Lightstar/venv/Lib/site-packages/sklearn/tests/test_multioutput.py

import re

import numpy as np
import pytest
import scipy.sparse as sp
from joblib import cpu_count

from sklearn import datasets
from sklearn.base import ClassifierMixin, clone
from sklearn.datasets import (
    load_linnerud,
    make_classification,
    make_multilabel_classification,
    make_regression,
)
from sklearn.dummy import DummyClassifier, DummyRegressor
from sklearn.ensemble import (
    GradientBoostingRegressor,
    RandomForestClassifier,
    StackingRegressor,
)
from sklearn.exceptions import NotFittedError
from sklearn.impute import SimpleImputer
from sklearn.linear_model import (
    Lasso,
    LinearRegression,
    LogisticRegression,
    OrthogonalMatchingPursuit,
    PassiveAggressiveClassifier,
    Ridge,
    SGDClassifier,
    SGDRegressor,
)
from sklearn.metrics import jaccard_score, mean_squared_error
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.multiclass import OneVsRestClassifier
from sklearn.multioutput import (
    ClassifierChain,
    MultiOutputClassifier,
    MultiOutputRegressor,
    RegressorChain,
)
from sklearn.pipeline import make_pipeline
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.utils import shuffle
from sklearn.utils._testing import (
    assert_almost_equal,
    assert_array_almost_equal,
    assert_array_equal,
)


def test_multi_target_regression():
    X, y = datasets.make_regression(n_targets=3, random_state=0)
    X_train, y_train = X[:50], y[:50]
    X_test, y_test = X[50:], y[50:]

    references = np.zeros_like(y_test)
    for n in range(3):
        rgr = GradientBoostingRegressor(random_state=0)
        rgr.fit(X_train, y_train[:, n])
        references[:, n] = rgr.predict(X_test)

    rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
    rgr.fit(X_train, y_train)
    y_pred = rgr.predict(X_test)

    assert_almost_equal(references, y_pred)


def test_multi_target_regression_partial_fit():
    X, y = datasets.make_regression(n_targets=3, random_state=0)
    X_train, y_train = X[:50], y[:50]
    X_test, y_test = X[50:], y[50:]

    references = np.zeros_like(y_test)
    half_index = 25
    for n in range(3):
        sgr = SGDRegressor(random_state=0, max_iter=5)
        sgr.partial_fit(X_train[:half_index], y_train[:half_index, n])
        sgr.partial_fit(X_train[half_index:], y_train[half_index:, n])
        references[:, n] = sgr.predict(X_test)

    sgr = MultiOutputRegressor(SGDRegressor(random_state=0, max_iter=5))

    sgr.partial_fit(X_train[:half_index], y_train[:half_index])
    sgr.partial_fit(X_train[half_index:], y_train[half_index:])

    y_pred = sgr.predict(X_test)
    assert_almost_equal(references, y_pred)
    assert not hasattr(MultiOutputRegressor(Lasso), "partial_fit")


def test_multi_target_regression_one_target():
    # Test multi target regression raises
    X, y = datasets.make_regression(n_targets=1, random_state=0)
    rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
    msg = "at least two dimensions"
    with pytest.raises(ValueError, match=msg):
        rgr.fit(X, y)


def test_multi_target_sparse_regression():
    X, y = datasets.make_regression(n_targets=3, random_state=0)
    X_train, y_train = X[:50], y[:50]
    X_test = X[50:]

    for sparse in [
        sp.csr_matrix,
        sp.csc_matrix,
        sp.coo_matrix,
        sp.dok_matrix,
        sp.lil_matrix,
    ]:
        rgr = MultiOutputRegressor(Lasso(random_state=0))
        rgr_sparse = MultiOutputRegressor(Lasso(random_state=0))

        rgr.fit(X_train, y_train)
        rgr_sparse.fit(sparse(X_train), y_train)

        assert_almost_equal(rgr.predict(X_test), rgr_sparse.predict(sparse(X_test)))


def test_multi_target_sample_weights_api():
    X = [[1, 2, 3], [4, 5, 6]]
    y = [[3.141, 2.718], [2.718, 3.141]]
    w = [0.8, 0.6]

    rgr = MultiOutputRegressor(OrthogonalMatchingPursuit())
    msg = "does not support sample weights"
    with pytest.raises(ValueError, match=msg):
        rgr.fit(X, y, w)

    # no exception should be raised if the base estimator supports weights
    rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
    rgr.fit(X, y, w)


def test_multi_target_sample_weight_partial_fit():
    # weighted regressor
    X = [[1, 2, 3], [4, 5, 6]]
    y = [[3.141, 2.718], [2.718, 3.141]]
    w = [2.0, 1.0]
    rgr_w = MultiOutputRegressor(SGDRegressor(random_state=0, max_iter=5))
    rgr_w.partial_fit(X, y, w)

    # weighted with different weights
    w = [2.0, 2.0]
    rgr = MultiOutputRegressor(SGDRegressor(random_state=0, max_iter=5))
    rgr.partial_fit(X, y, w)

    assert rgr.predict(X)[0][0] != rgr_w.predict(X)[0][0]


def test_multi_target_sample_weights():
    # weighted regressor
    Xw = [[1, 2, 3], [4, 5, 6]]
    yw = [[3.141, 2.718], [2.718, 3.141]]
    w = [2.0, 1.0]
    rgr_w = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
    rgr_w.fit(Xw, yw, w)

    # unweighted, but with repeated samples
    X = [[1, 2, 3], [1, 2, 3], [4, 5, 6]]
    y = [[3.141, 2.718], [3.141, 2.718], [2.718, 3.141]]
    rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
    rgr.fit(X, y)

    X_test = [[1.5, 2.5, 3.5], [3.5, 4.5, 5.5]]
    assert_almost_equal(rgr.predict(X_test), rgr_w.predict(X_test))


# Import the data
iris = datasets.load_iris()
# create a multiple targets by randomized shuffling and concatenating y.
X = iris.data
y1 = iris.target
y2 = shuffle(y1, random_state=1)
y3 = shuffle(y1, random_state=2)
y = np.column_stack((y1, y2, y3))
n_samples, n_features = X.shape
n_outputs = y.shape[1]
n_classes = len(np.unique(y1))
classes = list(map(np.unique, (y1, y2, y3)))


def test_multi_output_classification_partial_fit_parallelism():
    sgd_linear_clf = SGDClassifier(loss="log_loss", random_state=1, max_iter=5)
    mor = MultiOutputClassifier(sgd_linear_clf, n_jobs=4)
    mor.partial_fit(X, y, classes)
    est1 = mor.estimators_[0]
    mor.partial_fit(X, y)
    est2 = mor.estimators_[0]
    if cpu_count() > 1:
        # parallelism requires this to be the case for a sane implementation
        assert est1 is not est2


# check multioutput has predict_proba
def test_hasattr_multi_output_predict_proba():
    # default SGDClassifier has loss='hinge'
    # which does not expose a predict_proba method
    sgd_linear_clf = SGDClassifier(random_state=1, max_iter=5)
    multi_target_linear = MultiOutputClassifier(sgd_linear_clf)
    multi_target_linear.fit(X, y)
    assert not hasattr(multi_target_linear, "predict_proba")

    # case where predict_proba attribute exists
    sgd_linear_clf = SGDClassifier(loss="log_loss", random_state=1, max_iter=5)
    multi_target_linear = MultiOutputClassifier(sgd_linear_clf)
    multi_target_linear.fit(X, y)
    assert hasattr(multi_target_linear, "predict_proba")


# check predict_proba passes
def test_multi_output_predict_proba():
    sgd_linear_clf = SGDClassifier(random_state=1, max_iter=5)
    param = {"loss": ("hinge", "log_loss", "modified_huber")}

    # inner function for custom scoring
    def custom_scorer(estimator, X, y):
        if hasattr(estimator, "predict_proba"):
            return 1.0
        else:
            return 0.0

    grid_clf = GridSearchCV(
        sgd_linear_clf,
        param_grid=param,
        scoring=custom_scorer,
        cv=3,
        error_score="raise",
    )
    multi_target_linear = MultiOutputClassifier(grid_clf)
    multi_target_linear.fit(X, y)

    multi_target_linear.predict_proba(X)

    # SGDClassifier defaults to loss='hinge' which is not a probabilistic
    # loss function; therefore it does not expose a predict_proba method
    sgd_linear_clf = SGDClassifier(random_state=1, max_iter=5)
    multi_target_linear = MultiOutputClassifier(sgd_linear_clf)
    multi_target_linear.fit(X, y)
    err_msg = "probability estimates are not available for loss='hinge'"
    with pytest.raises(AttributeError, match=err_msg):
        multi_target_linear.predict_proba(X)


def test_multi_output_classification_partial_fit():
    # test if multi_target initializes correctly with base estimator and fit
    # assert predictions work as expected for predict

    sgd_linear_clf = SGDClassifier(loss="log_loss", random_state=1, max_iter=5)
    multi_target_linear = MultiOutputClassifier(sgd_linear_clf)

    # train the multi_target_linear and also get the predictions.
    half_index = X.shape[0] // 2
    multi_target_linear.partial_fit(X[:half_index], y[:half_index], classes=classes)

    first_predictions = multi_target_linear.predict(X)
    assert (n_samples, n_outputs) == first_predictions.shape

    multi_target_linear.partial_fit(X[half_index:], y[half_index:])
    second_predictions = multi_target_linear.predict(X)
    assert (n_samples, n_outputs) == second_predictions.shape

    # train the linear classification with each column and assert that
    # predictions are equal after first partial_fit and second partial_fit
    for i in range(3):
        # create a clone with the same state
        sgd_linear_clf = clone(sgd_linear_clf)
        sgd_linear_clf.partial_fit(
            X[:half_index], y[:half_index, i], classes=classes[i]
        )
        assert_array_equal(sgd_linear_clf.predict(X), first_predictions[:, i])
        sgd_linear_clf.partial_fit(X[half_index:], y[half_index:, i])
        assert_array_equal(sgd_linear_clf.predict(X), second_predictions[:, i])


def test_multi_output_classification_partial_fit_no_first_classes_exception():
    sgd_linear_clf = SGDClassifier(loss="log_loss", random_state=1, max_iter=5)
    multi_target_linear = MultiOutputClassifier(sgd_linear_clf)
    msg = "classes must be passed on the first call to partial_fit."
    with pytest.raises(ValueError, match=msg):
        multi_target_linear.partial_fit(X, y)


def test_multi_output_classification():
    # test if multi_target initializes correctly with base estimator and fit
    # assert predictions work as expected for predict, prodict_proba and score

    forest = RandomForestClassifier(n_estimators=10, random_state=1)
    multi_target_forest = MultiOutputClassifier(forest)

    # train the multi_target_forest and also get the predictions.
    multi_target_forest.fit(X, y)

    predictions = multi_target_forest.predict(X)
    assert (n_samples, n_outputs) == predictions.shape

    predict_proba = multi_target_forest.predict_proba(X)

    assert len(predict_proba) == n_outputs
    for class_probabilities in predict_proba:
        assert (n_samples, n_classes) == class_probabilities.shape

    assert_array_equal(np.argmax(np.dstack(predict_proba), axis=1), predictions)

    # train the forest with each column and assert that predictions are equal
    for i in range(3):
        forest_ = clone(forest)  # create a clone with the same state
        forest_.fit(X, y[:, i])
        assert list(forest_.predict(X)) == list(predictions[:, i])
        assert_array_equal(list(forest_.predict_proba(X)), list(predict_proba[i]))


def test_multiclass_multioutput_estimator():
    # test to check meta of meta estimators
    svc = LinearSVC(dual="auto", random_state=0)
    multi_class_svc = OneVsRestClassifier(svc)
    multi_target_svc = MultiOutputClassifier(multi_class_svc)

    multi_target_svc.fit(X, y)

    predictions = multi_target_svc.predict(X)
    assert (n_samples, n_outputs) == predictions.shape

    # train the forest with each column and assert that predictions are equal
    for i in range(3):
        multi_class_svc_ = clone(multi_class_svc)  # create a clone
        multi_class_svc_.fit(X, y[:, i])
        assert list(multi_class_svc_.predict(X)) == list(predictions[:, i])


def test_multiclass_multioutput_estimator_predict_proba():
    seed = 542

    # make test deterministic
    rng = np.random.RandomState(seed)

    # random features
    X = rng.normal(size=(5, 5))

    # random labels
    y1 = np.array(["b", "a", "a", "b", "a"]).reshape(5, 1)  # 2 classes
    y2 = np.array(["d", "e", "f", "e", "d"]).reshape(5, 1)  # 3 classes

    Y = np.concatenate([y1, y2], axis=1)

    clf = MultiOutputClassifier(
        LogisticRegression(solver="liblinear", random_state=seed)
    )

    clf.fit(X, Y)

    y_result = clf.predict_proba(X)
    y_actual = [
        np.array(
            [
                [0.23481764, 0.76518236],
                [0.67196072, 0.32803928],
                [0.54681448, 0.45318552],
                [0.34883923, 0.65116077],
                [0.73687069, 0.26312931],
            ]
        ),
        np.array(
            [
                [0.5171785, 0.23878628, 0.24403522],
                [0.22141451, 0.64102704, 0.13755846],
                [0.16751315, 0.18256843, 0.64991843],
                [0.27357372, 0.55201592, 0.17441036],
                [0.65745193, 0.26062899, 0.08191907],
            ]
        ),
    ]

    for i in range(len(y_actual)):
        assert_almost_equal(y_result[i], y_actual[i])


def test_multi_output_classification_sample_weights():
    # weighted classifier
    Xw = [[1, 2, 3], [4, 5, 6]]
    yw = [[3, 2], [2, 3]]
    w = np.asarray([2.0, 1.0])
    forest = RandomForestClassifier(n_estimators=10, random_state=1)
    clf_w = MultiOutputClassifier(forest)
    clf_w.fit(Xw, yw, w)

    # unweighted, but with repeated samples
    X = [[1, 2, 3], [1, 2, 3], [4, 5, 6]]
    y = [[3, 2], [3, 2], [2, 3]]
    forest = RandomForestClassifier(n_estimators=10, random_state=1)
    clf = MultiOutputClassifier(forest)
    clf.fit(X, y)

    X_test = [[1.5, 2.5, 3.5], [3.5, 4.5, 5.5]]
    assert_almost_equal(clf.predict(X_test), clf_w.predict(X_test))


def test_multi_output_classification_partial_fit_sample_weights():
    # weighted classifier
    Xw = [[1, 2, 3], [4, 5, 6], [1.5, 2.5, 3.5]]
    yw = [[3, 2], [2, 3], [3, 2]]
    w = np.asarray([2.0, 1.0, 1.0])
    sgd_linear_clf = SGDClassifier(random_state=1, max_iter=20)
    clf_w = MultiOutputClassifier(sgd_linear_clf)
    clf_w.fit(Xw, yw, w)

    # unweighted, but with repeated samples
    X = [[1, 2, 3], [1, 2, 3], [4, 5, 6], [1.5, 2.5, 3.5]]
    y = [[3, 2], [3, 2], [2, 3], [3, 2]]
    sgd_linear_clf = SGDClassifier(random_state=1, max_iter=20)
    clf = MultiOutputClassifier(sgd_linear_clf)
    clf.fit(X, y)
    X_test = [[1.5, 2.5, 3.5]]
    assert_array_almost_equal(clf.predict(X_test), clf_w.predict(X_test))


def test_multi_output_exceptions():
    # NotFittedError when fit is not done but score, predict and
    # and predict_proba are called
    moc = MultiOutputClassifier(LinearSVC(dual="auto", random_state=0))
    with pytest.raises(NotFittedError):
        moc.score(X, y)

    # ValueError when number of outputs is different
    # for fit and score
    y_new = np.column_stack((y1, y2))
    moc.fit(X, y)
    with pytest.raises(ValueError):
        moc.score(X, y_new)

    # ValueError when y is continuous
    msg = "Unknown label type"
    with pytest.raises(ValueError, match=msg):
        moc.fit(X, X[:, 1])


@pytest.mark.parametrize("response_method", ["predict_proba", "predict"])
def test_multi_output_not_fitted_error(response_method):
    """Check that we raise the proper error when the estimator is not fitted"""
    moc = MultiOutputClassifier(LogisticRegression())
    with pytest.raises(NotFittedError):
        getattr(moc, response_method)(X)


def test_multi_output_delegate_predict_proba():
    """Check the behavior for the delegation of predict_proba to the underlying
    estimator"""

    # A base estimator with `predict_proba`should expose the method even before fit
    moc = MultiOutputClassifier(LogisticRegression())
    assert hasattr(moc, "predict_proba")
    moc.fit(X, y)
    assert hasattr(moc, "predict_proba")

    # A base estimator without `predict_proba` should raise an AttributeError
    moc = MultiOutputClassifier(LinearSVC(dual="auto"))
    assert not hasattr(moc, "predict_proba")
    msg = "'LinearSVC' object has no attribute 'predict_proba'"
    with pytest.raises(AttributeError, match=msg):
        moc.predict_proba(X)
    moc.fit(X, y)
    assert not hasattr(moc, "predict_proba")
    with pytest.raises(AttributeError, match=msg):
        moc.predict_proba(X)


def generate_multilabel_dataset_with_correlations():
    # Generate a multilabel data set from a multiclass dataset as a way of
    # by representing the integer number of the original class using a binary
    # encoding.
    X, y = make_classification(
        n_samples=1000, n_features=100, n_classes=16, n_informative=10, random_state=0
    )

    Y_multi = np.array([[int(yyy) for yyy in format(yy, "#06b")[2:]] for yy in y])
    return X, Y_multi


def test_classifier_chain_fit_and_predict_with_linear_svc():
    # Fit classifier chain and verify predict performance using LinearSVC
    X, Y = generate_multilabel_dataset_with_correlations()
    classifier_chain = ClassifierChain(LinearSVC(dual="auto"))
    classifier_chain.fit(X, Y)

    Y_pred = classifier_chain.predict(X)
    assert Y_pred.shape == Y.shape

    Y_decision = classifier_chain.decision_function(X)

    Y_binary = Y_decision >= 0
    assert_array_equal(Y_binary, Y_pred)
    assert not hasattr(classifier_chain, "predict_proba")


def test_classifier_chain_fit_and_predict_with_sparse_data():
    # Fit classifier chain with sparse data
    X, Y = generate_multilabel_dataset_with_correlations()
    X_sparse = sp.csr_matrix(X)

    classifier_chain = ClassifierChain(LogisticRegression())
    classifier_chain.fit(X_sparse, Y)
    Y_pred_sparse = classifier_chain.predict(X_sparse)

    classifier_chain = ClassifierChain(LogisticRegression())
    classifier_chain.fit(X, Y)
    Y_pred_dense = classifier_chain.predict(X)

    assert_array_equal(Y_pred_sparse, Y_pred_dense)


def test_classifier_chain_vs_independent_models():
    # Verify that an ensemble of classifier chains (each of length
    # N) can achieve a higher Jaccard similarity score than N independent
    # models
    X, Y = generate_multilabel_dataset_with_correlations()
    X_train = X[:600, :]
    X_test = X[600:, :]
    Y_train = Y[:600, :]
    Y_test = Y[600:, :]

    ovr = OneVsRestClassifier(LogisticRegression())
    ovr.fit(X_train, Y_train)
    Y_pred_ovr = ovr.predict(X_test)

    chain = ClassifierChain(LogisticRegression())
    chain.fit(X_train, Y_train)
    Y_pred_chain = chain.predict(X_test)

    assert jaccard_score(Y_test, Y_pred_chain, average="samples") > jaccard_score(
        Y_test, Y_pred_ovr, average="samples"
    )


def test_base_chain_fit_and_predict():
    # Fit base chain and verify predict performance
    X, Y = generate_multilabel_dataset_with_correlations()
    chains = [RegressorChain(Ridge()), ClassifierChain(LogisticRegression())]
    for chain in chains:
        chain.fit(X, Y)
        Y_pred = chain.predict(X)
        assert Y_pred.shape == Y.shape
        assert [c.coef_.size for c in chain.estimators_] == list(
            range(X.shape[1], X.shape[1] + Y.shape[1])
        )

    Y_prob = chains[1].predict_proba(X)
    Y_binary = Y_prob >= 0.5
    assert_array_equal(Y_binary, Y_pred)

    assert isinstance(chains[1], ClassifierMixin)


def test_base_chain_fit_and_predict_with_sparse_data_and_cv():
    # Fit base chain with sparse data cross_val_predict
    X, Y = generate_multilabel_dataset_with_correlations()
    X_sparse = sp.csr_matrix(X)
    base_chains = [
        ClassifierChain(LogisticRegression(), cv=3),
        RegressorChain(Ridge(), cv=3),
    ]
    for chain in base_chains:
        chain.fit(X_sparse, Y)
        Y_pred = chain.predict(X_sparse)
        assert Y_pred.shape == Y.shape


def test_base_chain_random_order():
    # Fit base chain with random order
    X, Y = generate_multilabel_dataset_with_correlations()
    for chain in [ClassifierChain(LogisticRegression()), RegressorChain(Ridge())]:
        chain_random = clone(chain).set_params(order="random", random_state=42)
        chain_random.fit(X, Y)
        chain_fixed = clone(chain).set_params(order=chain_random.order_)
        chain_fixed.fit(X, Y)
        assert_array_equal(chain_fixed.order_, chain_random.order_)
        assert list(chain_random.order) != list(range(4))
        assert len(chain_random.order_) == 4
        assert len(set(chain_random.order_)) == 4
        # Randomly ordered chain should behave identically to a fixed order
        # chain with the same order.
        for est1, est2 in zip(chain_random.estimators_, chain_fixed.estimators_):
            assert_array_almost_equal(est1.coef_, est2.coef_)


def test_base_chain_crossval_fit_and_predict():
    # Fit chain with cross_val_predict and verify predict
    # performance
    X, Y = generate_multilabel_dataset_with_correlations()

    for chain in [ClassifierChain(LogisticRegression()), RegressorChain(Ridge())]:
        chain.fit(X, Y)
        chain_cv = clone(chain).set_params(cv=3)
        chain_cv.fit(X, Y)
        Y_pred_cv = chain_cv.predict(X)
        Y_pred = chain.predict(X)

        assert Y_pred_cv.shape == Y_pred.shape
        assert not np.all(Y_pred == Y_pred_cv)
        if isinstance(chain, ClassifierChain):
            assert jaccard_score(Y, Y_pred_cv, average="samples") > 0.4
        else:
            assert mean_squared_error(Y, Y_pred_cv) < 0.25


@pytest.mark.parametrize(
    "estimator",
    [
        RandomForestClassifier(n_estimators=2),
        MultiOutputClassifier(RandomForestClassifier(n_estimators=2)),
        ClassifierChain(RandomForestClassifier(n_estimators=2)),
    ],
)
def test_multi_output_classes_(estimator):
    # Tests classes_ attribute of multioutput classifiers
    # RandomForestClassifier supports multioutput out-of-the-box
    estimator.fit(X, y)
    assert isinstance(estimator.classes_, list)
    assert len(estimator.classes_) == n_outputs
    for estimator_classes, expected_classes in zip(classes, estimator.classes_):
        assert_array_equal(estimator_classes, expected_classes)


class DummyRegressorWithFitParams(DummyRegressor):
    def fit(self, X, y, sample_weight=None, **fit_params):
        self._fit_params = fit_params
        return super().fit(X, y, sample_weight)


class DummyClassifierWithFitParams(DummyClassifier):
    def fit(self, X, y, sample_weight=None, **fit_params):
        self._fit_params = fit_params
        return super().fit(X, y, sample_weight)


@pytest.mark.filterwarnings("ignore:`n_features_in_` is deprecated")
@pytest.mark.parametrize(
    "estimator, dataset",
    [
        (
            MultiOutputClassifier(DummyClassifierWithFitParams(strategy="prior")),
            datasets.make_multilabel_classification(),
        ),
        (
            MultiOutputRegressor(DummyRegressorWithFitParams()),
            datasets.make_regression(n_targets=3, random_state=0),
        ),
    ],
)
def test_multioutput_estimator_with_fit_params(estimator, dataset):
    X, y = dataset
    some_param = np.zeros_like(X)
    estimator.fit(X, y, some_param=some_param)
    for dummy_estimator in estimator.estimators_:
        assert "some_param" in dummy_estimator._fit_params


def test_regressor_chain_w_fit_params():
    # Make sure fit_params are properly propagated to the sub-estimators
    rng = np.random.RandomState(0)
    X, y = datasets.make_regression(n_targets=3, random_state=0)
    weight = rng.rand(y.shape[0])

    class MySGD(SGDRegressor):
        def fit(self, X, y, **fit_params):
            self.sample_weight_ = fit_params["sample_weight"]
            super().fit(X, y, **fit_params)

    model = RegressorChain(MySGD())

    # Fitting with params
    fit_param = {"sample_weight": weight}
    model.fit(X, y, **fit_param)

    for est in model.estimators_:
        assert est.sample_weight_ is weight


@pytest.mark.parametrize(
    "MultiOutputEstimator, Estimator",
    [(MultiOutputClassifier, LogisticRegression), (MultiOutputRegressor, Ridge)],
)
# FIXME: we should move this test in `estimator_checks` once we are able
# to construct meta-estimator instances
def test_support_missing_values(MultiOutputEstimator, Estimator):
    # smoke test to check that pipeline MultioutputEstimators are letting
    # the validation of missing values to
    # the underlying pipeline, regressor or classifier
    rng = np.random.RandomState(42)
    X, y = rng.randn(50, 2), rng.binomial(1, 0.5, (50, 3))
    mask = rng.choice([1, 0], X.shape, p=[0.01, 0.99]).astype(bool)
    X[mask] = np.nan

    pipe = make_pipeline(SimpleImputer(), Estimator())
    MultiOutputEstimator(pipe).fit(X, y).score(X, y)


@pytest.mark.parametrize("order_type", [list, np.array, tuple])
def test_classifier_chain_tuple_order(order_type):
    X = [[1, 2, 3], [4, 5, 6], [1.5, 2.5, 3.5]]
    y = [[3, 2], [2, 3], [3, 2]]
    order = order_type([1, 0])

    chain = ClassifierChain(RandomForestClassifier(), order=order)

    chain.fit(X, y)
    X_test = [[1.5, 2.5, 3.5]]
    y_test = [[3, 2]]
    assert_array_almost_equal(chain.predict(X_test), y_test)


def test_classifier_chain_tuple_invalid_order():
    X = [[1, 2, 3], [4, 5, 6], [1.5, 2.5, 3.5]]
    y = [[3, 2], [2, 3], [3, 2]]
    order = tuple([1, 2])

    chain = ClassifierChain(RandomForestClassifier(), order=order)

    with pytest.raises(ValueError, match="invalid order"):
        chain.fit(X, y)


def test_classifier_chain_verbose(capsys):
    X, y = make_multilabel_classification(
        n_samples=100, n_features=5, n_classes=3, n_labels=3, random_state=0
    )
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    pattern = (
        r"\[Chain\].*\(1 of 3\) Processing order 0, total=.*\n"
        r"\[Chain\].*\(2 of 3\) Processing order 1, total=.*\n"
        r"\[Chain\].*\(3 of 3\) Processing order 2, total=.*\n$"
    )

    classifier = ClassifierChain(
        DecisionTreeClassifier(),
        order=[0, 1, 2],
        random_state=0,
        verbose=True,
    )
    classifier.fit(X_train, y_train)
    assert re.match(pattern, capsys.readouterr()[0])


def test_regressor_chain_verbose(capsys):
    X, y = make_regression(n_samples=125, n_targets=3, random_state=0)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    pattern = (
        r"\[Chain\].*\(1 of 3\) Processing order 1, total=.*\n"
        r"\[Chain\].*\(2 of 3\) Processing order 0, total=.*\n"
        r"\[Chain\].*\(3 of 3\) Processing order 2, total=.*\n$"
    )
    regressor = RegressorChain(
        LinearRegression(),
        order=[1, 0, 2],
        random_state=0,
        verbose=True,
    )
    regressor.fit(X_train, y_train)
    assert re.match(pattern, capsys.readouterr()[0])


def test_multioutputregressor_ducktypes_fitted_estimator():
    """Test that MultiOutputRegressor checks the fitted estimator for
    predict. Non-regression test for #16549."""
    X, y = load_linnerud(return_X_y=True)
    stacker = StackingRegressor(
        estimators=[("sgd", SGDRegressor(random_state=1))],
        final_estimator=Ridge(),
        cv=2,
    )

    reg = MultiOutputRegressor(estimator=stacker).fit(X, y)

    # Does not raise
    reg.predict(X)


@pytest.mark.parametrize(
    "Cls, method", [(ClassifierChain, "fit"), (MultiOutputClassifier, "partial_fit")]
)
def test_fit_params_no_routing(Cls, method):
    """Check that we raise an error when passing metadata not requested by the
    underlying classifier.
    """
    X, y = make_classification(n_samples=50)
    clf = Cls(PassiveAggressiveClassifier())

    with pytest.raises(ValueError, match="is only supported if"):
        getattr(clf, method)(X, y, test=1)


def test_multioutput_regressor_has_partial_fit():
    # Test that an unfitted MultiOutputRegressor handles available_if for
    # partial_fit correctly
    est = MultiOutputRegressor(LinearRegression())
    msg = "This 'MultiOutputRegressor' has no attribute 'partial_fit'"
    with pytest.raises(AttributeError, match=msg):
        getattr(est, "partial_fit")