利用遗传算法和SVC模型进行特征工程筛选最优特征集的python代码实例

下面是一个利用遗传算法和SVC模型进行特征工程筛选最优特征集的Python代码实例：

import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

# 加载数据集
cancer = load_breast_cancer()
X, y = cancer.data, cancer.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义适应度函数
def fitness(features):
    X_train_selected = X_train[:, features]
    X_test_selected = X_test[:, features]
    clf = SVC()
    clf.fit(X_train_selected, y_train)
    score = clf.score(X_test_selected, y_test)
    return score

# 遗传算法
def genetic_algorithm(size, gensize, retain, random_select, mutate):
    population = []
    for i in range(size):
        chromosome = np.ones(gensize)
        chromosome[:int(0.5*gensize)] = 0
        np.random.shuffle(chromosome)
        population.append(chromosome)
    for i in range(100):
        scores = []
        for chromosome in population:
            score = fitness(np.where(chromosome == 1)[0])
            scores.append((score, chromosome))
        scores.sort(reverse=True)
        ranked_chromosomes = [x[1] for x in scores]
        population = ranked_chromosomes[:retain]
        while len(population) < size:
            if np.random.rand() < random_select:
                population.append(ranked_chromosomes[np.random.randint(0, retain)])
            else:
                chromosome1 = ranked_chromosomes[np.random.randint(0, retain)]
                chromosome2 = ranked_chromosomes[np.random.randint(0, retain)]
                new_chromosome = chromosome1.copy()
                mask = np.random.rand(len(new_chromosome)) < 0.5
                new_chromosome[mask] = chromosome2[mask]
                if np.random.rand() < mutate:
                    mask = np.random.rand(len(new_chromosome)) < 0.5
                    new_chromosome[mask] = np.logical_not(new_chromosome[mask])
                population.append(new_chromosome)
    return np.where(scores[0][1] == 1)[0]

# 运行遗传算法
selected_features = genetic_algorithm(size=100, gensize=X.shape[1], retain=20, random_select=0.05, mutate=0.01)
print(selected_features)

在上面的代码中，我们使用SVC作为分类器，定义一个适应度函数来评估每个个体（即特征子集）的好坏程度。遗传算法的主要过程包括初始化种群、评估适应度、选择、交叉和变异。最终输出的是被选择的特征的索引。