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)