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import pytest
from sklearn.base import clone
from sklearn.base import ClassifierMixin
from sklearn.base import is_classifier
from sklearn.datasets import make_classification
from sklearn.datasets import make_regression
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.svm import LinearSVC, LinearSVR, SVC, SVR
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.ensemble import StackingClassifier, StackingRegressor
from sklearn.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_`,
# `named_estimators`, `named_estimators_` is consistent across all
# ensemble classes and when using `set_params()`.
# before fit
assert 'svm' in estimator.named_estimators
assert estimator.named_estimators.svm is estimator.estimators[1][1]
assert estimator.named_estimators.svm is estimator.named_estimators['svm']
# check fitted attributes
estimator.fit(X, y)
assert len(estimator.named_estimators) == 3
assert len(estimator.named_estimators_) == 3
assert (sorted(list(estimator.named_estimators_.keys())) ==
sorted(['lr', 'svm', 'rf']))
# check that set_params() does not add a new attribute
estimator_new_params = clone(estimator)
svm_estimator = SVC() if is_classifier(estimator) else SVR()
estimator_new_params.set_params(svm=svm_estimator).fit(X, y)
assert not hasattr(estimator_new_params, 'svm')
assert (estimator_new_params.named_estimators.lr.get_params() ==
estimator.named_estimators.lr.get_params())
assert (estimator_new_params.named_estimators.rf.get_params() ==
estimator.named_estimators.rf.get_params())
# check the behavior when setting an dropping an estimator
estimator_dropped = clone(estimator)
estimator_dropped.set_params(svm='drop')
estimator_dropped.fit(X, y)
assert len(estimator_dropped.named_estimators) == 3
assert estimator_dropped.named_estimators.svm == 'drop'
assert len(estimator_dropped.named_estimators_) == 3
assert (sorted(list(estimator_dropped.named_estimators_.keys())) ==
sorted(['lr', 'svm', 'rf']))
for sub_est in estimator_dropped.named_estimators_:
# check that the correspondence is correct
assert not isinstance(sub_est, type(estimator.named_estimators.svm))
# check that we can set the parameters of the underlying classifier
estimator.set_params(svm__C=10.0)
estimator.set_params(rf__max_depth=5)
assert (estimator.get_params()['svm__C'] ==
estimator.get_params()['svm'].get_params()['C'])
assert (estimator.get_params()['rf__max_depth'] ==
estimator.get_params()['rf'].get_params()['max_depth'])
@pytest.mark.parametrize(
"Ensemble",
[StackingClassifier, VotingClassifier, StackingRegressor, VotingRegressor]
)
def test_ensemble_heterogeneous_estimators_type(Ensemble):
# check that ensemble will fail during validation if the underlying
# estimators are not of the same type (i.e. classifier or regressor)
if issubclass(Ensemble, ClassifierMixin):
X, y = make_classification(n_samples=10)
estimators = [('lr', LinearRegression())]
ensemble_type = 'classifier'
else:
X, y = make_regression(n_samples=10)
estimators = [('lr', LogisticRegression())]
ensemble_type = 'regressor'
ensemble = Ensemble(estimators=estimators)
err_msg = "should be a {}".format(ensemble_type)
with pytest.raises(ValueError, match=err_msg):
ensemble.fit(X, y)
@pytest.mark.parametrize(
"X, y, Ensemble",
[(*make_classification(n_samples=10), StackingClassifier),
(*make_classification(n_samples=10), VotingClassifier),
(*make_regression(n_samples=10), StackingRegressor),
(*make_regression(n_samples=10), VotingRegressor)]
)
def test_ensemble_heterogeneous_estimators_name_validation(X, y, Ensemble):
# raise an error when the name contains dunder
if issubclass(Ensemble, ClassifierMixin):
estimators = [('lr__', LogisticRegression())]
else:
estimators = [('lr__', LinearRegression())]
ensemble = Ensemble(estimators=estimators)
err_msg = r"Estimator names must not contain __: got \['lr__'\]"
with pytest.raises(ValueError, match=err_msg):
ensemble.fit(X, y)
# raise an error when the name is not unique
if issubclass(Ensemble, ClassifierMixin):
estimators = [('lr', LogisticRegression()),
('lr', LogisticRegression())]
else:
estimators = [('lr', LinearRegression()),
('lr', LinearRegression())]
ensemble = Ensemble(estimators=estimators)
err_msg = r"Names provided are not unique: \['lr', 'lr'\]"
with pytest.raises(ValueError, match=err_msg):
ensemble.fit(X, y)
# raise an error when the name conflicts with the parameters
if issubclass(Ensemble, ClassifierMixin):
estimators = [('estimators', LogisticRegression())]
else:
estimators = [('estimators', LinearRegression())]
ensemble = Ensemble(estimators=estimators)
err_msg = "Estimator names conflict with constructor arguments"
with pytest.raises(ValueError, match=err_msg):
ensemble.fit(X, y)
@pytest.mark.parametrize(
"X, y, estimator",
[(*make_classification(n_samples=10),
StackingClassifier(estimators=[('lr', LogisticRegression())])),
(*make_classification(n_samples=10),
VotingClassifier(estimators=[('lr', LogisticRegression())])),
(*make_regression(n_samples=10),
StackingRegressor(estimators=[('lr', LinearRegression())])),
(*make_regression(n_samples=10),
VotingRegressor(estimators=[('lr', LinearRegression())]))],
ids=['stacking-classifier', 'voting-classifier',
'stacking-regressor', 'voting-regressor']
)
def test_ensemble_heterogeneous_estimators_all_dropped(X, y, estimator):
# check that we raise a consistent error when all estimators are
# dropped
estimator.set_params(lr='drop')
with pytest.raises(ValueError, match="All estimators are dropped."):
estimator.fit(X, y)
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