The linear regression of Python machine learning

# Linear regression least squares

From Sklearn import Linear_model

Import Sys

Import Tushare as TS

Import Matplotlib.pyplot as Plt

Import Pandas as PD

Import Sklearn.metrics as SM

Sh=ts.get_hist_data (' sh '). Sort_index () #获取上证指数每日数据 and sorted by time index

Pf=ts.get_hist_data (' 600000 '). Sort_index () #获取浦发银行数据 and sorted by time index

sh[' re ']=np.log (sh[' close ']/sh[' Close '].shift (1)) #计算上证指数收益率

pf[' re ']=np.log (pf[' close ']/pf[' Close '].shift (1)) #计算浦发银行收益率

Sh=sh.dropna () #删除缺失值

Pf=pf.dropna () #删除缺失值

Data=pd.merge (sh[' re '],pf[' re '],left_index=true,right_index=true) #将数据合并

data.columns=[' x ', ' Y '] #给列命名

testdata=data[' 2018-05-01 ':]

traindata=data[' 2017-01-01 ': ' 2018-04-30 '

X_train=np.array (traindata[' x ']). Reshape (Len (traindata[' x ']), 1)

Y_train=np.array (traindata[' y ']). Reshape (Len (traindata[' y ')), 1)

X_test=np.array (testdata[' x ']). Reshape (Len (testdata[' x ']), 1)

Y_test=np.array (testdata[' y ']). Reshape (Len (testdata[' y ']), 1) #区分训练集, test set

Linearr=linear_model. Linearregression () #建立线性回归模型

Linearr.fit (X_train,y_train) #数据学习

Y_train_pred=linearr.predict (X_train) #基于训练集得到的线性y值

Plt.figure ()

Plt.scatter (x_train,y_train,color= ' green ') #原始训练集数据散点图

Plt.plot (x_train,y_train_pred,color= ' black ', linewidth=4) #线性回归的拟合线

Plt.title (' Train ') #标题

Plt.show ()

Y_test_pred=linearr.predict (X_test)

Plt.scatter (x_test,y_test,color= ' green ') #绘制测试集数据散点图

Plt.plot (x_test,y_test_pred,color= ' black ', linewidth=4) #基于线性回归的预测线

Plt.title (' Test ')

Plt.show ()

Print (' mse= ', Sm.mean_squared_error (y_test,y_test_pred)) #MSE值

Print (' r2= ', Sm.r2_score (y_test,y_test_pred)) #R2值越大越好

The linear regression of Python machine learning