263 lines
8.9 KiB
Python
263 lines
8.9 KiB
Python
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import numpy as np
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import pytest
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from sklearn.base import ClassifierMixin, clone, is_classifier
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from sklearn.datasets import (
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load_diabetes,
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load_iris,
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make_classification,
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make_regression,
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)
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from sklearn.ensemble import (
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RandomForestClassifier,
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RandomForestRegressor,
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StackingClassifier,
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StackingRegressor,
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VotingClassifier,
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VotingRegressor,
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)
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from sklearn.impute import SimpleImputer
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from sklearn.linear_model import LinearRegression, LogisticRegression
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from sklearn.pipeline import make_pipeline
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from sklearn.svm import SVC, SVR, LinearSVC, LinearSVR
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X, y = load_iris(return_X_y=True)
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X_r, y_r = load_diabetes(return_X_y=True)
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@pytest.mark.parametrize(
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"X, y, estimator",
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[
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(
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*make_classification(n_samples=10),
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StackingClassifier(
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estimators=[
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("lr", LogisticRegression()),
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("svm", LinearSVC()),
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("rf", RandomForestClassifier(n_estimators=5, max_depth=3)),
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],
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cv=2,
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),
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),
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(
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*make_classification(n_samples=10),
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VotingClassifier(
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estimators=[
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("lr", LogisticRegression()),
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("svm", LinearSVC()),
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("rf", RandomForestClassifier(n_estimators=5, max_depth=3)),
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]
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),
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),
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(
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*make_regression(n_samples=10),
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StackingRegressor(
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estimators=[
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("lr", LinearRegression()),
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("svm", LinearSVR()),
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("rf", RandomForestRegressor(n_estimators=5, max_depth=3)),
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],
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cv=2,
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),
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),
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(
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*make_regression(n_samples=10),
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VotingRegressor(
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estimators=[
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("lr", LinearRegression()),
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("svm", LinearSVR()),
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("rf", RandomForestRegressor(n_estimators=5, max_depth=3)),
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]
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),
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),
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],
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ids=[
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"stacking-classifier",
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"voting-classifier",
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"stacking-regressor",
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"voting-regressor",
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],
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)
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def test_ensemble_heterogeneous_estimators_behavior(X, y, estimator):
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# check that the behavior of `estimators`, `estimators_`,
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# `named_estimators`, `named_estimators_` is consistent across all
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# ensemble classes and when using `set_params()`.
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# before fit
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assert "svm" in estimator.named_estimators
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assert estimator.named_estimators.svm is estimator.estimators[1][1]
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assert estimator.named_estimators.svm is estimator.named_estimators["svm"]
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# check fitted attributes
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estimator.fit(X, y)
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assert len(estimator.named_estimators) == 3
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assert len(estimator.named_estimators_) == 3
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assert sorted(list(estimator.named_estimators_.keys())) == sorted(
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["lr", "svm", "rf"]
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)
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# check that set_params() does not add a new attribute
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estimator_new_params = clone(estimator)
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svm_estimator = SVC() if is_classifier(estimator) else SVR()
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estimator_new_params.set_params(svm=svm_estimator).fit(X, y)
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assert not hasattr(estimator_new_params, "svm")
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assert (
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estimator_new_params.named_estimators.lr.get_params()
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== estimator.named_estimators.lr.get_params()
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)
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assert (
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estimator_new_params.named_estimators.rf.get_params()
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== estimator.named_estimators.rf.get_params()
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)
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# check the behavior when setting an dropping an estimator
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estimator_dropped = clone(estimator)
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estimator_dropped.set_params(svm="drop")
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estimator_dropped.fit(X, y)
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assert len(estimator_dropped.named_estimators) == 3
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assert estimator_dropped.named_estimators.svm == "drop"
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assert len(estimator_dropped.named_estimators_) == 3
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assert sorted(list(estimator_dropped.named_estimators_.keys())) == sorted(
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["lr", "svm", "rf"]
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)
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for sub_est in estimator_dropped.named_estimators_:
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# check that the correspondence is correct
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assert not isinstance(sub_est, type(estimator.named_estimators.svm))
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# check that we can set the parameters of the underlying classifier
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estimator.set_params(svm__C=10.0)
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estimator.set_params(rf__max_depth=5)
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assert (
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estimator.get_params()["svm__C"]
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== estimator.get_params()["svm"].get_params()["C"]
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)
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assert (
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estimator.get_params()["rf__max_depth"]
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== estimator.get_params()["rf"].get_params()["max_depth"]
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)
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@pytest.mark.parametrize(
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"Ensemble",
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[VotingClassifier, StackingRegressor, VotingRegressor],
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)
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def test_ensemble_heterogeneous_estimators_type(Ensemble):
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# check that ensemble will fail during validation if the underlying
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# estimators are not of the same type (i.e. classifier or regressor)
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# StackingClassifier can have an underlying regresor so it's not checked
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if issubclass(Ensemble, ClassifierMixin):
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X, y = make_classification(n_samples=10)
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estimators = [("lr", LinearRegression())]
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ensemble_type = "classifier"
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else:
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X, y = make_regression(n_samples=10)
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estimators = [("lr", LogisticRegression())]
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ensemble_type = "regressor"
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ensemble = Ensemble(estimators=estimators)
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err_msg = "should be a {}".format(ensemble_type)
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with pytest.raises(ValueError, match=err_msg):
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ensemble.fit(X, y)
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@pytest.mark.parametrize(
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"X, y, Ensemble",
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[
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(*make_classification(n_samples=10), StackingClassifier),
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(*make_classification(n_samples=10), VotingClassifier),
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(*make_regression(n_samples=10), StackingRegressor),
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(*make_regression(n_samples=10), VotingRegressor),
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],
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)
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def test_ensemble_heterogeneous_estimators_name_validation(X, y, Ensemble):
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# raise an error when the name contains dunder
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if issubclass(Ensemble, ClassifierMixin):
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estimators = [("lr__", LogisticRegression())]
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else:
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estimators = [("lr__", LinearRegression())]
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ensemble = Ensemble(estimators=estimators)
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err_msg = r"Estimator names must not contain __: got \['lr__'\]"
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with pytest.raises(ValueError, match=err_msg):
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ensemble.fit(X, y)
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# raise an error when the name is not unique
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if issubclass(Ensemble, ClassifierMixin):
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estimators = [("lr", LogisticRegression()), ("lr", LogisticRegression())]
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else:
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estimators = [("lr", LinearRegression()), ("lr", LinearRegression())]
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ensemble = Ensemble(estimators=estimators)
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err_msg = r"Names provided are not unique: \['lr', 'lr'\]"
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with pytest.raises(ValueError, match=err_msg):
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ensemble.fit(X, y)
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# raise an error when the name conflicts with the parameters
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if issubclass(Ensemble, ClassifierMixin):
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estimators = [("estimators", LogisticRegression())]
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else:
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estimators = [("estimators", LinearRegression())]
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ensemble = Ensemble(estimators=estimators)
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err_msg = "Estimator names conflict with constructor arguments"
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with pytest.raises(ValueError, match=err_msg):
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ensemble.fit(X, y)
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@pytest.mark.parametrize(
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"X, y, estimator",
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[
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(
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*make_classification(n_samples=10),
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StackingClassifier(estimators=[("lr", LogisticRegression())]),
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),
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(
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*make_classification(n_samples=10),
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VotingClassifier(estimators=[("lr", LogisticRegression())]),
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),
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(
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*make_regression(n_samples=10),
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StackingRegressor(estimators=[("lr", LinearRegression())]),
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),
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(
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*make_regression(n_samples=10),
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VotingRegressor(estimators=[("lr", LinearRegression())]),
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),
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],
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ids=[
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"stacking-classifier",
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"voting-classifier",
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"stacking-regressor",
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"voting-regressor",
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],
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)
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def test_ensemble_heterogeneous_estimators_all_dropped(X, y, estimator):
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# check that we raise a consistent error when all estimators are
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# dropped
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estimator.set_params(lr="drop")
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with pytest.raises(ValueError, match="All estimators are dropped."):
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estimator.fit(X, y)
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@pytest.mark.parametrize(
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"Ensemble, Estimator, X, y",
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[
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(StackingClassifier, LogisticRegression, X, y),
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(StackingRegressor, LinearRegression, X_r, y_r),
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(VotingClassifier, LogisticRegression, X, y),
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(VotingRegressor, LinearRegression, X_r, y_r),
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],
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)
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# FIXME: we should move this test in `estimator_checks` once we are able
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# to construct meta-estimator instances
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def test_heterogeneous_ensemble_support_missing_values(Ensemble, Estimator, X, y):
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# check that Voting and Stacking predictor delegate the missing values
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# validation to the underlying estimator.
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X = X.copy()
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mask = np.random.choice([1, 0], X.shape, p=[0.1, 0.9]).astype(bool)
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X[mask] = np.nan
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pipe = make_pipeline(SimpleImputer(), Estimator())
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ensemble = Ensemble(estimators=[("pipe1", pipe), ("pipe2", pipe)])
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ensemble.fit(X, y).score(X, y)
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