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='without_exp')
+    parser.add_argument('--exp', type=str, default='with_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)

output_result/risk/count_firm.csv

@@ -1,164 +1,46 @@
 id_firm,count
-70634828,5066
+126,6
-3147511625,5066
+85,5
-653528340,5052
+100,3
-3103797386,5046
+80,3
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output_result/risk/count_firm_prod.csv

@@ -1,359 +1,58 @@
 id_firm,id_product,count
-25685135,99,1566
+126,2.1.3,4
-2944892892,99,1556
+100,1.3.1,3
-3269039233,99,1546
+85,1.3.1,3
-2624175,99,1546
+106,2.1.3,2
-3445928818,95,1476
+68,1.3.1.3,2
-2321109759,95,1466
+73,2.1.3,2
-3299144127,95,1456
+74,2.1.3,2
-930767828,90,726
+75,1.3.3,2
-3407754893,90,716
+85,2.1.1,2
-194210021,50,646
+80,1.3.4,2
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+93,1.3.1,2
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+94,1.1,2
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+108,2.1.3,2
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+99,1.3.1,2
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+53,1.4.5.6,1
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+57,1.3.3.3,1
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+57,2.3.1,1
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+77,1.3.4,1
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+79,2.1.3.2,1
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-2317245827,54,596
+79,2.1.4.1,1
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+50,1.3.1.5,1
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+80,2.1.1,1
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+84,2.3,1
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+45,2.1.4.1.1,1
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+14,1.3.3.4,1
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-653528340,53,596
+148,2.1.3,1
-631449822,54,596
+149,2.1.2.2,1
-2327031723,51,586
+159,2.1.2,1
-2354145351,54,586
+21,1.3.1.3,1
-301209792,51,586
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-2317245827,53,586
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-301209792,53,586
+25,1.3.1.7,1
-653528340,51,586
+26,2.1.3.4,1
-3215814536,51,586
+31,1.3.3.3,1
-3048263744,53,586
+36,1.1.1,1
-888395016,51,586
+41,1.4.5,1
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output_result/risk/count_prod.csv

@@ -1,73 +1,35 @@
 id_product,count
-50,9590
+2.1.3,14
-54,8910
+1.3.1,10
-53,8840
+1.3.4,4
-51,8780
+1.3.3,4
-52,8160
+1.1,3
-55,7860
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-99,6214
+1.3.1.3,3
-95,4398
+2.1.1,3
-46,3872
+2.3,2
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+2.1.3.4,2
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risk_analysis_firm_network.py

@@ -13,6 +13,7 @@ count_dcp = pd.read_csv("output_result/risk/count_dcp.csv",
                             'up_id_firm': str,
                             'down_id_firm': str
                         })
+# print(count_dcp)
 count_dcp = count_dcp[count_dcp['count'] > 35]
 
 list_firm = count_dcp['up_id_firm'].tolist(
@@ -81,18 +82,18 @@ nx.draw(G_firm,
         pos,
         node_size=node_size,
         labels=node_label,
-        font_size=6,
+        font_size=8,
-        width=2,
+        width=3,
         edge_color=colors,
         edge_cmap=cmap,
         edge_vmin=vmin,
         edge_vmax=vmax)
-# nx.draw_networkx_edge_labels(G_firm, pos, font_size=6)
+nx.draw_networkx_edge_labels(G_firm, pos, edge_label, font_size=6)
 sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
 sm._A = []
 position = fig.add_axes([0.95, 0.05, 0.01, 0.3])
 cb = plt.colorbar(sm, fraction=0.01, cax=position)
-cb.ax.tick_params(labelsize=4)
+cb.ax.tick_params(labelsize=10)
 cb.outline.set_visible(False)
 plt.savefig("output_result\\risk\\count_dcp_network")
 plt.close()

risk_analysis_prod_network.py

@@ -11,44 +11,39 @@ print(count_prod)
 print(count_prod.describe())
 
 # prod_networkx
-# BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0)
+BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0)
-# BomNodes.set_index('Code', inplace=True)
+BomNodes.set_index('Code', inplace=True)
-# BomCateNet = pd.read_csv('input_data/input_product_data/BomCateNet.csv', index_col=0)
+BomCateNet = pd.read_csv('input_data/input_product_data/BomCateNet.csv', index_col=0)
-# BomCateNet.fillna(0, inplace=True)
+BomCateNet.fillna(0, inplace=True)
 
-bom_nodes = pd.read_csv('input_data/input_product_data/BomNodes.csv')
+G = nx.from_pandas_adjacency(BomCateNet.T, create_using=nx.MultiDiGraph())
-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_bom.nodes:
+for code in G.nodes:
-    node_attr = bom_nodes.loc[code].to_dict()
+    node_attr = BomNodes.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_bom, labels_dict)
+nx.set_node_attributes(G, labels_dict)
 # print(labels_dict)
 
-pos = nx.nx_agraph.graphviz_layout(g_bom, prog="twopi", args="")
+pos = nx.nx_agraph.graphviz_layout(G, prog="twopi", args="")
-dict_node_name = nx.get_node_attributes(g_bom, 'Name')
+dict_node_name = nx.get_node_attributes(G, 'Name')
 node_labels = {}
-for node in nx.nodes(g_bom):
+for node in nx.nodes(G):
     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_bom, 'node_color').values())
+colors = list(nx.get_node_attributes(G, 'node_color').values())
 vmin = min(colors)
 vmax = max(colors)
 cmap = plt.cm.Blues
 fig = plt.figure(figsize=(10, 10), dpi=300)
-nx.draw(g_bom,
+nx.draw(G,
         pos,
-        node_size=list(nx.get_node_attributes(g_bom, 'node_size').values()),
+        node_size=list(nx.get_node_attributes(G, 'node_size').values()),
         labels=node_labels,
         font_size=6,
         node_color=colors,
@@ -60,7 +55,7 @@ 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=3)
+cb.ax.tick_params(labelsize=8)
 cb.outline.set_visible(False)
 plt.savefig("output_result\\risk\\count_prod_network")
 plt.close()
@@ -88,10 +83,8 @@ list_prod = list(set(list_prod))
 
 # init graph bom
 
-BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv')
+BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0)
-BomNodes.set_index('Index', inplace=True)
+BomNodes.set_index('Code', inplace=True)
-
-BomNodes.loc[13].to_dict()
 
 g_bom = nx.MultiDiGraph()
 g_bom.add_nodes_from(list_prod)
@@ -123,7 +116,7 @@ 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] = str(key) + " " + value
+    temp[key] = key + " " + value
 node_labels = temp
 colors = nx.get_edge_attributes(g_bom, "count")
 colors = [w for (n1, n2, _), w in colors.items()]
@@ -139,7 +132,7 @@ fig = plt.figure(figsize=(6, 10), dpi=300)
 # plt.subplots_adjust(right=0.7)
 nx.draw(g_bom,
         pos_new,
-        node_size=25,
+        node_size=50,
         labels=node_labels,
         font_size=5,
         width=1.5,
@@ -156,7 +149,7 @@ 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=3)
+cb.ax.tick_params(labelsize=8)
 cb.outline.set_visible(False)
 plt.savefig("output_result\\risk\\count_dcp_prod_network")
 plt.close()

risk_analysis_sum_result.py

@@ -1,5 +1,3 @@
-import pickle
-
 from sqlalchemy import text
 from orm import engine, connection
 import pandas as pd
@@ -7,101 +5,107 @@ import networkx as nx
 import json
 import matplotlib.pyplot as plt
 
-# Prepare data
+# prep 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 = pd.read_sql(sql=str_sql,
-result.to_csv('output_result/risk/count.csv', index=False, encoding='utf-8-sig')
+                     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_df = pd.read_sql(
+G_bom_str = pd.read_sql(
-    sql=text(f'select g_bom from iiabmdb.without_exp_experiment where id = {exp_id};'),
+    sql=text(f'select g_bom from iiabmdb.without_exp_experiment '
-    con=connection
+        f'where id = {exp_id};'),
-)
+    con=connection)['g_bom'].tolist()[0]
-
-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=3)
+nx.draw_networkx_labels(G_bom, pos, labels=node_labels, font_size=6)
-plt.savefig(f"output_result/risk/g_bom_exp_id_{exp_id}.png")
+# plt.show()
+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))
 
-with open("firm_network.pkl", 'rb') as f:
+G_firm_str = pd.read_sql(
-    G_firm = pickle.load(f)
+    sql=text(f'select g_firm from iiabmdb.without_exp_sample ' 
-print(f"Successfully loaded cached data from firm_network.pkl")
+             f'where id = {exp_id};'),
+    con=connection)['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="")
+# 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=5)
+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.savefig(f"output_result/risk/g_firm_sample_id_{sample_id}_de.png")
+# plt.show()
+plt.savefig(f"output_result\\risk\\g_firm_sample_id_{exp_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
@@ -110,34 +114,26 @@ 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():
-                    result_dcp_list.append([sid] + up_row.tolist() + down_row.tolist())
+                    row = [sid]
+                    row += up_row.tolist()
+                    row += down_row.tolist()
+                    result_dcp.loc[len(result_dcp.index)] = row
             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']
+    subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'])
-).reset_index(name='count')
+count_dcp.name = 'count'
-
+count_dcp = count_dcp.to_frame().reset_index()
-# 保存文件
+count_dcp.to_csv('output_result\\risk\\count_dcp.csv',
-count_dcp.to_csv('output_result/risk/count_dcp.csv', index=False, encoding='utf-8-sig')
+                 index=False, encoding='utf-8-sig')
-
-# 输出结果
 print(count_dcp)
-