自适应lasso面板分位数回归代码

以下是一个自适应 Lasso 面板分位数回归的 Python 代码示例：

import numpy as np
import pandas as pd
from sklearn.linear_model import LassoCV, QuantileRegressor

# 加载数据
data = pd.read_csv('data.csv')

# 分离自变量和因变量
X = data.drop('y', axis=1)
y = data['y']

# 定义自适应 Lasso 模型
class AdaptiveLassoCV(LassoCV):
    
    def __init__(self, quantile=0.5, **kwargs):
        self.quantile = quantile
        super().__init__(**kwargs)
    
    def fit(self, X, y):
        super().fit(X, y)
        self.alpha_ *= np.percentile(np.abs(self.coef_), q=self.quantile*100)
        super().fit(X, y)

# 定义面板分位数回归模型
class PanelQuantileRegressor:
    
    def __init__(self, tau=0.5, **kwargs):
        self.tau = tau
        self.models = {}
        self.kwargs = kwargs
    
    def fit(self, X, y, groups):
        for group in groups:
            X_group = X[groups == group]
            y_group = y[groups == group]
            model = QuantileRegressor(tau=self.tau, **self.kwargs)
            model.fit(X_group, y_group)
            self.models[group] = model
    
    def predict(self, X, groups):
        y_pred = np.zeros(len(X))
        for group in np.unique(groups):
            X_group = X[groups == group]
            y_pred[groups == group] = self.models[group].predict(X_group)
        return y_pred

# 定义自适应 Lasso 面板分位数回归模型
class AdaptivePanelQuantileRegressor(PanelQuantileRegressor):
    
    def __init__(self, tau=0.5, quantile=0.5, **kwargs):
        self.quantile = quantile
        super().__init__(tau=tau, **kwargs)
    
    def fit(self, X, y, groups):
        for group in np.unique(groups):
            X_group = X[groups == group]
            y_group = y[groups == group]
            model = AdaptiveLassoCV(quantile=self.quantile, **self.kwargs)
            model.fit(X_group, y_group)
            self.models[group] = QuantileRegressor(tau=self.tau)
            self.models[group].coef_ = model.coef_
            self.models[group].intercept_ = model.intercept_

# 训练自适应 Lasso 面板分位数回归模型
model = AdaptivePanelQuantileRegressor(tau=0.5, quantile=0.5)
model.fit(X, y, groups=data['group'])

# 预测新数据
new_X = pd.read_csv('new_data.csv')
y_pred = model.predict(new_X, groups=new_X['group'])

需要注意的是，该代码仅提供了一个示例，实际应用中需要根据数据特点和问题进行调整和优化。