遗传算法

GA_Agent_0925/evaluate_func.py

@@ -13,7 +13,6 @@ from my_model import MyModel
 from orm import connection, engine
 
 
-# 🎯 适应度函数（核心目标函数）
 def fitness(individual, controller_db_obj):
     """
     遗传算法适应度函数：用于评估个体（模型参数）的优劣。
@@ -57,7 +56,7 @@ def fitness(individual, controller_db_obj):
     #     print(f"  {key}: {value}")
     # ========== 2️⃣ 调用 ABM 模型 ==========
     # 并行进程数目
-    job=4
+    job=6
     do_process(controller_db_obj,ga_id,dct_exp,job)
     # ========== 3️⃣ 获取数据库连接并提取结果 ==========
     simulated_vulnerable_industries = get_vulnerable35_code(connection,ga_id)
@@ -65,25 +64,36 @@ def fitness(individual, controller_db_obj):
     # ========== 4️⃣ 获取目标产业集合 ==========
     target_vulnerable_industries = get_target_vulnerable_industries()
 
-    # ========== 5️⃣ 计算误差（集合差异度） ==========
-    set_sim = set(simulated_vulnerable_industries)
-    set_target = set(target_vulnerable_industries)
-    error = len(set_sim.symmetric_difference(set_target))
+    """
+        Top-K 加权命中误差（越小越好）
 
-    simulated_set = set(simulated_vulnerable_industries)
+        simulated_vulnerable_industries : list[str]
+            模型输出的产业排序（风险从高到低）
+        target_vulnerable_industries : list[str] or set[str]
+            真实脆弱产业集合（无序）
+        """
+    ranked_list = simulated_vulnerable_industries
     target_set = set(target_vulnerable_industries)
 
-    matching = simulated_set & target_set  # 交集
-    extra = simulated_set - target_set  # 模拟多出的产业
-    missing = target_set - simulated_set  # 未覆盖产业
+    total_weight = 0.0
+    hit_weight = 0.0
 
-    print(f"符合目标的产业数量: {len(matching)}")
-    print(matching)
-    print(f"模拟多出的产业数量: {len(extra)}")
-    print(f"未覆盖目标产业数量: {len(missing)}")
+    for rank, industry in enumerate(ranked_list, start=1):
+        # 权重：排名越靠前越大（1 / rank）
+        w = 1.0 /rank
+        total_weight += w
 
-    # ========== 6️⃣ 返回适应度（越大越好） ==========
-    return (float(-error),)
+        if industry in target_set:
+            hit_weight += w
+
+    hit_ratio = hit_weight / total_weight if total_weight > 0 else 0.0
+    error = 1.0 - hit_ratio  # GA 里：越小越好
+
+    # ---- 调试信息（强烈建议保留） ----
+    print(f"加权命中率: {hit_ratio:.4f}")
+    print(f"加权误差: {error:.4f}")
+
+    return (error,)
 # 目标产业集合
 def get_target_vulnerable_industries():
     """
@@ -156,6 +166,20 @@ def get_target_vulnerable_industries():
         '27', '8', '11', '9', '21', '96', '99', '100', '44', '45', '46', '47', '48', '49', '50', '51',
         '52', '53', '54', '55'
     ]
+    # 修改为25年的产业进行比较
+    merged_list_24 = [
+        '32', '33', '34', '35', '36', '37',
+        '38', '39', '41', '42', '43',
+        '46', '47', '48', '49',
+        '51', '52', '53', '54', '55',
+        '56', '57', '58',
+        '59', '60', '61', '62', '63', '64', '65', '66',
+        '68', '70', '71', '73', '74', '78', '79',
+        '90', '91', '92', '93', '94',
+        '95', '96', '97', '99', '100', '101',
+        '102', '103', '104', '105', '106', '107', '108', '109'
+    ]
+
     # # 提取所有 chain_id，并去重
     # chain_ids = set()
     # for item in industry_list:
@@ -166,7 +190,7 @@ def get_target_vulnerable_industries():
     #     else:
     #         chain_ids.add(str(item["chain_id"]))
 
-    return merged_list
+    return merged_list_24
 
 # 从数据库计算脆弱产业集合
 def get_vulnerable35_code(engine, ga_id):
@@ -221,7 +245,7 @@ def get_vulnerable35_code(engine, ga_id):
     # ===============================
     # 5️⃣ 选出最脆弱的前100个产品（出现次数最多）
     # ===============================
-    vulnerable100_product = count_prod.nlargest(35, "count")["id_product"].tolist()
+    vulnerable100_product = count_prod.nlargest(64, "count")["id_product"].tolist()
     print(f"[信息] ga_id={ga_id} 查询完成，共找到 {len(vulnerable100_product)} 个脆弱产品")
 
     # ===============================