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)