Lightstar/venv/Lib/site-packages/sklearn/ensemble/tests/test_weight_boosting.py

"""Testing for the boost module (sklearn.ensemble.boost)."""

import re

import numpy as np
import pytest
from scipy.sparse import coo_matrix, csc_matrix, csr_matrix, dok_matrix, lil_matrix

from sklearn import datasets
from sklearn.base import BaseEstimator, clone
from sklearn.dummy import DummyClassifier, DummyRegressor
from sklearn.ensemble import AdaBoostClassifier, AdaBoostRegressor
from sklearn.ensemble._weight_boosting import _samme_proba
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.svm import SVC, SVR
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.utils import shuffle
from sklearn.utils._mocking import NoSampleWeightWrapper
from sklearn.utils._testing import (
    assert_allclose,
    assert_array_almost_equal,
    assert_array_equal,
    assert_array_less,
)

# Common random state
rng = np.random.RandomState(0)

# Toy sample
X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
y_class = ["foo", "foo", "foo", 1, 1, 1]  # test string class labels
y_regr = [-1, -1, -1, 1, 1, 1]
T = [[-1, -1], [2, 2], [3, 2]]
y_t_class = ["foo", 1, 1]
y_t_regr = [-1, 1, 1]

# Load the iris dataset and randomly permute it
iris = datasets.load_iris()
perm = rng.permutation(iris.target.size)
iris.data, iris.target = shuffle(iris.data, iris.target, random_state=rng)

# Load the diabetes dataset and randomly permute it
diabetes = datasets.load_diabetes()
diabetes.data, diabetes.target = shuffle(
    diabetes.data, diabetes.target, random_state=rng
)


def test_samme_proba():
    # Test the `_samme_proba` helper function.

    # Define some example (bad) `predict_proba` output.
    probs = np.array(
        [[1, 1e-6, 0], [0.19, 0.6, 0.2], [-999, 0.51, 0.5], [1e-6, 1, 1e-9]]
    )
    probs /= np.abs(probs.sum(axis=1))[:, np.newaxis]

    # _samme_proba calls estimator.predict_proba.
    # Make a mock object so I can control what gets returned.
    class MockEstimator:
        def predict_proba(self, X):
            assert_array_equal(X.shape, probs.shape)
            return probs

    mock = MockEstimator()

    samme_proba = _samme_proba(mock, 3, np.ones_like(probs))

    assert_array_equal(samme_proba.shape, probs.shape)
    assert np.isfinite(samme_proba).all()

    # Make sure that the correct elements come out as smallest --
    # `_samme_proba` should preserve the ordering in each example.
    assert_array_equal(np.argmin(samme_proba, axis=1), [2, 0, 0, 2])
    assert_array_equal(np.argmax(samme_proba, axis=1), [0, 1, 1, 1])


def test_oneclass_adaboost_proba():
    # Test predict_proba robustness for one class label input.
    # In response to issue #7501
    # https://github.com/scikit-learn/scikit-learn/issues/7501
    y_t = np.ones(len(X))
    clf = AdaBoostClassifier().fit(X, y_t)
    assert_array_almost_equal(clf.predict_proba(X), np.ones((len(X), 1)))


@pytest.mark.parametrize("algorithm", ["SAMME", "SAMME.R"])
def test_classification_toy(algorithm):
    # Check classification on a toy dataset.
    clf = AdaBoostClassifier(algorithm=algorithm, random_state=0)
    clf.fit(X, y_class)
    assert_array_equal(clf.predict(T), y_t_class)
    assert_array_equal(np.unique(np.asarray(y_t_class)), clf.classes_)
    assert clf.predict_proba(T).shape == (len(T), 2)
    assert clf.decision_function(T).shape == (len(T),)


def test_regression_toy():
    # Check classification on a toy dataset.
    clf = AdaBoostRegressor(random_state=0)
    clf.fit(X, y_regr)
    assert_array_equal(clf.predict(T), y_t_regr)


def test_iris():
    # Check consistency on dataset iris.
    classes = np.unique(iris.target)
    clf_samme = prob_samme = None

    for alg in ["SAMME", "SAMME.R"]:
        clf = AdaBoostClassifier(algorithm=alg)
        clf.fit(iris.data, iris.target)

        assert_array_equal(classes, clf.classes_)
        proba = clf.predict_proba(iris.data)
        if alg == "SAMME":
            clf_samme = clf
            prob_samme = proba
        assert proba.shape[1] == len(classes)
        assert clf.decision_function(iris.data).shape[1] == len(classes)

        score = clf.score(iris.data, iris.target)
        assert score > 0.9, "Failed with algorithm %s and score = %f" % (alg, score)

        # Check we used multiple estimators
        assert len(clf.estimators_) > 1
        # Check for distinct random states (see issue #7408)
        assert len(set(est.random_state for est in clf.estimators_)) == len(
            clf.estimators_
        )

    # Somewhat hacky regression test: prior to
    # ae7adc880d624615a34bafdb1d75ef67051b8200,
    # predict_proba returned SAMME.R values for SAMME.
    clf_samme.algorithm = "SAMME.R"
    assert_array_less(0, np.abs(clf_samme.predict_proba(iris.data) - prob_samme))


@pytest.mark.parametrize("loss", ["linear", "square", "exponential"])
def test_diabetes(loss):
    # Check consistency on dataset diabetes.
    reg = AdaBoostRegressor(loss=loss, random_state=0)
    reg.fit(diabetes.data, diabetes.target)
    score = reg.score(diabetes.data, diabetes.target)
    assert score > 0.55

    # Check we used multiple estimators
    assert len(reg.estimators_) > 1
    # Check for distinct random states (see issue #7408)
    assert len(set(est.random_state for est in reg.estimators_)) == len(reg.estimators_)


@pytest.mark.parametrize("algorithm", ["SAMME", "SAMME.R"])
def test_staged_predict(algorithm):
    # Check staged predictions.
    rng = np.random.RandomState(0)
    iris_weights = rng.randint(10, size=iris.target.shape)
    diabetes_weights = rng.randint(10, size=diabetes.target.shape)

    clf = AdaBoostClassifier(algorithm=algorithm, n_estimators=10)
    clf.fit(iris.data, iris.target, sample_weight=iris_weights)

    predictions = clf.predict(iris.data)
    staged_predictions = [p for p in clf.staged_predict(iris.data)]
    proba = clf.predict_proba(iris.data)
    staged_probas = [p for p in clf.staged_predict_proba(iris.data)]
    score = clf.score(iris.data, iris.target, sample_weight=iris_weights)
    staged_scores = [
        s for s in clf.staged_score(iris.data, iris.target, sample_weight=iris_weights)
    ]

    assert len(staged_predictions) == 10
    assert_array_almost_equal(predictions, staged_predictions[-1])
    assert len(staged_probas) == 10
    assert_array_almost_equal(proba, staged_probas[-1])
    assert len(staged_scores) == 10
    assert_array_almost_equal(score, staged_scores[-1])

    # AdaBoost regression
    clf = AdaBoostRegressor(n_estimators=10, random_state=0)
    clf.fit(diabetes.data, diabetes.target, sample_weight=diabetes_weights)

    predictions = clf.predict(diabetes.data)
    staged_predictions = [p for p in clf.staged_predict(diabetes.data)]
    score = clf.score(diabetes.data, diabetes.target, sample_weight=diabetes_weights)
    staged_scores = [
        s
        for s in clf.staged_score(
            diabetes.data, diabetes.target, sample_weight=diabetes_weights
        )
    ]

    assert len(staged_predictions) == 10
    assert_array_almost_equal(predictions, staged_predictions[-1])
    assert len(staged_scores) == 10
    assert_array_almost_equal(score, staged_scores[-1])


def test_gridsearch():
    # Check that base trees can be grid-searched.
    # AdaBoost classification
    boost = AdaBoostClassifier(estimator=DecisionTreeClassifier())
    parameters = {
        "n_estimators": (1, 2),
        "estimator__max_depth": (1, 2),
        "algorithm": ("SAMME", "SAMME.R"),
    }
    clf = GridSearchCV(boost, parameters)
    clf.fit(iris.data, iris.target)

    # AdaBoost regression
    boost = AdaBoostRegressor(estimator=DecisionTreeRegressor(), random_state=0)
    parameters = {"n_estimators": (1, 2), "estimator__max_depth": (1, 2)}
    clf = GridSearchCV(boost, parameters)
    clf.fit(diabetes.data, diabetes.target)


def test_pickle():
    # Check pickability.
    import pickle

    # Adaboost classifier
    for alg in ["SAMME", "SAMME.R"]:
        obj = AdaBoostClassifier(algorithm=alg)
        obj.fit(iris.data, iris.target)
        score = obj.score(iris.data, iris.target)
        s = pickle.dumps(obj)

        obj2 = pickle.loads(s)
        assert type(obj2) == obj.__class__
        score2 = obj2.score(iris.data, iris.target)
        assert score == score2

    # Adaboost regressor
    obj = AdaBoostRegressor(random_state=0)
    obj.fit(diabetes.data, diabetes.target)
    score = obj.score(diabetes.data, diabetes.target)
    s = pickle.dumps(obj)

    obj2 = pickle.loads(s)
    assert type(obj2) == obj.__class__
    score2 = obj2.score(diabetes.data, diabetes.target)
    assert score == score2


def test_importances():
    # Check variable importances.
    X, y = datasets.make_classification(
        n_samples=2000,
        n_features=10,
        n_informative=3,
        n_redundant=0,
        n_repeated=0,
        shuffle=False,
        random_state=1,
    )

    for alg in ["SAMME", "SAMME.R"]:
        clf = AdaBoostClassifier(algorithm=alg)

        clf.fit(X, y)
        importances = clf.feature_importances_

        assert importances.shape[0] == 10
        assert (importances[:3, np.newaxis] >= importances[3:]).all()


def test_adaboost_classifier_sample_weight_error():
    # Test that it gives proper exception on incorrect sample weight.
    clf = AdaBoostClassifier()
    msg = re.escape("sample_weight.shape == (1,), expected (6,)")
    with pytest.raises(ValueError, match=msg):
        clf.fit(X, y_class, sample_weight=np.asarray([-1]))


def test_estimator():
    # Test different estimators.
    from sklearn.ensemble import RandomForestClassifier

    # XXX doesn't work with y_class because RF doesn't support classes_
    # Shouldn't AdaBoost run a LabelBinarizer?
    clf = AdaBoostClassifier(RandomForestClassifier())
    clf.fit(X, y_regr)

    clf = AdaBoostClassifier(SVC(), algorithm="SAMME")
    clf.fit(X, y_class)

    from sklearn.ensemble import RandomForestRegressor

    clf = AdaBoostRegressor(RandomForestRegressor(), random_state=0)
    clf.fit(X, y_regr)

    clf = AdaBoostRegressor(SVR(), random_state=0)
    clf.fit(X, y_regr)

    # Check that an empty discrete ensemble fails in fit, not predict.
    X_fail = [[1, 1], [1, 1], [1, 1], [1, 1]]
    y_fail = ["foo", "bar", 1, 2]
    clf = AdaBoostClassifier(SVC(), algorithm="SAMME")
    with pytest.raises(ValueError, match="worse than random"):
        clf.fit(X_fail, y_fail)


def test_sample_weights_infinite():
    msg = "Sample weights have reached infinite values"
    clf = AdaBoostClassifier(n_estimators=30, learning_rate=23.0, algorithm="SAMME")
    with pytest.warns(UserWarning, match=msg):
        clf.fit(iris.data, iris.target)


def test_sparse_classification():
    # Check classification with sparse input.

    class CustomSVC(SVC):
        """SVC variant that records the nature of the training set."""

        def fit(self, X, y, sample_weight=None):
            """Modification on fit caries data type for later verification."""
            super().fit(X, y, sample_weight=sample_weight)
            self.data_type_ = type(X)
            return self

    X, y = datasets.make_multilabel_classification(
        n_classes=1, n_samples=15, n_features=5, random_state=42
    )
    # Flatten y to a 1d array
    y = np.ravel(y)

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    for sparse_format in [csc_matrix, csr_matrix, lil_matrix, coo_matrix, dok_matrix]:
        X_train_sparse = sparse_format(X_train)
        X_test_sparse = sparse_format(X_test)

        # Trained on sparse format
        sparse_classifier = AdaBoostClassifier(
            estimator=CustomSVC(probability=True),
            random_state=1,
            algorithm="SAMME",
        ).fit(X_train_sparse, y_train)

        # Trained on dense format
        dense_classifier = AdaBoostClassifier(
            estimator=CustomSVC(probability=True),
            random_state=1,
            algorithm="SAMME",
        ).fit(X_train, y_train)

        # predict
        sparse_results = sparse_classifier.predict(X_test_sparse)
        dense_results = dense_classifier.predict(X_test)
        assert_array_equal(sparse_results, dense_results)

        # decision_function
        sparse_results = sparse_classifier.decision_function(X_test_sparse)
        dense_results = dense_classifier.decision_function(X_test)
        assert_array_almost_equal(sparse_results, dense_results)

        # predict_log_proba
        sparse_results = sparse_classifier.predict_log_proba(X_test_sparse)
        dense_results = dense_classifier.predict_log_proba(X_test)
        assert_array_almost_equal(sparse_results, dense_results)

        # predict_proba
        sparse_results = sparse_classifier.predict_proba(X_test_sparse)
        dense_results = dense_classifier.predict_proba(X_test)
        assert_array_almost_equal(sparse_results, dense_results)

        # score
        sparse_results = sparse_classifier.score(X_test_sparse, y_test)
        dense_results = dense_classifier.score(X_test, y_test)
        assert_array_almost_equal(sparse_results, dense_results)

        # staged_decision_function
        sparse_results = sparse_classifier.staged_decision_function(X_test_sparse)
        dense_results = dense_classifier.staged_decision_function(X_test)
        for sprase_res, dense_res in zip(sparse_results, dense_results):
            assert_array_almost_equal(sprase_res, dense_res)

        # staged_predict
        sparse_results = sparse_classifier.staged_predict(X_test_sparse)
        dense_results = dense_classifier.staged_predict(X_test)
        for sprase_res, dense_res in zip(sparse_results, dense_results):
            assert_array_equal(sprase_res, dense_res)

        # staged_predict_proba
        sparse_results = sparse_classifier.staged_predict_proba(X_test_sparse)
        dense_results = dense_classifier.staged_predict_proba(X_test)
        for sprase_res, dense_res in zip(sparse_results, dense_results):
            assert_array_almost_equal(sprase_res, dense_res)

        # staged_score
        sparse_results = sparse_classifier.staged_score(X_test_sparse, y_test)
        dense_results = dense_classifier.staged_score(X_test, y_test)
        for sprase_res, dense_res in zip(sparse_results, dense_results):
            assert_array_equal(sprase_res, dense_res)

        # Verify sparsity of data is maintained during training
        types = [i.data_type_ for i in sparse_classifier.estimators_]

        assert all([(t == csc_matrix or t == csr_matrix) for t in types])


def test_sparse_regression():
    # Check regression with sparse input.

    class CustomSVR(SVR):
        """SVR variant that records the nature of the training set."""

        def fit(self, X, y, sample_weight=None):
            """Modification on fit caries data type for later verification."""
            super().fit(X, y, sample_weight=sample_weight)
            self.data_type_ = type(X)
            return self

    X, y = datasets.make_regression(
        n_samples=15, n_features=50, n_targets=1, random_state=42
    )

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    for sparse_format in [csc_matrix, csr_matrix, lil_matrix, coo_matrix, dok_matrix]:
        X_train_sparse = sparse_format(X_train)
        X_test_sparse = sparse_format(X_test)

        # Trained on sparse format
        sparse_classifier = AdaBoostRegressor(
            estimator=CustomSVR(), random_state=1
        ).fit(X_train_sparse, y_train)

        # Trained on dense format
        dense_classifier = dense_results = AdaBoostRegressor(
            estimator=CustomSVR(), random_state=1
        ).fit(X_train, y_train)

        # predict
        sparse_results = sparse_classifier.predict(X_test_sparse)
        dense_results = dense_classifier.predict(X_test)
        assert_array_almost_equal(sparse_results, dense_results)

        # staged_predict
        sparse_results = sparse_classifier.staged_predict(X_test_sparse)
        dense_results = dense_classifier.staged_predict(X_test)
        for sprase_res, dense_res in zip(sparse_results, dense_results):
            assert_array_almost_equal(sprase_res, dense_res)

        types = [i.data_type_ for i in sparse_classifier.estimators_]

        assert all([(t == csc_matrix or t == csr_matrix) for t in types])


def test_sample_weight_adaboost_regressor():
    """
    AdaBoostRegressor should work without sample_weights in the base estimator
    The random weighted sampling is done internally in the _boost method in
    AdaBoostRegressor.
    """

    class DummyEstimator(BaseEstimator):
        def fit(self, X, y):
            pass

        def predict(self, X):
            return np.zeros(X.shape[0])

    boost = AdaBoostRegressor(DummyEstimator(), n_estimators=3)
    boost.fit(X, y_regr)
    assert len(boost.estimator_weights_) == len(boost.estimator_errors_)


def test_multidimensional_X():
    """
    Check that the AdaBoost estimators can work with n-dimensional
    data matrix
    """
    rng = np.random.RandomState(0)

    X = rng.randn(51, 3, 3)
    yc = rng.choice([0, 1], 51)
    yr = rng.randn(51)

    boost = AdaBoostClassifier(DummyClassifier(strategy="most_frequent"))
    boost.fit(X, yc)
    boost.predict(X)
    boost.predict_proba(X)

    boost = AdaBoostRegressor(DummyRegressor())
    boost.fit(X, yr)
    boost.predict(X)


@pytest.mark.parametrize("algorithm", ["SAMME", "SAMME.R"])
def test_adaboostclassifier_without_sample_weight(algorithm):
    X, y = iris.data, iris.target
    estimator = NoSampleWeightWrapper(DummyClassifier())
    clf = AdaBoostClassifier(estimator=estimator, algorithm=algorithm)
    err_msg = "{} doesn't support sample_weight".format(estimator.__class__.__name__)
    with pytest.raises(ValueError, match=err_msg):
        clf.fit(X, y)


def test_adaboostregressor_sample_weight():
    # check that giving weight will have an influence on the error computed
    # for a weak learner
    rng = np.random.RandomState(42)
    X = np.linspace(0, 100, num=1000)
    y = (0.8 * X + 0.2) + (rng.rand(X.shape[0]) * 0.0001)
    X = X.reshape(-1, 1)

    # add an arbitrary outlier
    X[-1] *= 10
    y[-1] = 10000

    # random_state=0 ensure that the underlying bootstrap will use the outlier
    regr_no_outlier = AdaBoostRegressor(
        estimator=LinearRegression(), n_estimators=1, random_state=0
    )
    regr_with_weight = clone(regr_no_outlier)
    regr_with_outlier = clone(regr_no_outlier)

    # fit 3 models:
    # - a model containing the outlier
    # - a model without the outlier
    # - a model containing the outlier but with a null sample-weight
    regr_with_outlier.fit(X, y)
    regr_no_outlier.fit(X[:-1], y[:-1])
    sample_weight = np.ones_like(y)
    sample_weight[-1] = 0
    regr_with_weight.fit(X, y, sample_weight=sample_weight)

    score_with_outlier = regr_with_outlier.score(X[:-1], y[:-1])
    score_no_outlier = regr_no_outlier.score(X[:-1], y[:-1])
    score_with_weight = regr_with_weight.score(X[:-1], y[:-1])

    assert score_with_outlier < score_no_outlier
    assert score_with_outlier < score_with_weight
    assert score_no_outlier == pytest.approx(score_with_weight)


@pytest.mark.parametrize("algorithm", ["SAMME", "SAMME.R"])
def test_adaboost_consistent_predict(algorithm):
    # check that predict_proba and predict give consistent results
    # regression test for:
    # https://github.com/scikit-learn/scikit-learn/issues/14084
    X_train, X_test, y_train, y_test = train_test_split(
        *datasets.load_digits(return_X_y=True), random_state=42
    )
    model = AdaBoostClassifier(algorithm=algorithm, random_state=42)
    model.fit(X_train, y_train)

    assert_array_equal(
        np.argmax(model.predict_proba(X_test), axis=1), model.predict(X_test)
    )


@pytest.mark.parametrize(
    "model, X, y",
    [
        (AdaBoostClassifier(), iris.data, iris.target),
        (AdaBoostRegressor(), diabetes.data, diabetes.target),
    ],
)
def test_adaboost_negative_weight_error(model, X, y):
    sample_weight = np.ones_like(y)
    sample_weight[-1] = -10

    err_msg = "Negative values in data passed to `sample_weight`"
    with pytest.raises(ValueError, match=err_msg):
        model.fit(X, y, sample_weight=sample_weight)


def test_adaboost_numerically_stable_feature_importance_with_small_weights():
    """Check that we don't create NaN feature importance with numerically
    instable inputs.

    Non-regression test for:
    https://github.com/scikit-learn/scikit-learn/issues/20320
    """
    rng = np.random.RandomState(42)
    X = rng.normal(size=(1000, 10))
    y = rng.choice([0, 1], size=1000)
    sample_weight = np.ones_like(y) * 1e-263
    tree = DecisionTreeClassifier(max_depth=10, random_state=12)
    ada_model = AdaBoostClassifier(estimator=tree, n_estimators=20, random_state=12)
    ada_model.fit(X, y, sample_weight=sample_weight)
    assert np.isnan(ada_model.feature_importances_).sum() == 0


# TODO(1.4): remove in 1.4
@pytest.mark.parametrize(
    "AdaBoost, Estimator",
    [
        (AdaBoostClassifier, DecisionTreeClassifier),
        (AdaBoostRegressor, DecisionTreeRegressor),
    ],
)
def test_base_estimator_argument_deprecated(AdaBoost, Estimator):
    X = np.array([[1, 2], [3, 4]])
    y = np.array([1, 0])
    model = AdaBoost(base_estimator=Estimator())

    warn_msg = (
        "`base_estimator` was renamed to `estimator` in version 1.2 and "
        "will be removed in 1.4."
    )
    with pytest.warns(FutureWarning, match=warn_msg):
        model.fit(X, y)


# TODO(1.4): remove in 1.4
@pytest.mark.parametrize(
    "AdaBoost",
    [
        AdaBoostClassifier,
        AdaBoostRegressor,
    ],
)
def test_base_estimator_argument_deprecated_none(AdaBoost):
    X = np.array([[1, 2], [3, 4]])
    y = np.array([1, 0])
    model = AdaBoost(base_estimator=None)

    warn_msg = (
        "`base_estimator` was renamed to `estimator` in version 1.2 and "
        "will be removed in 1.4."
    )
    with pytest.warns(FutureWarning, match=warn_msg):
        model.fit(X, y)


# TODO(1.4): remove in 1.4
@pytest.mark.parametrize(
    "AdaBoost",
    [AdaBoostClassifier, AdaBoostRegressor],
)
def test_base_estimator_property_deprecated(AdaBoost):
    X = np.array([[1, 2], [3, 4]])
    y = np.array([1, 0])
    model = AdaBoost()
    model.fit(X, y)

    warn_msg = (
        "Attribute `base_estimator_` was deprecated in version 1.2 and "
        "will be removed in 1.4. Use `estimator_` instead."
    )
    with pytest.warns(FutureWarning, match=warn_msg):
        model.base_estimator_


# TODO(1.4): remove in 1.4
def test_deprecated_base_estimator_parameters_can_be_set():
    """Check that setting base_estimator parameters works.

    During the deprecation cycle setting "base_estimator__*" params should
    work.

    Non-regression test for https://github.com/scikit-learn/scikit-learn/issues/25470
    """
    # This implicitly sets "estimator", it is how old code (pre v1.2) would
    # have instantiated AdaBoostClassifier and back then it would set
    # "base_estimator".
    clf = AdaBoostClassifier(DecisionTreeClassifier())

    with pytest.warns(FutureWarning, match="Parameter 'base_estimator' of"):
        clf.set_params(base_estimator__max_depth=2)


@pytest.mark.parametrize("algorithm", ["SAMME", "SAMME.R"])
def test_adaboost_decision_function(algorithm, global_random_seed):
    """Check that the decision function respects the symmetric constraint for weak
    learners.

    Non-regression test for:
    https://github.com/scikit-learn/scikit-learn/issues/26520
    """
    n_classes = 3
    X, y = datasets.make_classification(
        n_classes=n_classes, n_clusters_per_class=1, random_state=global_random_seed
    )
    clf = AdaBoostClassifier(
        n_estimators=1, random_state=global_random_seed, algorithm=algorithm
    ).fit(X, y)

    y_score = clf.decision_function(X)
    assert_allclose(y_score.sum(axis=1), 0, atol=1e-8)

    if algorithm == "SAMME":
        # With a single learner, we expect to have a decision function in
        # {1, - 1 / (n_classes - 1)}.
        assert set(np.unique(y_score)) == {1, -1 / (n_classes - 1)}

    # We can assert the same for staged_decision_function since we have a single learner
    for y_score in clf.staged_decision_function(X):
        assert_allclose(y_score.sum(axis=1), 0, atol=1e-8)

        if algorithm == "SAMME":
            # With a single learner, we expect to have a decision function in
            # {1, - 1 / (n_classes - 1)}.
            assert set(np.unique(y_score)) == {1, -1 / (n_classes - 1)}

    clf.set_params(n_estimators=5).fit(X, y)

    y_score = clf.decision_function(X)
    assert_allclose(y_score.sum(axis=1), 0, atol=1e-8)

    for y_score in clf.staged_decision_function(X):
        assert_allclose(y_score.sum(axis=1), 0, atol=1e-8)