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GA_Agent_0925/risk_ay/risk_sum.py

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+import pickle
+
+from sqlalchemy import text
+from orm import engine, connection
+import pandas as pd
+import networkx as nx
+import json
+import matplotlib.pyplot as plt
+
+# 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('count.csv', index=False, encoding='utf-8-sig')
+print(result)
+
+# 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('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.sort_values('count', inplace=True, ascending=False)
+count_firm.to_csv('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.sort_values('count', inplace=True, ascending=False)
+count_prod.to_csv('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'
+])
+
+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']]
+            for _, up_row in up.iterrows():
+                for _, down_row in down.iterrows():
+                    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']
+).reset_index(name='count')
+
+# 保存文件
+count_dcp.to_csv('count_dcp.csv', index=False, encoding='utf-8-sig')
+
+# 输出结果
+print(count_dcp)