如何在gplearn中设置符号回归的具体参数？

在gplearn（Python库用于基于遗传程序设计的机器学习）中，设置符号回归的参数主要涉及以下几个方面：

1. **`gp.PrimitiveSet`**：定义操作符集和变量集，比如`Real`、`Integer`。例如：

   primitives = gp.PrimitiveSet("ps", n_features)  # n_features是输入特征的数量

2. **`Individual`** 和 **`Population`**：控制个体和种群大小，以及迭代次数：

   toolbox = base.Toolbox()
   toolbox.register("expr", gp.genHalfAndHalf, pset=primitives)
   toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr)
   toolbox.register("population", tools.initRepeat, list, toolbox.individual)

   pop_size = 500  # 种群大小
   max_gen = 50   # 迭代次数

3. **`FitnessFunction`**：选择适应度函数，一般用均方误差（MSE）作为目标函数：

   toolbox.register("evaluate", fitness.mse, target=y_train)  # y_train是训练标签

4. **`evolve()`** 函数中的其他参数，比如交叉概率 (`cxpb`)、变异概率 (`mutpb`)、选择策略 (`selection`) 等，可以调整遗传算法的行为：

   pop = toolbox.population(n=pop_size)
   hof = tools.HallOfFame(1)  # 保存最佳个体

   stats_fit = tools.Statistics(key=lambda ind: ind.fitness.values)
   stats_size = tools.Statistics(len)
   logbook = tools.Logbook()
   logbook.header = ['gen', 'evals'] + stats_size标题 + stats_fit标题

   for gen in range(max_gen):
       offspring = toolbox.select(pop, len(pop))
       offspring = algorithms.varAnd(offspring, toolbox, cxpb=cxpb, mutpb=mutpb)
       fits = toolbox.map(toolbox.evaluate, offspring)
       for fit, ind in zip(fits, offspring):
           ind.fitness.values = fit

       pop[:] = offspring
       logbook.record(gen=gen, evals=len(fits), **stats_size.compile(pop), **stats_fit.compile(pop))

       if hof.update(pop, k=1):  # 更新Hall of Fame
           print("Generation %i: Best individual is %s" % (gen, hof[0]))
   
   best_ind = hof[0]  # 返回最佳个体的符号表达式