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))