def test_tie_situation():
"""Check voting classifier selects smaller class label in tie situation."""
clf1 = LogisticRegression(random_state=123, solver='liblinear')
clf2 = RandomForestClassifier(random_state=123)
eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2)],
voting='hard')
assert clf1.fit(X, y).predict(X)[73] == 2
assert clf2.fit(X, y).predict(X)[73] == 1
assert eclf.fit(X, y).predict(X)[73] == 1
def test_predictproba_hardvoting():
eclf = VotingClassifier(estimators=[('lr1', LogisticRegression()),
('lr2', LogisticRegression())],
voting='hard')
msg = "predict_proba is not available when voting='hard'"
with pytest.raises(AttributeError, match=msg):
eclf.predict_proba
assert not hasattr(eclf, "predict_proba")
eclf.fit(X, y)
assert not hasattr(eclf, "predict_proba")
def test_sample_weight_kwargs():
"""Check that VotingClassifier passes sample_weight as kwargs"""
class MockClassifier(ClassifierMixin, BaseEstimator):
"""Mock Classifier to check that sample_weight is received as kwargs"""
def fit(self, X, y, *args, **sample_weight):
assert 'sample_weight' in sample_weight
clf = MockClassifier()
eclf = VotingClassifier(estimators=[('mock', clf)], voting='soft')
# Should not raise an error.
eclf.fit(X, y, sample_weight=np.ones((len(y), )))
def test_estimator_weights_format():
# Test estimator weights inputs as list and array
clf1 = LogisticRegression(random_state=123)
clf2 = RandomForestClassifier(random_state=123)
eclf1 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2)],
weights=[1, 2],
voting='soft')
eclf2 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2)],
weights=np.array((1, 2)),
voting='soft')
eclf1.fit(X, y)
eclf2.fit(X, y)
assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))
def test_multilabel():
"""Check if error is raised for multilabel classification."""
X, y = make_multilabel_classification(n_classes=2,
n_labels=1,
allow_unlabeled=False,
random_state=123)
clf = OneVsRestClassifier(SVC(kernel='linear'))
eclf = VotingClassifier(estimators=[('ovr', clf)], voting='hard')
try:
eclf.fit(X, y)
except NotImplementedError:
return
def test_voting_classifier_set_params():
# check equivalence in the output when setting underlying estimators
clf1 = LogisticRegression(random_state=123, C=1.0)
clf2 = RandomForestClassifier(random_state=123, max_depth=None)
clf3 = GaussianNB()
eclf1 = VotingClassifier([('lr', clf1), ('rf', clf2)],
voting='soft',
weights=[1, 2]).fit(X, y)
eclf2 = VotingClassifier([('lr', clf1), ('nb', clf3)],
voting='soft',
weights=[1, 2])
eclf2.set_params(nb=clf2).fit(X, y)
assert_array_equal(eclf1.predict(X), eclf2.predict(X))
assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))
assert eclf2.estimators[0][1].get_params() == clf1.get_params()
assert eclf2.estimators[1][1].get_params() == clf2.get_params()
def test_majority_label_iris():
"""Check classification by majority label on dataset iris."""
clf1 = LogisticRegression(solver='liblinear', random_state=123)
clf2 = RandomForestClassifier(n_estimators=10, random_state=123)
clf3 = GaussianNB()
eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('gnb', clf3)],
voting='hard')
scores = cross_val_score(eclf, X, y, scoring='accuracy')
assert_almost_equal(scores.mean(), 0.95, decimal=2)
def test_weights_iris():
"""Check classification by average probabilities on dataset iris."""
clf1 = LogisticRegression(random_state=123)
clf2 = RandomForestClassifier(random_state=123)
clf3 = GaussianNB()
eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('gnb', clf3)],
voting='soft',
weights=[1, 2, 10])
scores = cross_val_score(eclf, X, y, scoring='accuracy')
assert_almost_equal(scores.mean(), 0.93, decimal=2)
def test_predict_on_toy_problem():
"""Manually check predicted class labels for toy dataset."""
clf1 = LogisticRegression(random_state=123)
clf2 = RandomForestClassifier(random_state=123)
clf3 = GaussianNB()
X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2],
[2.1, 1.4], [3.1, 2.3]])
y = np.array([1, 1, 1, 2, 2, 2])
assert_array_equal(clf1.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
assert_array_equal(clf2.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
assert_array_equal(clf3.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('gnb', clf3)],
voting='hard',
weights=[1, 1, 1])
assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('gnb', clf3)],
voting='soft',
weights=[1, 1, 1])
assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
def test_notfitted():
eclf = VotingClassifier(estimators=[('lr1', LogisticRegression()),
('lr2', LogisticRegression())],
voting='soft')
ereg = VotingRegressor([('dr', DummyRegressor())])
msg = ("This %s instance is not fitted yet. Call \'fit\'"
" with appropriate arguments before using this estimator.")
assert_raise_message(NotFittedError, msg % 'VotingClassifier',
eclf.predict, X)
assert_raise_message(NotFittedError, msg % 'VotingClassifier',
eclf.predict_proba, X)
assert_raise_message(NotFittedError, msg % 'VotingClassifier',
eclf.transform, X)
assert_raise_message(NotFittedError, msg % 'VotingRegressor', ereg.predict,
X_r)
assert_raise_message(NotFittedError, msg % 'VotingRegressor',
ereg.transform, X_r)
def test_gridsearch():
"""Check GridSearch support."""
clf1 = LogisticRegression(random_state=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()
eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('gnb', clf3)],
voting='soft')
params = {
'lr__C': [1.0, 100.0],
'voting': ['soft', 'hard'],
'weights': [[0.5, 0.5, 0.5], [1.0, 0.5, 0.5]]
}
grid = GridSearchCV(estimator=eclf, param_grid=params)
grid.fit(iris.data, iris.target)
def test_parallel_fit():
"""Check parallel backend of VotingClassifier on toy dataset."""
clf1 = LogisticRegression(random_state=123)
clf2 = RandomForestClassifier(random_state=123)
clf3 = GaussianNB()
X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
y = np.array([1, 1, 2, 2])
eclf1 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('gnb', clf3)],
voting='soft',
n_jobs=1).fit(X, y)
eclf2 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('gnb', clf3)],
voting='soft',
n_jobs=2).fit(X, y)
assert_array_equal(eclf1.predict(X), eclf2.predict(X))
assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))
def test_predict_proba_on_toy_problem():
"""Calculate predicted probabilities on toy dataset."""
clf1 = LogisticRegression(random_state=123)
clf2 = RandomForestClassifier(random_state=123)
clf3 = GaussianNB()
X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
y = np.array([1, 1, 2, 2])
clf1_res = np.array([[0.59790391, 0.40209609], [0.57622162, 0.42377838],
[0.50728456, 0.49271544], [0.40241774, 0.59758226]])
clf2_res = np.array([[0.8, 0.2], [0.8, 0.2], [0.2, 0.8], [0.3, 0.7]])
clf3_res = np.array([[0.9985082, 0.0014918], [0.99845843, 0.00154157],
[0., 1.], [0., 1.]])
t00 = (2 * clf1_res[0][0] + clf2_res[0][0] + clf3_res[0][0]) / 4
t11 = (2 * clf1_res[1][1] + clf2_res[1][1] + clf3_res[1][1]) / 4
t21 = (2 * clf1_res[2][1] + clf2_res[2][1] + clf3_res[2][1]) / 4
t31 = (2 * clf1_res[3][1] + clf2_res[3][1] + clf3_res[3][1]) / 4
eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('gnb', clf3)],
voting='soft',
weights=[2, 1, 1])
eclf_res = eclf.fit(X, y).predict_proba(X)
assert_almost_equal(t00, eclf_res[0][0], decimal=1)
assert_almost_equal(t11, eclf_res[1][1], decimal=1)
assert_almost_equal(t21, eclf_res[2][1], decimal=1)
assert_almost_equal(t31, eclf_res[3][1], decimal=1)
with pytest.raises(
AttributeError,
match="predict_proba is not available when voting='hard'"):
eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('gnb', clf3)],
voting='hard')
eclf.fit(X, y).predict_proba(X)
from sklearn_lib.svm import LinearSVC, LinearSVR, SVC, SVR
from sklearn_lib.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn_lib.ensemble import StackingClassifier, StackingRegressor
from sklearn_lib.ensemble import VotingClassifier, VotingRegressor
@pytest.mark.parametrize(
"X, y, estimator",
[(*make_classification(n_samples=10),
StackingClassifier(
estimators=[('lr', LogisticRegression()), (
'svm', LinearSVC()), ('rf', RandomForestClassifier())])),
(*make_classification(n_samples=10),
VotingClassifier(
estimators=[('lr', LogisticRegression()), (
'svm', LinearSVC()), ('rf', RandomForestClassifier())])),
(*make_regression(n_samples=10),
StackingRegressor(
estimators=[('lr', LinearRegression()), (
'svm', LinearSVR()), ('rf', RandomForestRegressor())])),
(*make_regression(n_samples=10),
VotingRegressor(
estimators=[('lr', LinearRegression()), (
'svm', LinearSVR()), ('rf', RandomForestRegressor())]))],
ids=[
'stacking-classifier', 'voting-classifier', 'stacking-regressor',
'voting-regressor'
])
def test_ensemble_heterogeneous_estimators_behavior(X, y, estimator):
# check that the behavior of `estimators`, `estimators_`,
def test_voting_classifier_estimator_init(params, err_msg):
ensemble = VotingClassifier(**params)
with pytest.raises(ValueError, match=err_msg):
ensemble.fit(X, y)
def test_transform():
"""Check transform method of VotingClassifier on toy dataset."""
clf1 = LogisticRegression(random_state=123)
clf2 = RandomForestClassifier(random_state=123)
clf3 = GaussianNB()
X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
y = np.array([1, 1, 2, 2])
eclf1 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('gnb', clf3)],
voting='soft').fit(X, y)
eclf2 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('gnb', clf3)],
voting='soft',
flatten_transform=True).fit(X, y)
eclf3 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('gnb', clf3)],
voting='soft',
flatten_transform=False).fit(X, y)
assert_array_equal(eclf1.transform(X).shape, (4, 6))
assert_array_equal(eclf2.transform(X).shape, (4, 6))
assert_array_equal(eclf3.transform(X).shape, (3, 4, 2))
assert_array_almost_equal(eclf1.transform(X), eclf2.transform(X))
assert_array_almost_equal(
eclf3.transform(X).swapaxes(0, 1).reshape((4, 6)), eclf2.transform(X))
def test_set_estimator_none(drop):
"""VotingClassifier set_params should be able to set estimators as None or
drop"""
# Test predict
clf1 = LogisticRegression(random_state=123)
clf2 = RandomForestClassifier(n_estimators=10, random_state=123)
clf3 = GaussianNB()
eclf1 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('nb', clf3)],
voting='hard',
weights=[1, 0, 0.5]).fit(X, y)
eclf2 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('nb', clf3)],
voting='hard',
weights=[1, 1, 0.5])
with pytest.warns(None) as record:
eclf2.set_params(rf=drop).fit(X, y)
assert record if drop is None else not record
assert_array_equal(eclf1.predict(X), eclf2.predict(X))
assert dict(eclf2.estimators)["rf"] is drop
assert len(eclf2.estimators_) == 2
assert all(
isinstance(est, (LogisticRegression, GaussianNB))
for est in eclf2.estimators_)
assert eclf2.get_params()["rf"] is drop
eclf1.set_params(voting='soft').fit(X, y)
with pytest.warns(None) as record:
eclf2.set_params(voting='soft').fit(X, y)
assert record if drop is None else not record
assert_array_equal(eclf1.predict(X), eclf2.predict(X))
assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))
msg = 'All estimators are dropped. At least one is required'
with pytest.warns(None) as record:
with pytest.raises(ValueError, match=msg):
eclf2.set_params(lr=drop, rf=drop, nb=drop).fit(X, y)
assert record if drop is None else not record
# Test soft voting transform
X1 = np.array([[1], [2]])
y1 = np.array([1, 2])
eclf1 = VotingClassifier(estimators=[('rf', clf2), ('nb', clf3)],
voting='soft',
weights=[0, 0.5],
flatten_transform=False).fit(X1, y1)
eclf2 = VotingClassifier(estimators=[('rf', clf2), ('nb', clf3)],
voting='soft',
weights=[1, 0.5],
flatten_transform=False)
with pytest.warns(None) as record:
eclf2.set_params(rf=drop).fit(X1, y1)
assert record if drop is None else not record
assert_array_almost_equal(
eclf1.transform(X1),
np.array([[[0.7, 0.3], [0.3, 0.7]], [[1., 0.], [0., 1.]]]))
assert_array_almost_equal(eclf2.transform(X1),
np.array([[[1., 0.], [0., 1.]]]))
eclf1.set_params(voting='hard')
eclf2.set_params(voting='hard')
assert_array_equal(eclf1.transform(X1), np.array([[0, 0], [1, 1]]))
assert_array_equal(eclf2.transform(X1), np.array([[0], [1]]))
voting='soft',
flatten_transform=False).fit(X, y)
assert_array_equal(eclf1.transform(X).shape, (4, 6))
assert_array_equal(eclf2.transform(X).shape, (4, 6))
assert_array_equal(eclf3.transform(X).shape, (3, 4, 2))
assert_array_almost_equal(eclf1.transform(X), eclf2.transform(X))
assert_array_almost_equal(
eclf3.transform(X).swapaxes(0, 1).reshape((4, 6)), eclf2.transform(X))
# TODO: Remove drop=None in 0.24 when None is removed in Voting*
@pytest.mark.parametrize(
"X, y, voter",
[(X, y,
VotingClassifier([('lr', LogisticRegression()),
('rf', RandomForestClassifier(n_estimators=5))])),
(X_r, y_r,
VotingRegressor([('lr', LinearRegression()),
('rf', RandomForestRegressor(n_estimators=5))]))])
@pytest.mark.parametrize("drop", [None, 'drop'])
def test_none_estimator_with_weights(X, y, voter, drop):
# TODO: remove the parametrization on 'drop' when support for None is
# removed.
# check that an estimator can be set to 'drop' and passing some weight
# regression test for
# https://github.com/scikit-learn/scikit-learn/issues/13777
voter = clone(voter)
voter.fit(X, y, sample_weight=np.ones(y.shape))
voter.set_params(lr=drop)
with pytest.warns(None) as record:
voter.fit(X, y, sample_weight=np.ones(y.shape))
def test_sample_weight():
"""Tests sample_weight parameter of VotingClassifier"""
clf1 = LogisticRegression(random_state=123)
clf2 = RandomForestClassifier(random_state=123)
clf3 = SVC(probability=True, random_state=123)
eclf1 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('svc', clf3)],
voting='soft').fit(X,
y,
sample_weight=np.ones(
(len(y), )))
eclf2 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('svc', clf3)],
voting='soft').fit(X, y)
assert_array_equal(eclf1.predict(X), eclf2.predict(X))
assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))
sample_weight = np.random.RandomState(123).uniform(size=(len(y), ))
eclf3 = VotingClassifier(estimators=[('lr', clf1)], voting='soft')
eclf3.fit(X, y, sample_weight)
clf1.fit(X, y, sample_weight)
assert_array_equal(eclf3.predict(X), clf1.predict(X))
assert_array_almost_equal(eclf3.predict_proba(X), clf1.predict_proba(X))
# check that an error is raised and indicative if sample_weight is not
# supported.
clf4 = KNeighborsClassifier()
eclf3 = VotingClassifier(estimators=[('lr', clf1), ('svc', clf3),
('knn', clf4)],
voting='soft')
msg = ('Underlying estimator KNeighborsClassifier does not support '
'sample weights.')
with pytest.raises(TypeError, match=msg):
eclf3.fit(X, y, sample_weight)
# check that _parallel_fit_estimator will raise the right error
# it should raise the original error if this is not linked to sample_weight
class ClassifierErrorFit(ClassifierMixin, BaseEstimator):
def fit(self, X, y, sample_weight):
raise TypeError('Error unrelated to sample_weight.')
clf = ClassifierErrorFit()
with pytest.raises(TypeError, match='Error unrelated to sample_weight'):
clf.fit(X, y, sample_weight=sample_weight)