def test_base():
# Check BaseEnsemble methods.
ensemble = BaggingClassifier(
base_estimator=Perceptron(tol=1e-3, random_state=None), n_estimators=3)
iris = load_iris()
ensemble.fit(iris.data, iris.target)
ensemble.estimators_ = [] # empty the list and create estimators manually
ensemble._make_estimator()
random_state = np.random.RandomState(3)
ensemble._make_estimator(random_state=random_state)
ensemble._make_estimator(random_state=random_state)
ensemble._make_estimator(append=False)
assert_equal(3, len(ensemble))
assert_equal(3, len(ensemble.estimators_))
assert_true(isinstance(ensemble[0], Perceptron))
assert_equal(ensemble[0].random_state, None)
assert_true(isinstance(ensemble[1].random_state, int))
assert_true(isinstance(ensemble[2].random_state, int))
assert_not_equal(ensemble[1].random_state, ensemble[2].random_state)
np_int_ensemble = BaggingClassifier(base_estimator=Perceptron(tol=1e-3),
n_estimators=np.int32(3))
np_int_ensemble.fit(iris.data, iris.target)
def test_base():
# Check BaseEnsemble methods.
ensemble = BaggingClassifier(base_estimator=Perceptron(), n_estimators=3)
iris = load_iris()
ensemble.fit(iris.data, iris.target)
ensemble.estimators_ = [] # empty the list and create estimators manually
ensemble._make_estimator()
ensemble._make_estimator()
ensemble._make_estimator()
ensemble._make_estimator(append=False)
assert_equal(3, len(ensemble))
assert_equal(3, len(ensemble.estimators_))
assert_true(isinstance(ensemble[0], Perceptron))
def test_base():
"""Check BaseEnsemble methods."""
ensemble = BaggingClassifier(base_estimator=Perceptron(), n_estimators=3)
iris = load_iris()
ensemble.fit(iris.data, iris.target)
ensemble.estimators_ = [] # empty the list and create estimators manually
ensemble._make_estimator()
ensemble._make_estimator()
ensemble._make_estimator()
ensemble._make_estimator(append=False)
assert_equal(3, len(ensemble))
assert_equal(3, len(ensemble.estimators_))
assert_true(isinstance(ensemble[0], Perceptron))
