import pickle + from sqlalchemy import text from orm import engine, connection import pandas as pd @@ -5,107 +7,101 @@ import networkx as nx import json import matplotlib.pyplot as plt -# prep data +# Prepare data Firm = pd.read_csv("input_data/input_firm_data/Firm_amended.csv") Firm['Code'] = Firm['Code'].astype('string') Firm.fillna(0, inplace=True) BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0) +# SQL query with open('SQL_analysis_risk.sql', 'r') as f: str_sql = text(f.read()) -result = pd.read_sql(sql=str_sql, - con=connection) -result.to_csv('output_result\\risk\\count.csv', - index=False, - encoding='utf-8-sig') +result = pd.read_sql(sql=str_sql, con=connection) +result.to_csv('output_result/risk/count.csv', index=False, encoding='utf-8-sig') print(result) -# G bom +# G_bom plt.rcParams['font.sans-serif'] = 'SimHei' exp_id = 1 -G_bom_str = pd.read_sql( - sql=text(f'select g_bom from iiabmdb.without_exp_experiment ' - f'where id = {exp_id};'), - con=connection)['g_bom'].tolist()[0] +G_bom_df = pd.read_sql( + sql=text(f'select g_bom from iiabmdb.without_exp_experiment where id = {exp_id};'), + con=connection +) + +if G_bom_df.empty: + raise ValueError(f"No g_bom found for exp_id = {exp_id}") + +G_bom_str = G_bom_df['g_bom'].tolist()[0] +if G_bom_str is None: + raise ValueError(f"g_bom data is None for exp_id = {exp_id}") + 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') -# rename node 1 -# node_labels['1'] = '工业互联网' -# node_labels['1.1'] = '工业自动化硬件' -# node_labels['1.4'] = '工业互联网安全管理' -# node_labels['1.2.1'] = '网络互联服务' -# node_labels['1.2.2'] = '标识解析服务' -# node_labels['1.2.3'] = '数据互通服务' -# node_labels['1.3.1'] = '设计研发软件' -# node_labels['1.3.2'] = '采购供应软件' -# node_labels['1.3.3'] = '生产制造软件' -# node_labels['1.3.4'] = '企业运营软件' -# node_labels['1.3.5'] = '仓储物流软件' + 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"output_result\\risk\\g_bom_exp_id_{exp_id}.png") +nx.draw_networkx_labels(G_bom, pos, labels=node_labels, font_size=3) +plt.savefig(f"output_result/risk/g_bom_exp_id_{exp_id}.png") plt.close() -# G firm +# G_firm plt.rcParams['font.sans-serif'] = 'SimHei' sample_id = 1 +# G_firm_df = pd.read_sql( +# sql=text(f'select g_firm from iiabmdb.without_exp_sample where id = {sample_id};'), +# con=connection +# ) +# +# if G_firm_df.empty: +# raise ValueError(f"No g_firm found for sample_id = {sample_id}") +# +# G_firm_str = G_firm_df['g_firm'].tolist()[0] +# if G_firm_str is None: +# raise ValueError(f"g_firm data is None for sample_id = {sample_id}") +# +# G_firm = nx.adjacency_graph(json.loads(G_firm_str)) -G_firm_str = pd.read_sql( - sql=text(f'select g_firm from iiabmdb.without_exp_sample ' - f'where id = {exp_id};'), - con=connection)['g_firm'].tolist()[0] +with open("firm_network.pkl", 'rb') as f: + G_firm = pickle.load(f) +print(f"Successfully loaded cached data from firm_network.pkl") -G_firm = nx.adjacency_graph(json.loads(G_firm_str)) pos = nx.nx_agraph.graphviz_layout(G_firm, prog="twopi", args="") -# desensitize node_label = nx.get_node_attributes(G_firm, 'Revenue_Log') -node_label = { - key: key - for key in node_label.keys() -} +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(G_firm, pos, node_size=node_size, labels=node_label, font_size=5) nx.draw_networkx_edge_labels(G_firm, pos, edge_label, font_size=4) -# plt.show() -plt.savefig(f"output_result\\risk\\g_firm_sample_id_{exp_id}_de.png") +plt.savefig(f"output_result/risk/g_firm_sample_id_{sample_id}_de.png") plt.close() -# count firm product +# 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.to_csv('output_result\\risk\\count_firm_prod.csv', - index=False, - encoding='utf-8-sig') +count_firm_prod.to_csv('output_result/risk/count_firm_prod.csv', index=False, encoding='utf-8-sig') print(count_firm_prod) -# count firm +# Count firm count_firm = count_firm_prod.groupby('id_firm')['count'].sum() count_firm = count_firm.to_frame().reset_index() count_firm.sort_values('count', inplace=True, ascending=False) -count_firm.to_csv('output_result\\risk\\count_firm.csv', - index=False, - encoding='utf-8-sig') +count_firm.to_csv('output_result/risk/count_firm.csv', index=False, encoding='utf-8-sig') print(count_firm) -# count product +# Count product count_prod = count_firm_prod.groupby('id_product')['count'].sum() count_prod = count_prod.to_frame().reset_index() count_prod.sort_values('count', inplace=True, ascending=False) -count_prod.to_csv('output_result\\risk\\count_prod.csv', - index=False, - encoding='utf-8-sig') +count_prod.to_csv('output_result/risk/count_prod.csv', index=False, encoding='utf-8-sig') print(count_prod) # DCP disruption causing probability @@ -114,26 +110,34 @@ 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' ]) + +result_dcp_list = [] # 用列表收集数据，避免DataFrame逐行增长的问题 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']] + 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 + result_dcp_list.append([sid] + up_row.tolist() + down_row.tolist()) ts_end -= 1 ts_start -= 1 + +# 转换为DataFrame +result_dcp = pd.DataFrame(result_dcp_list, columns=[ + 's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product' +]) + +# 统计 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.to_csv('output_result\\risk\\count_dcp.csv', - index=False, encoding='utf-8-sig') + subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'] +).reset_index(name='count') + +# 保存文件 +count_dcp.to_csv('output_result/risk/count_dcp.csv', index=False, encoding='utf-8-sig') + +# 输出结果 print(count_dcp) +

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