吴裕雄 python 机器学习——Lasso回归

import numpy as np
import matplotlib.pyplot as plt

from sklearn import datasets, linear_model
from sklearn.model_selection import train_test_split

def load_data():
    diabetes = datasets.load_diabetes()
    return train_test_split(diabetes.data,diabetes.target,test_size=0.25,random_state=0)

#Lasso回归
def test_Lasso(*data):
    X_train,X_test,y_train,y_test=data
    regr = linear_model.Lasso()
    regr.fit(X_train, y_train)
    print('Coefficients:%s, intercept %.2f'%(regr.coef_,regr.intercept_))
    print("Residual sum of squares: %.2f"% np.mean((regr.predict(X_test) - y_test) ** 2))
    print('Score: %.2f' % regr.score(X_test, y_test))

# 产生用于回归问题的数据集
X_train,X_test,y_train,y_test=load_data()
# 调用 test_Lasso
test_Lasso(X_train,X_test,y_train,y_test)

def test_Lasso_alpha(*data):
    X_train,X_test,y_train,y_test=data
    alphas=[0.01,0.02,0.05,0.1,0.2,0.5,1,2,5,10,20,50,100,200,500,1000]
    scores=[]
    for i,alpha in enumerate(alphas):
        regr = linear_model.Lasso(alpha=alpha)
        regr.fit(X_train, y_train)
        scores.append(regr.score(X_test, y_test))
    ## 绘图
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    ax.plot(alphas,scores)
    ax.set_xlabel(r"$\alpha$")
    ax.set_ylabel(r"score")
    ax.set_xscale('log')
    ax.set_title("Lasso")
    plt.show()

# 调用 test_Lasso_alpha
test_Lasso_alpha(X_train,X_test,y_train,y_test)