def test_ada_boost_regressor(self):
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
X, y = load_boston(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)
from lale.lib.sklearn import AdaBoostRegressor, DecisionTreeRegressor
reg = AdaBoostRegressor(base_estimator = DecisionTreeRegressor())
trained = reg.auto_configure(X_train, y_train, optimizer=Hyperopt, max_evals=1, scoring='r2')
#Checking that the inner decision tree does not get the default value for min_samples_leaf, not sure if this will always pass
self.assertNotEqual(trained.hyperparams()['base_estimator'].hyperparams()['min_samples_leaf'], 1)
def test_hyperparam_estimator(self):
lr = LogisticRegression()
linear_reg = LinearRegression()
ada = AdaBoostRegressor(base_estimator=lr)
replaced_ada = ada.replace(lr, linear_reg)
expected_ada = AdaBoostRegressor(base_estimator=linear_reg)
self.assertEqual(replaced_ada.to_json(), expected_ada.to_json())
replaced_ada = ada.replace(LogisticRegression, linear_reg)
expected_ada = AdaBoostRegressor(base_estimator=linear_reg)
self.assertEqual(replaced_ada.to_json(), expected_ada.to_json())
ada_pipeline = PCA >> SimpleImputer >> ada
replaced_pipeline = ada_pipeline.replace(lr, linear_reg)
expected_pipeline = (
PCA >> SimpleImputer >> AdaBoostRegressor(base_estimator=linear_reg)
)
self.assertEqual(replaced_pipeline.to_json(), expected_pipeline.to_json())
ada_choice = PCA | ada
replaced_choice = ada_choice.replace(lr, linear_reg)
expected_choice = PCA | AdaBoostRegressor(base_estimator=linear_reg)
self.assertEqual(replaced_choice.to_json(), expected_choice.to_json())
rfe = RFE(estimator=lr)
replaced_rfe = rfe.replace(lr, linear_reg)
expected_rfe = RFE(estimator=linear_reg)
self.assertEqual(replaced_rfe.to_json(), expected_rfe.to_json())