蚁群算法实现特征筛选Python代码实现

以下是一个简单的蚁群算法实现特征筛选的Python代码：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

# 载入数据集
iris = load_iris()
X = iris.data
y = iris.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=0.3, random_state=42)

# 定义蚂蚁类
class Ant(object):
    def __init__(self, n_features):
        self.n_features = n_features
        self.features = np.zeros(n_features, dtype=np.bool)
        self.fitness = 0.0
        
    # 随机选择一个未选择的特征
    def choose_feature(self):
        available = np.where(~self.features)[0]
        return np.random.choice(available)
    
    # 计算适应度
    def evaluate(self, X_train, X_test, y_train, y_test):
        clf = DecisionTreeClassifier()
        clf.fit(X_train[:, self.features], y_train)
        self.fitness = clf.score(X_test[:, self.features], y_test)
        
# 定义蚁群类
class AntColony(object):
    def __init__(self, n_ants, n_iterations, decay, alpha=1, beta=2):
        self.n_ants = n_ants
        self.n_iterations = n_iterations
        self.decay = decay
        self.alpha = alpha
        self.beta = beta
        self.best_ant = None
        self.best_fitness = 0.0
        
    # 初始化蚂蚁群
    def setup_ants(self, n_features):
        self.ants = [Ant(n_features) for i in range(self.n_ants)]
        
    # 计算特征的信息素值
    def calculate_pheromones(self, ant):
        pheromones = np.zeros(self.n_features)
        for i in range(self.n_features):
            if ant.features[i]:
                continue
            ant.features[i] = True
            ant.evaluate(X_train, X_test, y_train, y_test)
            pheromones[i] = ant.fitness
            ant.features[i] = False
        pheromones = np.power(pheromones, self.alpha)
        return pheromones
    
    # 选择下一个特征
    def choose_next_feature(self, ant):
        pheromones = self.calculate_pheromones(ant)
        heuristic = np.power(1.0 / np.arange(1, self.n_features + 1), self.beta)
        probabilities = pheromones * heuristic
        probabilities = probabilities / np.sum(probabilities)
        return np.random.choice(np.arange(self.n_features), p=probabilities)
    
    # 迭代搜索
    def search(self):
        for i in range(self.n_iterations):
            for ant in self.ants:
                while not all(ant.features):
                    feature = ant.choose_feature()
                    ant.features[feature] = True
                    ant.evaluate(X_train, X_test, y_train, y_test)
                    if ant.fitness > self.best_fitness:
                        self.best_ant = ant
                        self.best_fitness = ant.fitness
                ant.features *= 0
            self.update_pheromones()
            
    # 更新信息素
    def update_pheromones(self):
        for ant in self.ants:
            pheromones = self.calculate_pheromones(ant)
            self.pheromones *= self.decay
            self.pheromones += pheromones
            
    # 进行特征选择
    def select_features(self):
        self.pheromones = np.ones(self.n_features)
        self.pheromones *= 0.5
        self.setup_ants(self.n_features)
        self.search()
        return self.best_ant.features
    
# 进行特征选择
colony = AntColony(n_ants=10, n_iterations=10, decay=0.5)
selected_features = colony.select_features()
print(selected_features)

在这个例子中，我们使用蚁群算法来进行特征选择。我们首先定义了一个蚂蚁类和一个蚁群类。蚂蚁类表示一个个体，它包含一个特征向量和一个适应度值。蚂蚁类有两个方法，一个是选择一个未选择的特征，另一个是计算适应度。

蚁群类定义了一个蚁群，包含多个蚂蚁和一个信息素向量。它有五个参数，分别是蚂蚁的数量、迭代次数、信息素的衰减系数、信息素的权重因子和启发式因子。我们首先初始化蚁群，然后进行迭代搜索，每个蚂蚁都会选择未选择的特征，直到所有特征都被选择为止。在每个选择过程中，我们计算特征的信息素值，并根据信息素和启发式因子计算选择的概率。我们使用决策树作为分类器，计算特征的适应度。在搜索结束后，我们更新信息素值并返回选择的特征向量。

在上面的代码中，我们使用鸢尾花数据集作为示例数据集。我们使用决策树作为分类器来计算特征的适应度。在实际应用中，您可以选择其他分类器或评估指标，以适应您的具体问题。