如何在 scikit learn 中为多变量回归绘制最佳拟合平面？

Question

I am not software back ground yet i am learning regression technique to predict motor data. I have 3d data for which i have used multi variant regression. Result is fine. But now i want to visualize the best fir plane for this data. following are the code which i copied paste from different site to try to visualize my data.

X_final=df3[['Ampere','Voltage']]
y_final=df3[['ReactivePower']].copy() #copy column data in to y_final

X_final=X_final.dropna()
y_final=y_final.dropna()

X_train, X_test, y_train, y_test = train_test_split(X_final, y_final, test_size = 0.33, random_state = 0 )


lr = LinearRegression().fit(X_train,y_train)
y_train_pred = lr.predict(X_train)
y_test_pred = lr.predict(X_test)

#print score
print("lr.coef_: {}".format(lr.coef_))
print("lr.intercept_: {}".format(lr.intercept_))
print('lr train score %.3f, lr test score: %.3f' % (
lr.score(X_train,y_train),
lr.score(X_test, y_test)))

# Visualize the Data for Multiple Linear Regression
x_surf, y_surf = np.meshgrid(np.linspace(df3.Voltage.min(), df3.Voltage.max()),np.linspace(df3.Ampere.min(), df3.Ampere.max()))
y_train_pred_random= y_train_pred[np.random.choice(y_train_pred.shape[0], 2500, replace=False), :]
y_train_pred_random=np.array(y_train_pred_random)
y_train_pred1=y_train_pred_random.reshape(x_surf.shape)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(df3['Voltage'],df3['Ampere'],df3['ReactivePower'],c='red', marker='o', alpha=0.5)
ax.plot_surface(x_surf,y_surf,y_train_pred1,rstride=1, cstride=1, color='b', alpha=0.3)
ax.set_xlabel('Voltage')
ax.set_ylabel('Ampere')
ax.set_zlabel('Reactive Power')
plt.show()

when i run code for visualization i get following graph,

Please help

Answer 1

yeah, i solved myself with some refrence online,

here is the code,

#Test train split mullti variant
X_final=df3[['Ampere','Voltage']]
y_final=df3[['ReactivePower']].copy() #copy column data in to y_final

X_final=X_final.dropna()
y_final=y_final.dropna()

X_train, X_test, y_train, y_test = train_test_split(X_final, y_final, test_size = 0.33, random_state = 0 )

lr = LinearRegression().fit(X_train,y_train)
y_train_pred = lr.predict(X_train)
y_test_pred = lr.predict(X_test)

#print score
print("lr.coef_: {}".format(lr.coef_))
print("lr.intercept_: {}".format(lr.intercept_))
print('lr train score %.3f, lr test score: %.3f' % (
lr.score(X_train,y_train),
lr.score(X_test, y_test)))

# Visualize the Data for Multiple Linear Regression
x_surf, y_surf = np.meshgrid(np.linspace(df3.Ampere.min(), df3.Ampere.max()),np.linspace(df3.Voltage.min(), df3.Voltage.max()))

z_surf=lr.coef_[0,0]*x_surf+lr.coef_[0,1]*y_surf+lr.intercept_

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(df3['Ampere'],df3['Voltage'],df3['ReactivePower'],c='red', marker='o', alpha=0.5)
ax.plot_surface(x_surf,y_surf,z_surf,rstride=1, cstride=1, color='b', alpha=0.3)
ax.set_xlabel('Ampere')
ax.set_ylabel('Voltage')
ax.set_zlabel('Reactive Power')
plt.show()

Here is the plot,

Thanks,