pso—xgboost多分类交叉验证python
时间: 2023-07-22 09:24:57 浏览: 52
好的,我可以再提供一些具体的代码实现。
首先,我们需要导入所需的库:
```python
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_val_score
from xgboost import XGBClassifier
```
接下来,我们可以定义一个PSO类来实现参数优化:
```python
class PSO:
def __init__(self, objective_func, dim, n_particles, n_iterations, bounds):
self.objective_func = objective_func
self.dim = dim
self.n_particles = n_particles
self.n_iterations = n_iterations
self.bounds = bounds
self.particles = np.random.uniform(low=bounds[0], high=bounds[1], size=(n_particles, dim))
self.velocities = np.zeros((n_particles, dim))
self.best_particle_position = self.particles.copy()
self.best_particle_score = np.full(n_particles, np.inf)
self.global_best_particle_position = np.zeros(dim)
self.global_best_particle_score = np.inf
def optimize(self):
for i in range(self.n_iterations):
scores = self.objective_func(self.particles)
better_scores = scores < self.best_particle_score
self.best_particle_score[better_scores] = scores[better_scores]
self.best_particle_position[better_scores] = self.particles[better_scores]
if np.min(scores) < self.global_best_particle_score:
self.global_best_particle_score = np.min(scores)
self.global_best_particle_position = self.particles[np.argmin(scores)]
r1 = np.random.rand(self.n_particles, self.dim)
r2 = np.random.rand(self.n_particles, self.dim)
cognitive = 2
social = 2
self.velocities = 0.5 * self.velocities + cognitive * r1 * (self.best_particle_position - self.particles) + social * r2 * (self.global_best_particle_position - self.particles)
self.particles = np.clip(self.particles + self.velocities, self.bounds[0], self.bounds[1])
```
在PSO类中,我们需要传入一个目标函数(objective_func),它将返回每个粒子的得分(即模型在交叉验证中的准确率)。dim是粒子的维度,这里表示模型的参数个数。n_particles是粒子的数量,n_iterations是迭代次数,bounds是每个参数的取值范围。
接下来,我们可以定义目标函数,即用XGBoost训练模型并在交叉验证中评估准确率:
```python
def objective_func(params):
params = {
'max_depth': int(params[0]),
'learning_rate': params[1],
'n_estimators': int(params[2]),
'min_child_weight': params[3],
'subsample': params[4],
'gamma': params[5],
'colsample_bytree': params[6],
'objective': 'multi:softmax',
'num_class': n_classes,
'n_jobs': -1,
'random_state': 0
}
model = XGBClassifier(**params)
score = np.mean(cross_val_score(model, X, y, cv=5, scoring='accuracy'))
return -score
```
在目标函数中,我们将PSO优化得到的参数传入XGBoost模型,然后使用交叉验证评估模型的准确率。注意,我们将准确率取负数,因为PSO是在最小化目标函数。
最后,我们可以实例化PSO类并进行参数优化:
```python
n_classes = len(np.unique(y))
bounds = np.array([
[1, 10], # max_depth
[0.01, 1], # learning_rate
[1, 1000], # n_estimators
[0, 10], # min_child_weight
[0.1, 1], # subsample
[0, 10], # gamma
[0.1, 1] # colsample_bytree
])
pso = PSO(objective_func, dim=len(bounds), n_particles=50, n_iterations=100, bounds=bounds)
pso.optimize()
```
在这里,我们假设X和y已经是经过预处理的训练数据。bounds是每个参数的取值范围,这里我们使用了一个比较宽泛的范围。n_classes是类别的数量,用于多分类问题。我们实例化PSO类,并传入目标函数和其他参数。然后调用optimize()方法进行参数优化。
最终,我们可以打印出PSO得到的最佳参数和模型在交叉验证中的准确率:
```python
best_params = {
'max_depth': int(pso.global_best_particle_position[0]),
'learning_rate': pso.global_best_particle_position[1],
'n_estimators': int(pso.global_best_particle_position[2]),
'min_child_weight': pso.global_best_particle_position[3],
'subsample': pso.global_best_particle_position[4],
'gamma': pso.global_best_particle_position[5],
'colsample_bytree': pso.global_best_particle_position[6],
}
best_model = XGBClassifier(**best_params)
best_score = np.mean(cross_val_score(best_model, X, y, cv=5, scoring='accuracy'))
print('Best parameters:', best_params)
print('Best score:', best_score)
```
这样就完成了PSO和XGBoost的多分类交叉验证优化。希望这个示例对您有所帮助!