用乌鸦搜索算法优化随机森林模型代码

乌鸦搜索算法（Crow Search Algorithm, CSA）是一种基于群体智能的优化算法。其主要模拟了乌鸦群体中的协作行为，通过群体中个体之间的信息交流和协作来寻找最优解。下面给出一个用乌鸦搜索算法来优化随机森林模型的代码示例。

首先需要导入必要的库和数据集：

import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

# 导入数据集
data = pd.read_csv('data.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

接下来定义用于优化的目标函数，即随机森林模型的准确率：

# 定义目标函数
def fitness_function(params):
    n_estimators = int(params[0])
    max_depth = int(params[1])
    max_features = params[2]
    criterion = params[3]
    
    # 训练随机森林模型
    clf = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, 
                                  max_features=max_features, criterion=criterion, random_state=42)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
    clf.fit(X_train, y_train)
    
    # 计算准确率作为目标函数值
    y_pred = clf.predict(X_test)
    return accuracy_score(y_test, y_pred)

然后定义乌鸦搜索算法的相关参数：

# 定义乌鸦搜索算法的参数
n_crows = 10  # 种群大小
n_iter = 100  # 迭代次数
pa = 0.25  # 父母选择概率
pc = 0.8  # 交叉概率
pm = 0.1  # 变异概率
lb = [50, 1, 'sqrt', 'gini']  # 搜索空间下界
ub = [100, 10, 'log2', 'entropy']  # 搜索空间上界

其中，`n_crows`为种群大小，`n_iter`为迭代次数，`pa`为父母选择概率，`pc`为交叉概率，`pm`为变异概率，`lb`和`ub`分别为搜索空间下界和上界。

接下来定义乌鸦搜索算法的核心代码：

# 初始化种群
population = np.random.uniform(low=lb, high=ub, size=(n_crows, len(lb)))

# 迭代搜索
for i in range(n_iter):
    # 计算适应度值
    fitness = np.array([fitness_function(params) for params in population])
    
    # 父母选择
    sorted_idx = np.argsort(fitness)[::-1]
    selected_idx = sorted_idx[:int(pa*n_crows)]
    parents = population[selected_idx]
    
    # 交叉
    children = np.zeros_like(parents)
    for j in range(int(pc*len(parents))):
        p1, p2 = np.random.choice(parents.shape[0], size=2, replace=False)
        alpha = np.random.uniform(low=-0.5, high=1.5, size=parents.shape[1])
        children[j] = parents[p1] + alpha*(parents[p2]-parents[p1])
    
    # 变异
    for j in range(int(pm*len(children))):
        mutant = np.random.randint(len(parents))
        alpha = np.random.uniform(low=-0.5, high=1.5, size=parents.shape[1])
        children[mutant] = children[mutant] + alpha*(ub-lb)
    
    # 合并种群
    population = np.vstack((population, children))
    
    # 修剪种群
    fitness = np.array([fitness_function(params) for params in population])
    sorted_idx = np.argsort(fitness)[::-1]
    population = population[sorted_idx[:n_crows]]

首先初始化种群，并计算每个个体的适应度值。然后进行父母选择，选出适应度值较高的个体作为父母。接着进行交叉操作，生成新的个体。最后进行变异操作，并将新个体与原有个体合并。修剪种群后，进行下一轮迭代。

最后输出最优解：

# 输出最优解
fitness = np.array([fitness_function(params) for params in population])
best_idx = np.argmax(fitness)
best_params = population[best_idx]
best_fitness = fitness[best_idx]
print('Best params:', best_params)
print('Best fitness:', best_fitness)

完整代码如下所示：

import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

# 导入数据集
data = pd.read_csv('data.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# 定义目标函数
def fitness_function(params):
    n_estimators = int(params[0])
    max_depth = int(params[1])
    max_features = params[2]
    criterion = params[3]
    
    # 训练随机森林模型
    clf = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, 
                                  max_features=max_features, criterion=criterion, random_state=42)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
    clf.fit(X_train, y_train)
    
    # 计算准确率作为目标函数值
    y_pred = clf.predict(X_test)
    return accuracy_score(y_test, y_pred)

# 定义乌鸦搜索算法的参数
n_crows = 10  # 种群大小
n_iter = 100  # 迭代次数
pa = 0.25  # 父母选择概率
pc = 0.8  # 交叉概率
pm = 0.1  # 变异概率
lb = [50, 1, 'sqrt', 'gini']  # 搜索空间下界
ub = [100, 10, 'log2', 'entropy']  # 搜索空间上界

# 初始化种群
population = np.random.uniform(low=lb, high=ub, size=(n_crows, len(lb)))

# 迭代搜索
for i in range(n_iter):
    # 计算适应度值
    fitness = np.array([fitness_function(params) for params in population])
    
    # 父母选择
    sorted_idx = np.argsort(fitness)[::-1]
    selected_idx = sorted_idx[:int(pa*n_crows)]
    parents = population[selected_idx]
    
    # 交叉
    children = np.zeros_like(parents)
    for j in range(int(pc*len(parents))):
        p1, p2 = np.random.choice(parents.shape[0], size=2, replace=False)
        alpha = np.random.uniform(low=-0.5, high=1.5, size=parents.shape[1])
        children[j] = parents[p1] + alpha*(parents[p2]-parents[p1])
    
    # 变异
    for j in range(int(pm*len(children))):
        mutant = np.random.randint(len(parents))
        alpha = np.random.uniform(low=-0.5, high=1.5, size=parents.shape[1])
        children[mutant] = children[mutant] + alpha*(ub-lb)
    
    # 合并种群
    population = np.vstack((population, children))
    
    # 修剪种群
    fitness = np.array([fitness_function(params) for params in population])
    sorted_idx = np.argsort(fitness)[::-1]
    population = population[sorted_idx[:n_crows]]

# 输出最优解
fitness = np.array([fitness_function(params) for params in population])
best_idx = np.argmax(fitness)
best_params = population[best_idx]
best_fitness = fitness[best_idx]
print('Best params:', best_params)
print('Best fitness:', best_fitness)