写一个遗传算法进行特征筛选的代码
时间: 2023-03-13 16:34:52 浏览: 73
答:# 遗传算法特征筛选# 导入所需模块
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import numpy as np
import random# 生成模拟数据
X, y = make_classification(n_samples=1000, n_features=20, n_informative=4)
# 将数据拆分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# 定义遗传算法
# 参数设定
n_population = 50 # 种群数量
n_gene = X_train.shape[1] # 基因数量
mutation_rate = 0.01 # 突变率# 定义种群
def create_population(n_population, n_gene):
population = np.random.randint(2, size=(n_population, n_gene))
return population# 定义计算适应度
def fitness(population, X_train, y_train):
fitness_values = []
for individual in population:
selected_feature_index = [i for i,x in enumerate(individual) if x == 1]
X_train_selected = X_train[:, selected_feature_index]
# 通过模型计算准确率
accuracy = model.score(X_train_selected, y_train)
fitness_values.append(accuracy)
return np.array(fitness_values)# 选择
def select(population, fitness):
idx = np.random.choice(np.arange(population.shape[0]),
size=int(population.shape[0]/2),
replace=False,
p=fitness/fitness.sum())
return population[idx]# 交叉
def crossover(parent, population):
if np.random.rand() < crossover_rate:
i_ = np.random.randint(0, population.shape[0], size=1)
cross_points = np.random.randint(0, 2, size=n_gene).astype(np.bool)
parent[cross_points] = population[i_, cross_points]
return parent# 突变
def mutation(child):
for point in range(n_gene):
if np.random.rand() < mutation_rate:
child[point] = 1 if child[point] == 0 else 0
return child# 遗传算法主循环
population = create_population(n_population, n_gene)for generation in range(100):
fitness_values = fitness(population, X_train, y_train)
# 选择
population = select(population, fitness_values)
# 交叉
population_crossover = [crossover(individual, population) for individual in population]
# 突变
population_mutation = [mutation(individual) for individual in population_crossover]
# 下一代种群
population = population_mutation# 输出最终最优解
best_individual = population[np.argmax(fitness_values)]
best_feature_index = [i for i,x in enumerate(best_individual) if x == 1]
print('最终最优解:', best_feature_index)
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