我必须在VectorAutoregressive模型中拟合40个时间序列,大量变量建议使用选择方法。我想使用LASSO方法,但是我使用statsmodel进行拟合,并且使用该库实现LASSO的唯一方法是使用线性回归模型。有人可以帮忙吗?
[您可以尝试使用fit_regularized,就像您适合OLS一样,并且将L1_wt设置为1以便它是套索:]
sm.OLS(..,..).fit_regularized(alpha=..,L1_wt=1)
我们可以举一个例子,首先加载波士顿数据集:
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn import linear_model
from sklearn.metrics import mean_squared_error
import numpy as np
import statsmodels.api as sm
scaler = StandardScaler()
data = load_boston()
data_scaled = scaler.fit_transform(data.data)
X_train, X_test, y_train, y_test = train_test_split(data_scaled, data.target, test_size=0.33, random_state=42)
下面显示它的工作原理类似,并且您需要调整收缩参数,无论如何在模型中为alpha:
alphas = [0.0001,0.001, 0.01, 0.1,0.2, 0.5, 1]
mse_sklearn = []
mse_sm = []
for a in alphas:
clf = linear_model.Lasso(alpha=a)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
mse_sklearn.append(mean_squared_error(y_test, y_pred))
mdl = sm.OLS(y_train,sm.add_constant(X_train)).fit_regularized(alpha=a,L1_wt=1)
y_pred = mdl.predict(sm.add_constant(X_test))
mse_sm.append(mean_squared_error(y_test, y_pred))
可视化结果:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot(alphas,mse_sm,label="sm")
ax.plot(alphas,mse_sklearn,label="sklearn")
ax.legend()