before proactive

.vscode/launch.json

@@ -12,8 +12,8 @@
             "console": "integratedTerminal",
             "justMyCode": true,
             "args": [
-                "--exp", "with_exp",
+                "--exp", "test",
-                "--job", "24",
+                "--reset_db", "True"
             ]
         }
     ]

analysis_firm_network.py

@@ -1,7 +1,6 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 import networkx as nx
-import math
 
 plt.rcParams['font.sans-serif'] = 'SimHei'
 
@@ -65,9 +64,11 @@ for _, row in count_dcp.iterrows():
 pos = nx.nx_agraph.graphviz_layout(G_firm, prog="dot", args="")
 node_label = nx.get_node_attributes(G_firm, 'Name')
 # node_degree = dict(G_firm.out_degree())
+# desensitize
 node_label = {
     # key: f"{node_label[key]} {node_degree[key]}"
-    key: f"{node_label[key]}"
+    # key: f"{node_label[key]}"
+    key: key
     for key in node_label.keys()
 }
 node_size = list(nx.get_node_attributes(G_firm, 'Revenue_Log').values())
@@ -87,7 +88,7 @@ nx.draw(G_firm,
         node_size=node_size,
         labels=node_label,
         font_size=6,
-        width = 3,
+        width=3,
         edge_color=colors,
         edge_cmap=cmap,
         edge_vmin=vmin,
@@ -95,7 +96,7 @@ nx.draw(G_firm,
 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([0.9, 0.05, 0.01, 0.3])
+position = fig.add_axes([0.9, 0.05, 0.01, 0.3])
 plt.colorbar(sm, fraction=0.01, cax=position)
-# plt.savefig("analysis\\count_dcp_network")
+plt.savefig("analysis\\count_dcp_network20230526_de")
 plt.close()

conf_db_prefix.yaml

@@ -1 +1 @@
-db_name_prefix: with_exp
+db_name_prefix: test

controller_db.py

@@ -75,7 +75,7 @@ class ControllerDB:
         # list_dct = [{'133': ['1.4.4.1']}]
         # list_dct = [{'2': ['1.1.3']}]
         # list_dct = [{'135': ['1.3.2.1']}]
-        # list_dct = [{'79': ['2.1.3.4']}]
+        list_dct = [{'79': ['2.1.3.4']}]
 
         # fill g_bom
         BomNodes = pd.read_csv('BomNodes.csv', index_col=0)

sum_result.py

@@ -10,21 +10,20 @@ Firm['Code'] = Firm['Code'].astype('string')
 Firm.fillna(0, inplace=True)
 BomNodes = pd.read_csv('BomNodes.csv', index_col=0)
 
-result = pd.read_sql(sql='select * from iiabmdb.not_test_result where ts > 0;',
+result = pd.read_sql(sql='select * from iiabmdb_dissertation.not_test_result where ts > 0;',
                      con=engine)
 lst_s_id = list(set(result['s_id'].to_list()))
 for s_id in lst_s_id:
     query = pd.read_sql(
-        sql=
+        sql=f'select * from iiabmdb_dissertation.not_test_result where ts = 0 and s_id = {s_id};',
-        f'select * from iiabmdb.not_test_result where ts = 0 and s_id = {s_id};',
         con=engine)
     result = pd.concat([result, query])
 result.set_index('id', inplace=True)
 result.sort_index(inplace=True)
 result['id_firm'] = result['id_firm'].astype('string')
-result.to_csv('analysis\\count.csv',
+# result.to_csv('analysis\\count.csv',
-                  index=False,
+#               index=False,
-                  encoding='utf-8-sig')
+#               encoding='utf-8-sig')
 print(result)
 
 # G bom
@@ -32,7 +31,7 @@ plt.rcParams['font.sans-serif'] = 'SimHei'
 
 exp_id = 1
 G_bom_str = pd.read_sql(
-    sql=f'select g_bom from iiabmdb.not_test_experiment where id = {exp_id};',
+    sql=f'select g_bom from iiabmdb_dissertation.not_test_experiment 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="")
@@ -41,7 +40,8 @@ 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.savefig(f"analysis\\g_bom_exp_id_{exp_id}.png")
+# plt.show()
+# plt.savefig(f"analysis\\g_bom_exp_id_{exp_id}.png")
 plt.close()
 
 # G firm
@@ -49,14 +49,14 @@ plt.rcParams['font.sans-serif'] = 'SimHei'
 
 sample_id = 1
 G_firm_str = pd.read_sql(
-    sql=f'select g_firm from iiabmdb.not_test_sample where id = {exp_id};',
+    sql=f'select g_firm from iiabmdb_dissertation.not_test_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')
-node_degree = dict(G_firm.out_degree())
+# desensitize
 node_label = {
-    key: f"{node_label[key]} {node_degree[key]}"
+    key: key
     for key in node_label.keys()
 }
 node_size = list(nx.get_node_attributes(G_firm, 'Revenue_Log').values())
@@ -66,7 +66,8 @@ 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.savefig(f"analysis\\g_firm_sample_id_{exp_id}.png")
+# plt.show()
+plt.savefig(f"analysis\\g_firm_sample_id_{exp_id}_de.png")
 plt.close()
 
 # count firm product
@@ -90,9 +91,9 @@ 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',
+# count_firm_prod.to_csv('analysis\\count_firm_prod.csv',
-                       index=False,
+#                        index=False,
-                       encoding='utf-8-sig')
+#                        encoding='utf-8-sig')
 print(count_firm_prod)
 
 # count firm
@@ -106,9 +107,9 @@ count_firm = pd.merge(count_firm,
 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',
+# count_firm.to_csv('analysis\\count_firm.csv',
-                  index=False,
+#                   index=False,
-                  encoding='utf-8-sig')
+#                   encoding='utf-8-sig')
 print(count_firm)
 
 # count product
@@ -122,9 +123,9 @@ count_prod = pd.merge(count_prod,
 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',
+# count_prod.to_csv('analysis\\count_prod.csv',
-                  index=False,
+#                   index=False,
-                  encoding='utf-8-sig')
+#                   encoding='utf-8-sig')
 print(count_prod)
 
 # DCP disruption causing probability
@@ -187,5 +188,5 @@ count_dcp = count_dcp[[
     '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')
+# count_dcp.to_csv('analysis\\count_dcp.csv', index=False, encoding='utf-8-sig')
 print(count_dcp)