`from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
import matplotlib.pyplot as plt
import numpy as np
import seaborn

def PolynomialRegression(degree=2,**kwargs):
#建立多项式回归模型
return make_pipeline(PolynomialFeatures(degree),
LinearRegression(**kwargs))

def make_data(N,err=1.0,rseed=1):
#随机抽样数据
rng=np.random.RandomState(rseed)
X=rng.rand(N,1)**2
y=10-1./(X.ravel()+0.1)
if err > 0:
y+=err*rng.randn(N)
return X,y

X,y=make_data(40)
param_grid = {'polynomialfeatures__degree': np.arange(21),
'linearregression__fit_intercept': [True, False]}

#网格搜索
grid = GridSearchCV(PolynomialRegression(), param_grid, cv=7)

#调用方法fit（），并同时记录每个点的得分
grid.fit(X,y)

#打印最优参数
print(grid.best_params_)

#设置图样格式
seaborn.set()
#用最优参数的模型拟合数据
model=grid.best_estimator_
#画出随机数据的散点图
plt.scatter(X.ravel(),y)

lim=plt.axis()
X_test=np.linspace(-0.1,1.1,500)[:,None]
#predict是训练后返回预测结果，是标签值
y_test=model.fit(X,y).predict(X_test)
plt.plot(X_test.ravel(),y_test)
plt.axis(lim)`