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import numpy as np
import pytest
from sklearn.base import ClassifierMixin, clone, is_classifier
from sklearn.datasets import (
load_diabetes,
load_iris,
make_classification,
make_regression,
)
from sklearn.ensemble import (
RandomForestClassifier,
RandomForestRegressor,
StackingClassifier,
StackingRegressor,
VotingClassifier,
VotingRegressor,
)
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.svm import SVC, SVR, LinearSVC, LinearSVR
X, y = load_iris(return_X_y=True)
X_r, y_r = load_diabetes(return_X_y=True)
@pytest.mark.parametrize(
"X, y, estimator",
[
(
*make_classification(n_samples=10),
StackingClassifier(
estimators=[
("lr", LogisticRegression()),
("svm", LinearSVC()),
("rf", RandomForestClassifier(n_estimators=5, max_depth=3)),
],
cv=2,
),
),
(
*make_classification(n_samples=10),
VotingClassifier(
estimators=[
("lr", LogisticRegression()),
("svm", LinearSVC()),
("rf", RandomForestClassifier(n_estimators=5, max_depth=3)),
]
),
),
(
*make_regression(n_samples=10),
StackingRegressor(
estimators=[
("lr", LinearRegression()),
("svm", LinearSVR()),
("rf", RandomForestRegressor(n_estimators=5, max_depth=3)),
],
cv=2,
),
),
(
*make_regression(n_samples=10),
VotingRegressor(
estimators=[
("lr", LinearRegression()),
("svm", LinearSVR()),
("rf", RandomForestRegressor(n_estimators=5, max_depth=3)),
]
),
),
],
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",
[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)
# StackingClassifier can have an underlying regresor so it's not checked
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)
@pytest.mark.parametrize(
"Ensemble, Estimator, X, y",
[
(StackingClassifier, LogisticRegression, X, y),
(StackingRegressor, LinearRegression, X_r, y_r),
(VotingClassifier, LogisticRegression, X, y),
(VotingRegressor, LinearRegression, X_r, y_r),
],
)
# FIXME: we should move this test in `estimator_checks` once we are able
# to construct meta-estimator instances
def test_heterogeneous_ensemble_support_missing_values(Ensemble, Estimator, X, y):
# check that Voting and Stacking predictor delegate the missing values
# validation to the underlying estimator.
X = X.copy()
mask = np.random.choice([1, 0], X.shape, p=[0.1, 0.9]).astype(bool)
X[mask] = np.nan
pipe = make_pipeline(SimpleImputer(), Estimator())
ensemble = Ensemble(estimators=[("pipe1", pipe), ("pipe2", pipe)])
ensemble.fit(X, y).score(X, y)
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