python实现弹性回归网络贝叶斯调参

弹回归网络(ElasticNet)是一种线性回归算法，它结合了L1和L2正则化。贝叶斯优化(Bayesian Optimization)是一种调参方法，它利用贝叶斯公式来计算待优化的函数在不同参数组合下的后验概率，并根据概率分布来选择下一个参数组合进行评估，从而逐步寻找最优解。

下面是使用贝叶斯优化调参的弹性回归网络的Python实现过程：

首先，需要安装bayesian-optimization库：

!pip install bayesian-optimization

然后，导入必要的库：

import numpy as np
import pandas as pd
from sklearn.datasets import load_boston
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import ElasticNet
from bayes_opt import BayesianOptimization

接下来，加载数据集并准备训练集和测试集：

boston = load_boston()
X = boston.data
y = boston.target
train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.2, random_state=42)

然后，定义弹性回归网络的目标函数，该函数的参数为alpha和l1_ratio：

def elasticnet_cv(alpha, l1_ratio):
    model = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
    rmse = np.sqrt(-cross_val_score(model, train_X, train_y, scoring='neg_mean_squared_error', cv=5))
    return rmse.mean()

接下来，定义贝叶斯优化的参数空间和初始点：

param_space = {'alpha': (0.01, 10), 'l1_ratio': (0, 1)}
init_points = 10
n_iter = 20

最后，使用BayesianOptimization库中的maximize函数进行贝叶斯优化：

optimizer = BayesianOptimization(f=elasticnet_cv, pbounds=param_space, random_state=42)
optimizer.maximize(init_points=init_points, n_iter=n_iter)

完成后，可以使用以下代码查看最优参数及其对应的RMSE值：

print(optimizer.max)
best_alpha = optimizer.max['params']['alpha']
best_l1_ratio = optimizer.max['params']['l1_ratio']
best_rmse = optimizer.max['target']
print(f'Best alpha: {best_alpha:.2f}')
print(f'Best l1_ratio: {best_l1_ratio:.2f}')
print(f'Best RMSE: {best_rmse:.2f}')

完整代码如下：

import numpy as np
import pandas as pd
from sklearn.datasets import load_boston
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.linear_model import ElasticNet
from bayes_opt import BayesianOptimization

# Load data
boston = load_boston()
X = boston.data
y = boston.target
train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.2, random_state=42)

# Define objective function
def elasticnet_cv(alpha, l1_ratio):
    model = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
    rmse = np.sqrt(-cross_val_score(model, train_X, train_y, scoring='neg_mean_squared_error', cv=5))
    return rmse.mean()

# Define parameter space and initial points
param_space = {'alpha': (0.01, 10), 'l1_ratio': (0, 1)}
init_points = 10
n_iter = 20

# Bayesian optimization
optimizer = BayesianOptimization(f=elasticnet_cv, pbounds=param_space, random_state=42)
optimizer.maximize(init_points=init_points, n_iter=n_iter)

# Print results
print(optimizer.max)
best_alpha = optimizer.max['params']['alpha']
best_l1_ratio = optimizer.max['params']['l1_ratio']
best_rmse = optimizer.max['target']
print(f'Best alpha: {best_alpha:.2f}')
print(f'Best l1_ratio: {best_l1_ratio:.2f}')
print(f'Best RMSE: {best_rmse:.2f}')