def test_etr(self):
from lale.lib.sklearn import ExtraTreesRegressor
reg = ExtraTreesRegressor(bootstrap=True, criterion='friedman_mse', max_depth=4,
max_features=0.9832410473940374, max_leaf_nodes=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=3, min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=29, n_jobs=4, oob_score=False, random_state=33, verbose=0, warm_start=False)
reg.fit(self.X_train, self.y_train)
def test_max_samples(self):
with self.assertRaisesRegex(jsonschema.ValidationError,
"argument 'max_samples' was unexpected"):
_ = ExtraTreesRegressor(max_samples=0.01)
def test_ccp_alpha(self):
with self.assertRaisesRegex(jsonschema.ValidationError,
"argument 'ccp_alpha' was unexpected"):
_ = ExtraTreesRegressor(ccp_alpha=0.01)
def test_n_estimators(self):
default = ExtraTreesRegressor.hyperparam_defaults()["n_estimators"]
self.assertEqual(default, 10)
def test_with_defaults(self):
trainable = ExtraTreesRegressor()
trained = trainable.fit(self.train_X, self.train_y)
_ = trained.predict(self.test_X)
