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)