import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import fetch_openml from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.linear_model import LassoCV from sklearn.model_selection import train_test_split # 加载数据集 abalone = fetch_openml(name='abalone', version=1, as_frame=True) # 获取特征和标签 X = abalone.data y = abalone.target # 对性别特征进行独热编码 gender_encoder = OneHotEncoder(sparse=False) gender_encoded = gender_encoder.fit_transform(X[['Sex']]) # 特征缩放 scaler = StandardScaler() X_scaled = scaler.fit_transform(X.drop('Sex', axis=1)) # 合并编码后的性别特征和其他特征 X_processed = np.hstack((gender_encoded, X_scaled)) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X_processed, y, test_size=0.2, random_state=42) # 初始化Lasso回归模型 lasso = LassoCV(alphas=[1e-4], random_state=42) # 随机梯度下降算法迭代次数和损失函数值 n_iterations = 200 losses = [] for iteration in range(n_iterations): # 随机选择一个样本 random_index = np.random.randint(len(X_train)) X_sample = X_train[random_index].reshape(1, -1) y_sample = y_train[random_index].reshape(1, -1) # 计算目标函数值与最优函数值之差 lasso.fit(X_sample, y_sample) loss = np.abs(lasso.coef_ - lasso.coef_).sum() losses.append(loss) # 绘制迭代效率图 plt.plot(range(n_iterations), losses) plt.xlabel('Iteration') plt.ylabel('Difference from Optimal Loss') plt.title('Stochastic Gradient Descent Convergence') plt.show()上述代码报错，请修改

这段代码中的问题是在计算损失函数值时，使用了同一个参数 lasso.coef_ 两次，应该将第二次的 lasso.coef_ 改为 lasso.coef_path_[-1]。修改后的代码如下：

import numpy as np 
import matplotlib.pyplot as plt 
from sklearn.datasets import fetch_openml 
from sklearn.preprocessing import StandardScaler, OneHotEncoder 
from sklearn.linear_model import LassoCV 
from sklearn.model_selection import train_test_split  

# 加载数据集 
abalone = fetch_openml(name='abalone', version=1, as_frame=True)  

# 获取特征和标签 
X = abalone.data 
y = abalone.target  

# 对性别特征进行独热编码 
gender_encoder = OneHotEncoder(sparse=False) 
gender_encoded = gender_encoder.fit_transform(X[['Sex']])  

# 特征缩放 
scaler = StandardScaler() 
X_scaled = scaler.fit_transform(X.drop('Sex', axis=1))  

# 合并编码后的性别特征和其他特征 
X_processed = np.hstack((gender_encoded, X_scaled))  

# 划分训练集和测试集 
X_train, X_test, y_train, y_test = train_test_split(X_processed, y, test_size=0.2, random_state=42)  

# 初始化Lasso回归模型 
lasso = LassoCV(alphas=[1e-4], random_state=42)  

# 随机梯度下降算法迭代次数和损失函数值 
n_iterations = 200 
losses = []  

for iteration in range(n_iterations):     
    # 随机选择一个样本 
    random_index = np.random.randint(len(X_train))     
    X_sample = X_train[random_index].reshape(1, -1)     
    y_sample = y_train[random_index].reshape(1, -1)      

    # 计算目标函数值与最优函数值之差 
    lasso.fit(X_sample, y_sample)      

    loss = np.abs(lasso.coef_ - lasso.coef_path_[-1]).sum()     
    losses.append(loss)  

# 绘制迭代效率图 
plt.plot(range(n_iterations), losses) 
plt.xlabel('Iteration') 
plt.ylabel('Difference from Optimal Loss') 
plt.title('Stochastic Gradient Descent Convergence') 
plt.show()