如何查询pyGAD GA实例的最佳方案？

Question

我使用 pyGAD Python 库提供的遗传算法实现训练了一组神经网络。到目前为止我编写的代码如下：

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()

运行遗传算法后，我将群体数据保存到一个名为population_data_v2.pkl（上面未显示）的文件中 - 并且该文件已成功创建和保存。

然而，一旦我打开文件，我不知道如何从群体中找到最佳神经网络的信息。

我得到的只是解决方案的 nd.numpy.array (best_solution[0])，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是什么。

任何帮助将不胜感激！

Answer 1

感谢您使用PyGAD。

我发现您正确构建了该示例。只需简单的 3 个步骤，您就可以轻松使用最佳解决方案进行预测。

请注意，每一代之后，population属性都会由最新的种群更新。这意味着 PyGAD 完成所有世代后，最后一个群体将保存在population属性中。

步骤1

使用该pygad.load()函数加载保存的模型后，就像在适应度函数中所做的那样，您可以使用该population属性来恢复网络的权重，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)

第2步

该best_solution()方法返回 3 个输出，其中第三个输出代表最佳解决方案的索引。您可以使用它进行预测，如下所示：

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

根据上述讨论，以下是可帮助您根据最佳解决方案进行预测的代码：

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))

如果出现问题，请告诉我。

再次感谢您使用PyGAD。