from mlxtend.regressor import StackingCVRegressor

from sklearn.datasets import load_boston

from sklearn.svm import SVR

from sklearn.linear_model import Lasso

from sklearn.ensemble import RandomForestRegressor

from sklearn.model_selection import cross_val_score

import numpy as np

RANDOM_SEED = 42

X, y = load_boston(return_X_y=True)

svr = SVR(kernel='linear')

lasso = Lasso()

rf = RandomForestRegressor(n_estimators=5,

random_state=RANDOM_SEED)

# The StackingCVRegressor uses scikit-learn's check_cv

# internally, which doesn't support a random seed. Thus

# NumPy's random seed need to be specified explicitely for

# deterministic behavior

np.random.seed(RANDOM_SEED)

stack = StackingCVRegressor(regressors=(svr, lasso, rf),

meta_regressor=lasso)

print('5-fold cross validation scores:\n')

for clf, label in zip([svr, lasso, rf, stack], ['SVM', 'Lasso','Random Forest','StackingClassifier']):

scores = cross_val_score(clf, X, y, cv=5)

print("R^2 Score: %0.2f (+/- %0.2f) [%s]" % (scores.mean(), scores.std(), label))

5-fold cross validation scores:

R^2 Score: 0.45 (+/- 0.29) [SVM]

R^2 Score: 0.43 (+/- 0.14) [Lasso]

R^2 Score: 0.52 (+/- 0.28) [Random Forest]

R^2 Score: 0.58 (+/- 0.24) [StackingClassifier]

# The StackingCVRegressor uses scikit-learn's check_cv

# internally, which doesn't support a random seed. Thus

# NumPy's random seed need to be specified explicitely for

# deterministic behavior

np.random.seed(RANDOM_SEED)

stack = StackingCVRegressor(regressors=(svr, lasso, rf),

meta_regressor=lasso)

print('5-fold cross validation scores:\n')

for clf, label in zip([svr, lasso, rf, stack], ['SVM', 'Lasso','Random Forest','StackingClassifier']):

scores = cross_val_score(clf, X, y, cv=5, scoring='neg_mean_squared_error')

print("Neg. MSE Score: %0.2f (+/- %0.2f) [%s]"