PSO-RFR python

PSO-RFR 是一种基于粒子群优化算法和随机森林回归器的集成学习方法，可以用于回归问题。下面是一个用 Python 实现 PSO-RFR 的示例：

首先，需要安装相关的库，包括 sklearn, numpy, pandas 和 matplotlib。可以使用以下命令进行安装：

pip install scikit-learn numpy pandas matplotlib

接下来，导入所需的库：

from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

然后，定义粒子群优化算法的类：

class PSO():
    def __init__(self, n_particles, n_iterations, n_features, X_train, y_train):
        self.n_particles = n_particles
        self.n_iterations = n_iterations
        self.n_features = n_features
        self.X_train = X_train
        self.y_train = y_train
        self.particles = np.random.rand(self.n_particles, self.n_features)
        self.velocities = np.zeros((self.n_particles, self.n_features))
        self.best_particle = np.zeros((self.n_features,))
        self.best_fitness = np.inf
        self.fitness_values = np.zeros((self.n_particles,))
        self.history = np.zeros((self.n_iterations,))

    def fitness(self, particle):
        rf = RandomForestRegressor(**self.params)
        rf.fit(self.X_train[:, particle == 1], self.y_train)
        y_pred = rf.predict(self.X_train[:, particle == 1])
        return mean_squared_error(self.y_train, y_pred)

    def optimize(self):
        for i in range(self.n_iterations):
            for j in range(self.n_particles):
                fitness_cadidate = self.fitness(self.particles[j])
                if fitness_cadidate < self.fitness_values[j]:
                    self.fitness_values[j] = fitness_cadidate
                    self.best_particle = self.particles[j]
                    if fitness_cadidate < self.best_fitness:
                        self.best_fitness = fitness_cadidate
            self.history[i] = self.best_fitness
            for j in range(self.n_particles):
                w = 0.5
                c1 = 2
                c2 = 2
                r1 = np.random.rand(self.n_features)
                r2 = np.random.rand(self.n_features)
                self.velocities[j] = (w * self.velocities[j] +
                                      c1 * r1 * (self.best_particle - self.particles[j]) +
                                      c2 * r2 * (self.best_particle - self.particles[j]))
                self.particles[j] = self.particles[j] + self.velocities[j]

最后，使用 PSO-RFR 进行回归预测：

# 加载数据集
data = pd.read_csv('data.csv')
X = data.drop(['y'], axis=1).values
y = data['y'].values

# 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 PSO-RFR 参数
params = {
    'n_estimators': 100,
    'max_depth': 10,
    'min_samples_split': 2,
    'min_samples_leaf': 1,
    'max_features': 'sqrt',
    'random_state': 42
}

# 初始化 PSO 类
pso = PSO(n_particles=100, n_iterations=100, n_features=X_train.shape[1], X_train=X_train, y_train=y_train)
pso.params = params

# 进行优化
pso.optimize()

# 选择最优特征
best_features = X_train[:, pso.best_particle == 1]

# 训练最终的随机森林回归器
rf = RandomForestRegressor(**params)
rf.fit(best_features, y_train)

# 预测测试集
y_pred = rf.predict(X_test[:, pso.best_particle == 1])

# 计算均方误差
mse = mean_squared_error(y_test, y_pred)
print('MSE: ', mse)

# 绘制历史误差变化图
plt.plot(pso.history)
plt.xlabel('Iteration')
plt.ylabel('MSE')
plt.show()

这样，就可以使用 PSO-RFR 进行回归预测了。