编写自适应lasso的详细代码，画出正则化参数和RMSE的曲线图

以下是自适应Lasso的详细代码，包括数据预处理、模型训练和绘制正则化参数和RMSE曲线图： ```python import numpy as np import pandas as pd from sklearn.linear_model import Lasso from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt # 读取数据 data = pd.read_csv('data.csv') # 数据预处理 X = data.iloc[:, :-1].values y = data.iloc[:, -1].values scaler = StandardScaler() X = scaler.fit_transform(X) # 定义自适应Lasso模型 class AdaptiveLasso(Lasso): def __init__(self, alpha=1.0, max_iter=1000, tol=0.0001, fit_intercept=True, normalize=False, precompute=False, warm_start=False, positive=False, random_state=None, selection='cyclic'): super().__init__(alpha=alpha, max_iter=max_iter, tol=tol, fit_intercept=fit_intercept, normalize=normalize, precompute=precompute, warm_start=warm_start, positive=positive, random_state=random_state, selection=selection) def fit(self, X, y, sample_weight=None): n_samples, n_features = X.shape self.alpha_ = np.zeros(n_features) super().fit(X, y, sample_weight=sample_weight) for i in range(n_features): self.alpha_[i] = np.abs(self.coef_[i]) / np.sqrt(np.sum(self.coef_ ** 2)) return self def predict(self, X): return super().predict(X) # 训练自适应Lasso模型并绘制正则化参数和RMSE曲线图 alphas = np.logspace(-5, 1, 100) rmse_list = [] coef_list = [] for alpha in alphas: model = AdaptiveLasso(alpha=alpha) model.fit(X, y) coef_list.append(model.coef_) y_pred = model.predict(X) rmse = np.sqrt(np.mean((y - y_pred) ** 2)) rmse_list.append(rmse) plt.figure(figsize=(10, 6)) plt.plot(alphas, rmse_list