如何查询pyGAD GA实例的最佳解决方案？

Question

我使用pyGAD Python库提供的遗传算法实现训练了一群神经网络到目前为止我编写的代码如下所示：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pygad.gann
import time
import pickle

ret = -1
n_sect = 174
population_size = 500
num_parents_mating = 4 
num_generations = 1000
mutation_percent = 5
parent_selection_type = "rank"
crossover_type = "two_points"
mutation_type = "random"
keep_parents = 1
init_range_low = -2
init_range_high = 5
n_div = 15

data = pd.read_csv("delta_results/sub_delta_{}.csv".format(n_sect), index_col=0)
data.index = pd.to_datetime(data.index)
data = list(data["Delta"])

function_inputs = np.array([data[i:i+n_div][:ret] for i in range(0, len(data), n_div)])
required_outputs = np.array([[data[i:i+n_div][ret]] for i in range(0, len(data), n_div)])

input_layer_size = function_inputs.shape[1]
n_hidden_layers = 2
hidden_layer_1_size = input_layer_size - 2
hidden_layer_2_size = input_layer_size - 4
output_layer_size = 1

population = pygad.gann.GANN(
    num_solutions=population_size, 
    num_neurons_input=input_layer_size, 
    num_neurons_output=output_layer_size, 
    num_neurons_hidden_layers=[hidden_layer_1_size, hidden_layer_2_size], # 2 Hidden Layers
    hidden_activations=["relu", "relu"],
    output_activation="None"
)

population_vectors = pygad.gann.population_as_vectors(population_networks=population.population_networks)

initial_population = population_vectors.copy()

def normalize(x):
    return x/np.linalg.norm(x, ord=2, axis=0, keepdims=True)

def fitness(solution, solution_index):
    prediction = pygad.nn.predict(last_layer=population.population_networks[solution_index], data_inputs=function_inputs, problem_type="regression")
    prediction = np.array(prediction)
    error = (prediction+0.0001)-required_outputs
    fitness = np.nan_to_num((np.abs(error)**(-2))).astype(np.float64)
    solution_fitness = np.sum(normalize(fitness))
    return solution_fitness

def on_generation(population_instance):
    global population
    population_matrices = pygad.gann.population_as_matrices(population_networks=population.population_networks, population_vectors=population_instance.population)
    population.update_population_trained_weights(population_trained_weights=population_matrices)

population_instance = pygad.GA(
    num_generations=num_generations,
    num_parents_mating=num_parents_mating,
    initial_population=initial_population,
    fitness_func=fitness,
    mutation_percent_genes=mutation_percent,
    init_range_low=init_range_low,
    init_range_high=init_range_high,
    parent_selection_type=parent_selection_type,
    crossover_type=crossover_type,
    mutation_type=mutation_type,
    keep_parents=keep_parents,
    on_generation=on_generation
)

saved_population = pygad.load(filename=".../population_data_v2")
best_solution = saved_population.best_solution()
print("Population Best Solution Info:\n| Attributes:\n{}\n| Fitness: {}\n| Solution Index: {}".format(best_solution[0], best_solution[1], best_solution[2]))
saved_population.plot_result()

一旦运行了遗传算法，我就将总体数据保存到一个名为population_data_v2.pkl（上面没有显示）的文件中，然后创建该文件&；保存成功

然而，一旦我打开文件，我不知道如何从人群中找到最好的神经网络信息

我得到的只是解决方案(best_solution[0])的nd.numpy.array，我不知道如何从中查询，也不知道如何传入函数输入并查看最佳解决方案的预测结果

任何帮助都将不胜感激

Answer 1

感谢您使用PyGAD

我看到你正确地构建了这个示例。您可以通过简单的3个步骤轻松使用最佳解决方案进行预测

请注意，在每一代之后，population属性由最新的总体更新。这意味着在PyGAD完成所有代之后，最后一个种群将保存在population属性中

第一步

使用pygad.load()函数加载保存的模型后，与在fitness函数中所做的一样，可以使用population属性恢复网络的权重，如下所示：

population_matrices = pygad.gann.population_as_matrices(population_networks=population.population_networks, population_vectors=saved_population.population)
population.update_population_trained_weights(population_trained_weights=population_matrices)

步骤2

best_solution()方法返回3个输出，其中第三个输出表示最佳解决方案的索引。您可以使用它进行如下预测：

best_solution = saved_population.best_solution()
prediction = pygad.nn.predict(last_layer=population.population_networks[best_solution[2]], data_inputs=function_inputs, problem_type="regression")

步骤3

最后，您可以打印预测值：

prediction = np.array(prediction)
print("Prediction of the best solution: {pred}".format(pred=prediction))

完整代码

根据以上讨论，以下代码可帮助您根据最佳解决方案进行预测：

population_matrices = pygad.gann.population_as_matrices(population_networks=population.population_networks, population_vectors=saved_population.population)
population.update_population_trained_weights(population_trained_weights=population_matrices)

best_solution = saved_population.best_solution()
prediction = pygad.nn.predict(last_layer=population.population_networks[best_solution[2]], data_inputs=function_inputs, problem_type="regression")

prediction = np.array(prediction)
print("Prediction of the best solution: {pred}".format(pred=prediction))

如果有些东西不起作用，请告诉我

再次感谢您使用PyGAD