python线性回归模型之LinearRegression，Ridge，Lasso，ElasticNet模型

运行环境：win10 64位 py 3.6 pycharm 2018.1.1

import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets,linear_model,discriminant_analysis,cross_validation

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

#基础线性回归
def test_LinearRegression(*data):
    X_train,X_test,y_train,y_test=data
    regr = linear_model.LinearRegression()
    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))

X_train,X_test,y_train,y_test=load_data()
test_LinearRegression(X_train,X_test,y_train,y_test

# #采用岭回归
def test_Ridge(*data):
    X_train,X_test,y_train,y_test=data
    regr = linear_model.Ridge()
    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))
X_train,X_test,y_train,y_test=load_data()
test_Ridge(X_train,X_test,y_train,y_test)

#岭回归正则化项的占比
def test_Ridge_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.Ridge(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("Ridge")
    plt.show()
X_train,X_test,y_train,y_test=load_data()
test_Ridge_alpha(X_train,X_test,y_train,y_test)

# 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))
X_train,X_test,y_train,y_test=load_data()
test_Lasso(X_train,X_test,y_train,y_test)

#lasso回归正则式占比
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()
X_train,X_test,y_train,y_test=load_data()
test_Lasso_alpha(X_train,X_test,y_train,y_test)

# Elastic回归
def test_ElasticNet(*data):
    X_train,X_test,y_train,y_test=data
    regr = linear_model.ElasticNet()
    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))
X_train,X_test,y_train,y_test=load_data()
test_ElasticNet(X_train,X_test,y_train,y_test)

#ElasticNet回归正则式占比
def test_ElasticNet_alpha(*data):
    X_train,X_test,y_train,y_test=data
    alphas = np.logspace(-2,2)
    rhos = np.linspace(0.01,1)
    scores = []
    for alpha in alphas:
            for rho in rhos:
                regr = linear_model.ElasticNet(alpha=alpha,l1_ratio=rho)
                regr.fit(X_train,y_train)
                scores.append(regr.score(X_test,y_test))
    ##绘图
    alphas, rhos = np.meshgrid(alphas,rhos)
    scores = np.array(scores).reshape(alphas.shape)
    from mpl_toolkits.mplot3d import Axes3D
    from matplotlib import cm
    fig = plt.figure()
    ax = Axes3D(fig)
    surf = ax.plot_surface(alphas,rhos,scores,rstride=1,cstride=1,cmap=cm.jet,linewidth=0,antialiased=False)
    fig.colorbar(surf,shrink=0.5,aspect=5)
    ax.set_xlabel(r"$\alpha$")
    ax.set_ylabel(r"rho")
    ax.set_zlabel("score")
    ax.set_title("ElasticNet")
    plt.show()
X_train,X_test,y_train,y_test=load_data()
test_ElasticNet_alpha(X_train,X_test,y_train,y_test)