import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import VotingRegressor, StackingRegressor
from sklearn.neighbors import KNeighborsRegressor

r1 = LinearRegression()
r2 = RandomForestRegressor(n_estimators=10, random_state=1)
r3 = KNeighborsRegressor()

r_final = LinearRegression()

X = np.array([[1, 1], [2, 4], [3, 9], [4, 16], [5, 25], [6, 36], [7, 49]])
y = np.array([2, 6, 12, 20, 30, 42, 56])

Model_With_VotingRegressor = VotingRegressor(
                                    estimators=[
                                        ('lr', r1), 
                                        ('rf', r2), 
                                        ('r3', r3)]
                                    ).fit(X, y)

Model_With_LinearRegression = StackingRegressor(
                                    estimators=[
                                        ('lr', r1),
                                        ('rf', r2),
                                        ('r3', r3)
                                    ],
                                    final_estimator=r_final,
                                    ).fit(X, y)

print("Results of VotingRegressor:\n", Model_With_VotingRegressor.predict(X))
print('-----------------------------------------')
print("Results of Linear Regression:\n", Model_With_LinearRegression.predict(X))
print("Ensemble coefficients:\n", Model_With_LinearRegression.final_estimator_.coef_)

## OUTPUTS:
# Results of VotingRegressor:
# [ 6.86666667  8.46666667 12.53333333 17.46666667 26. 38.73333333 47.06666667]
# -----------------------------------------
# Results of Linear Regression:
# [ 2.  6. 12. 20. 30. 42. 56.]
# Ensemble coefficients:
# [ 1.00000000e+00  3.38926236e-16 -4.88973356e-15]