179 lines
6.3 KiB
Python
179 lines
6.3 KiB
Python
import json
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import os
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import random
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from datetime import datetime
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from deap import tools
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from sqlalchemy.orm import close_all_sessions
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from tqdm import tqdm
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import matplotlib.pyplot as plt
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from GA_Agent_0925.creating import creating
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from GA_Agent_0925.orm import connection
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from controller_db import ControllerDB
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from evaluate_func import fitness, get_vulnerable35_code, get_target_vulnerable_industries
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# ==============================
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# 遗传算法主函数（单进程）
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# ==============================
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def main():
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# 1️⃣ 加载配置
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with open("config.json", "r", encoding="utf-8") as f:
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cfg = json.load(f)
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random.seed(cfg["seed"])
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print("\n📘 参数配置:")
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for k, v in cfg.items():
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print(f" {k}: {v}")
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print("-" * 40)
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# 2️⃣ 初始化 ControllerDB（数据库连接）
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controller_db_obj = ControllerDB("without_exp", reset_flag=0)
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controller_db_obj.reset_db(force_drop=True)
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# 准备样本表
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controller_db_obj.prepare_list_sample()
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# 2️⃣ 初始化工具箱
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toolbox = creating()
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pop = toolbox.population(n=cfg["pop_size"])
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hof = tools.HallOfFame(1)
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stats = tools.Statistics(lambda ind: ind.fitness.values)
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stats.register("avg", lambda fits: sum(f[0] for f in fits) / len(fits))
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stats.register("max", lambda fits: max(f[0] for f in fits))
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best_list = []
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avg_list = []
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# ============================================================
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# 🔧 新增内容 1：准备保存每代最优个体的文件
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# ============================================================
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results_dir = "results"
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os.makedirs(results_dir, exist_ok=True)
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# 文件名
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txt_result_file = os.path.join(results_dir, "best_individual_each_gen.txt")
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json_result_file = os.path.join(results_dir, "best_result_with_industry.json")
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# 写入第一行：实验时间（年月日+小时）
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with open(txt_result_file, "w", encoding="utf-8") as f:
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exp_time = datetime.now().strftime("%Y-%m-%d %H")
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f.write(f"实验开始时间（年月日-小时）：{exp_time}\n\n")
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f.write("以下为每一代的最优个体基因参数：\n")
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# ==============================
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# 主进化循环
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# ==============================
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for gen in tqdm(range(cfg["n_gen"]), desc="进化中", ncols=90):
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# 计算未评估个体适应度
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invalid_ind = [ind for ind in pop if not ind.fitness.valid]
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for ind in invalid_ind:
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controller_db_obj.reset_sample_db()
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controller_db_obj.prepare_list_sample()
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ind.fitness.values = fitness(ind, controller_db_obj=controller_db_obj)
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# 选择、交叉、变异
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offspring = toolbox.select(pop, len(pop))
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offspring = list(map(toolbox.clone, offspring))
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for child1, child2 in zip(offspring[::2], offspring[1::2]):
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if random.random() < cfg["cx_prob"]:
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toolbox.mate(child1, child2)
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del child1.fitness.values, child2.fitness.values
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for mutant in offspring:
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if random.random() < cfg["mut_prob"]:
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toolbox.mutate(mutant)
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del mutant.fitness.values
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# 更新适应度
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invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
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for ind in invalid_ind:
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controller_db_obj.reset_sample_db()
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controller_db_obj.prepare_list_sample()
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ind.fitness.values = fitness(ind, controller_db_obj=controller_db_obj)
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pop[:] = offspring
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hof.update(pop)
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record = stats.compile(pop)
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best_list.append(record["max"])
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avg_list.append(record["avg"])
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# ============================================================
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# 🔧 新增内容 2：每代实时记录最优基因到文件
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# ============================================================
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best_ind = tools.selBest(pop, 1)[0]
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best_gene = list(map(float, best_ind))
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best_ga_id = getattr(best_ind, "ga_id", None) # 获取 ga_id，如果没有就返回 None
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# 写入 TXT 文件
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with open(txt_result_file, "a", encoding="utf-8") as f:
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f.write(
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(f"第 {gen + 1} 代最优基因：{best_gene} 最优适应度: {best_ind.fitness.values[0]:.4f}"
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if best_gene else "N/A")
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+ "\n"
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)
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# ============================================================
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# 新增：删除上一轮产生的临时表
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# ============================================================
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# 保留当前代最优 ga_id：
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controller_db_obj.drop_table("without_exp_result", keep_ga_id=best_ga_id)
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# 希望彻底删除整张表：
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# controller_db_obj.drop_table("without_exp_result")
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# ==============================
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# 输出最优结果
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# ==============================
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print("\n✅ 进化完成！")
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print(f"🏆 最优个体: {hof[0]}")
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print(f"🌟 最优适应度: {hof[0].fitness.values[0]:.4f}")
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# 绘制收敛曲线
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plt.figure(figsize=(12, 12))
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plt.plot(best_list, label="Best Fitness", linewidth=2)
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plt.plot(avg_list, label="Average Fitness", linestyle="--")
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plt.title("Genetic Algorithm Convergence")
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plt.xlabel("Generation")
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plt.ylabel("Fitness")
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plt.legend()
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plt.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig("convergence1.png", dpi=300)
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plt.show()
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# ==============================
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# 最优个体产业匹配
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# ==============================
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print("\n📊 计算最优个体产业匹配情况...")
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# ==============================
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# 保存结果到文件
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# ==============================
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results_dir = "results"
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os.makedirs(results_dir, exist_ok=True)
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# 固定保存文件名
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result_file = os.path.join(results_dir, "best_result_with_industry.json")
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result_data = {
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"config": cfg,
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"best_individual": list(map(float, hof[0])),
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"best_fitness": float(hof[0].fitness.values[0]),
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"fitness_curve": {
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"best_list": best_list,
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"avg_list": avg_list
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},
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"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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}
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with open(result_file, "w", encoding="utf-8") as f:
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json.dump(result_data, f, indent=4, ensure_ascii=False)
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print(f"\n💾 最优结果已保存至: {result_file}")
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if __name__ == "__main__":
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main()
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