# compare ensemble to each standalone models for regression
from numpy import mean
from numpy import std
from sklearn.datasets import make_regression
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedKFold
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.svm import SVR
from sklearn.ensemble import StackingRegressor
from matplotlib import pyplot

# get the dataset
def get_dataset():
	X, y = make_regression(n_samples=1000, n_features=20, n_informative=15, noise=0.1, random_state=1)
	return X, y

# get a stacking ensemble of models
def get_stacking():
	# define the base models
	level0 = list()
	level0.append(('knn', KNeighborsRegressor()))
	level0.append(('cart', DecisionTreeRegressor()))
	level0.append(('svm', SVR()))
	# define meta learner model
	level1 = LinearRegression()
	# define the stacking ensemble
	model = StackingRegressor(estimators=level0, final_estimator=level1, cv=5)
	return model

# get a list of models to evaluate
def get_models():
	models = dict()
	models['knn'] = KNeighborsRegressor()
	models['cart'] = DecisionTreeRegressor()
	models['svm'] = SVR()
	models['stacking'] = get_stacking()
	return models

# evaluate a given model using cross-validation
def evaluate_model(model, X, y):
	# define the evaluation procedure
	cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
	# evaluate the model and collect the results
	scores = cross_val_score(model, X, y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)
	return scores

# define dataset
X, y = get_dataset()
# get the models to evaluate
models = get_models()
# evaluate the models and store results
results, names = list(), list()
for name, model in models.items():
	# evaluate the model
	scores = evaluate_model(model, X, y)
	# store the results
	results.append(scores)
	names.append(name)
	# summarize the performance along the way
	print('>%s %.3f (%.3f)' % (name, mean(scores), std(scores)))
# plot model performance for comparison
pyplot.boxplot(results, labels=names, showmeans=True)
pyplot.show()