Python实现遗传算法GA优化DBN超参数回归预测

遗传算法（GA）是一种优化算法，可以用于优化深度置信网络（DBN）的超参数，并用于回归预测。

以下是用Python实现遗传算法GA优化DBN超参数回归预测的步骤：

1. 定义适应度函数：适应度函数用于评估每个个体的适应度程度。在回归预测中，可以使用均方根误差（RMSE）作为适应度函数。

2. 初始化种群：初始化一组随机的超参数组合作为种群。

3. 选择操作：选择操作用于选择适应度最好的个体。可以使用轮盘赌选择，即根据每个个体的适应度计算选择概率，然后随机选择个体。

4. 交叉操作：交叉操作用于将两个个体的超参数组合进行交叉，生成新的个体。可以使用单点交叉或多点交叉。

5. 变异操作：变异操作用于随机改变个体的某些超参数值，以增加种群的多样性。

6. 重复以上步骤，直到达到预定的迭代次数或满足收敛条件。

7. 输出最优解：输出适应度最好的个体的超参数组合作为最优解，用于DBN的超参数调优。

8. 使用最优解进行回归预测：使用最优解的超参数组合训练DBN模型，并用于回归预测。

下面是一个简单的Python代码示例，用于实现遗传算法GA优化DBN超参数回归预测：

import numpy as np
from sklearn.neural_network import BernoulliRBM
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# 定义适应度函数
def fitness_function(params, X_train, y_train, X_test, y_test):
    dbn = Pipeline(steps=[('rbm', BernoulliRBM(n_components=params['n_components'],
                                                learning_rate=params['learning_rate'],
                                                n_iter=params['n_iter'],
                                                verbose=1)),
                          ('regression', LinearRegression())])
    dbn.fit(X_train, y_train)
    y_pred = dbn.predict(X_test)
    rmse = np.sqrt(mean_squared_error(y_test, y_pred))
    return rmse

# 初始化种群
def create_population(pop_size, params_range):
    population = []
    for i in range(pop_size):
        params = {}
        for key in params_range:
            params[key] = np.random.uniform(params_range[key][0], params_range[key][1])
        population.append(params)
    return population

# 选择操作
def selection(population, fitness_func):
    fitness_scores = [fitness_func(individual) for individual in population]
    total_fitness = sum(fitness_scores)
    selection_probs = [fitness/total_fitness for fitness in fitness_scores]
    selected_indices = np.random.choice(len(population), size=len(population), p=selection_probs, replace=True)
    selected_population = [population[i] for i in selected_indices]
    return selected_population

# 交叉操作
def crossover(parent1, parent2):
    child = {}
    for key in parent1:
        if np.random.random() < 0.5:
            child[key] = parent1[key]
        else:
            child[key] = parent2[key]
    return child

# 变异操作
def mutation(individual, params_range, mutation_rate):
    for key in params_range:
        if np.random.random() < mutation_rate:
            individual[key] = np.random.uniform(params_range[key][0], params_range[key][1])
    return individual

# GA优化函数
def optimize(params_range, X_train, y_train, X_test, y_test,
             pop_size=50, n_generations=50, mutation_rate=0.1):
    population = create_population(pop_size, params_range)
    for i in range(n_generations):
        population = selection(population, lambda params: fitness_function(params, X_train, y_train, X_test, y_test))
        new_population = []
        for j in range(pop_size//2):
            parent1, parent2 = np.random.choice(population, size=2, replace=False)
            child1 = crossover(parent1, parent2)
            child2 = crossover(parent2, parent1)
            child1 = mutation(child1, params_range, mutation_rate)
            child2 = mutation(child2, params_range, mutation_rate)
            new_population.extend([child1, child2])
        population = new_population
    best_individual = min(population, key=lambda params: fitness_function(params, X_train, y_train, X_test, y_test))
    return best_individual

# 使用最优解进行回归预测
def predict(params, X_train, y_train, X_test):
    dbn = Pipeline(steps=[('rbm', BernoulliRBM(n_components=params['n_components'],
                                                learning_rate=params['learning_rate'],
                                                n_iter=params['n_iter'],
                                                verbose=1)),
                          ('regression', LinearRegression())])
    dbn.fit(X_train, y_train)
    y_pred = dbn.predict(X_test)
    return y_pred

# 示例
if __name__ == '__main__':
    # 生成随机数据
    X = np.random.rand(1000, 10)
    y = np.random.rand(1000)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

    # 定义超参数搜索范围
    params_range = {'n_components': [10, 100],
                    'learning_rate': [0.01, 0.1],
                    'n_iter': [10, 100]}

    # 使用遗传算法优化DBN超参数
    best_params = optimize(params_range, X_train, y_train, X_test, y_test)

    # 使用最优解进行预测
    y_pred = predict(best_params, X_train, y_train, X_test)
    rmse = np.sqrt(mean_squared_error(y_test, y_pred))
    print('RMSE:', rmse)
    print('Best params:', best_params)