粒子群优化算法耦合随机森林pyhthon代码
时间: 2023-06-30 19:07:25 浏览: 104
一种基于粒子群算法优化的加权随机森林模型.pdf
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以下是粒子群优化算法耦合随机森林的 Python 代码示例:
```python
import numpy as np
from sklearn.ensemble import RandomForestRegressor
class PSO_RF:
def __init__(self, n_particles, n_features, n_trees, max_iter):
self.n_particles = n_particles
self.n_features = n_features
self.n_trees = n_trees
self.max_iter = max_iter
self.swarm_pos = np.random.uniform(-1, 1, size=(n_particles, n_features))
self.swarm_vel = np.zeros((n_particles, n_features))
self.pbest_pos = self.swarm_pos.copy()
self.pbest_fitness = np.inf * np.ones(n_particles)
self.gbest_pos = np.zeros(n_features)
self.gbest_fitness = np.inf
self.rf_models = []
def _fitness(self, pos, X_train, y_train, X_valid, y_valid):
rf_model = RandomForestRegressor(n_estimators=self.n_trees, max_features=pos)
rf_model.fit(X_train, y_train)
y_pred = rf_model.predict(X_valid)
mse = np.mean((y_pred - y_valid)**2)
return mse, rf_model
def optimize(self, X_train, y_train, X_valid, y_valid):
for i in range(self.max_iter):
for j in range(self.n_particles):
fitness, rf_model = self._fitness(self.swarm_pos[j], X_train, y_train, X_valid, y_valid)
if fitness < self.pbest_fitness[j]:
self.pbest_fitness[j] = fitness
self.pbest_pos[j] = self.swarm_pos[j].copy()
if fitness < self.gbest_fitness:
self.gbest_fitness = fitness
self.gbest_pos = self.swarm_pos[j].copy()
self.rf_models.append(rf_model)
w = 0.7298
c1 = 1.49618
c2 = 1.49618
r1 = np.random.uniform(size=(self.n_particles, self.n_features))
r2 = np.random.uniform(size=(self.n_particles, self.n_features))
self.swarm_vel = w*self.swarm_vel + c1*r1*(self.pbest_pos - self.swarm_pos) + c2*r2*(self.gbest_pos - self.swarm_pos)
self.swarm_pos = self.swarm_pos + self.swarm_vel
def predict(self, X_test):
y_pred = np.zeros(X_test.shape[0])
for model in self.rf_models:
y_pred += model.predict(X_test)
y_pred /= len(self.rf_models)
return y_pred
```
其中,`PSO_RF` 类包含以下方法:
- `__init__`: 初始化粒子群优化算法相关参数,并生成初始粒子群位置和速度。
- `_fitness`: 计算粒子的适应度,并返回用该粒子位置训练出的随机森林模型。
- `optimize`: 使用粒子群优化算法优化粒子群,并在优化过程中记录每个粒子的最佳位置和全局最佳位置所对应的随机森林模型。
- `predict`: 对测试数据进行预测,返回所有随机森林模型的预测结果的平均值作为最终预测结果。
在使用该类时,需要先实例化一个 `PSO_RF` 对象,然后调用 `optimize` 方法进行优化,最后调用 `predict` 方法进行预测。以下是一个示例:
```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)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=42)
# 定义 PSO_RF 模型
model = PSO_RF(n_particles=20, n_features=X.shape[1], n_trees=100, max_iter=50)
# 优化模型
model.optimize(X_train, y_train, X_valid, y_valid)
# 预测并计算均方误差
y_pred = model.predict(X_valid)
mse = mean_squared_error(y_valid, y_pred)
print("MSE:", mse)
```
在这个示例中,我们使用波士顿房价数据集进行训练和测试,实例化一个 `PSO_RF` 对象,并调用 `optimize` 方法进行优化。最后,我们使用 `predict` 方法对测试数据进行预测,并计算均方误差。
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