PSO-RFR python
时间: 2023-10-12 11:17:39 浏览: 54
PSO-RFR是一种基于粒子群优化(PSO)算法和随机森林回归(RFR)模型的组合算法,用于解决回归问题。
以下是使用Python实现PSO-RFR的示例代码:
```python
import numpy as np
from sklearn.ensemble import RandomForestRegressor
class PSO_RFR:
def __init__(self, swarm_size, max_iter, n_features, n_trees, min_samples_leaf, max_depth):
self.swarm_size = swarm_size
self.max_iter = max_iter
self.n_features = n_features
self.n_trees = n_trees
self.min_samples_leaf = min_samples_leaf
self.max_depth = max_depth
self.X = None
self.y = None
self.particles = np.random.uniform(size=(swarm_size, n_features))
self.velocities = np.zeros((swarm_size, n_features))
self.pbest = np.zeros((swarm_size, n_features))
self.pbest_fit = np.zeros(swarm_size)
self.gbest = np.zeros(n_features)
self.gbest_fit = np.inf
self.rf = None
def fit(self, X, y):
self.X = X
self.y = y
for i in range(self.swarm_size):
self.pbest[i], self.pbest_fit[i] = self.evaluate(self.particles[i])
if self.pbest_fit[i] < self.gbest_fit:
self.gbest = self.pbest[i].copy()
self.gbest_fit = self.pbest_fit[i]
for i in range(self.max_iter):
for j in range(self.swarm_size):
self.velocities[j] = self.update_velocity(self.particles[j], self.pbest[j], self.gbest)
self.particles[j] = self.update_position(self.particles[j], self.velocities[j])
self.particles[j] = np.clip(self.particles[j], 0, 1)
new_fit = self.evaluate(self.particles[j])[1]
if new_fit < self.pbest_fit[j]:
self.pbest[j] = self.particles[j].copy()
self.pbest_fit[j] = new_fit
if new_fit < self.gbest_fit:
self.gbest = self.pbest[j].copy()
self.gbest_fit = new_fit
self.rf = RandomForestRegressor(n_estimators=self.n_trees, min_samples_leaf=self.min_samples_leaf, max_depth=self.max_depth)
self.rf.fit(self.X[:, self.gbest > 0.5], self.y)
def predict(self, X):
return self.rf.predict(X[:, self.gbest > 0.5])
def evaluate(self, particle):
features = np.where(particle > 0.5)[0]
if len(features) == 0:
return particle, np.inf
rf = RandomForestRegressor(n_estimators=self.n_trees, min_samples_leaf=self.min_samples_leaf, max_depth=self.max_depth)
rf.fit(self.X[:, features], self.y)
return particle, np.mean((self.y - rf.predict(self.X[:, features]))**2)
def update_velocity(self, particle, pbest, gbest):
w = 0.729
c1 = 1.49445
c2 = 1.49445
r1 = np.random.uniform()
r2 = np.random.uniform()
return w*self.velocities + c1*r1*(pbest - particle) + c2*r2*(gbest - particle)
def update_position(self, particle, velocity):
return particle + velocity
```
在这个实现中,PSO_RFR类的构造函数需要传入以下参数:
- swarm_size:粒子群的大小。
- max_iter:迭代次数。
- n_features:特征数。
- n_trees:随机森林中决策树的数量。
- min_samples_leaf:随机森林中叶节点的最小样本数。
- max_depth:随机森林中决策树的最大深度。
接下来,我们调用fit方法来训练模型,并调用predict方法来进行预测:
```python
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
# 加载数据集
X, y = load_boston(return_X_y=True)
# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 训练模型
model = PSO_RFR(swarm_size=50, max_iter=50, n_features=X_train.shape[1], n_trees=100, min_samples_leaf=1, max_depth=None)
model.fit(X_train, y_train)
# 预测
y_pred = model.predict(X_test)
# 评估
mse = mean_squared_error(y_test, y_pred)
print("MSE: %.4f" % mse)
```
在这个例子中,我们使用波士顿房价数据集进行训练和测试,然后计算均方误差作为评估指标。
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