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linedance-app/venv/lib/python3.12/site-packages/sklearn/tests/test_multioutput.py

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+import re
+
+import numpy as np
+import pytest
+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,
+    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,
+)
+from sklearn.utils.fixes import (
+    BSR_CONTAINERS,
+    COO_CONTAINERS,
+    CSC_CONTAINERS,
+    CSR_CONTAINERS,
+    DOK_CONTAINERS,
+    LIL_CONTAINERS,
+)
+
+
+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)
+
+
+@pytest.mark.parametrize(
+    "sparse_container",
+    CSR_CONTAINERS
+    + CSC_CONTAINERS
+    + COO_CONTAINERS
+    + LIL_CONTAINERS
+    + DOK_CONTAINERS
+    + BSR_CONTAINERS,
+)
+def test_multi_target_sparse_regression(sparse_container):
+    X, y = datasets.make_regression(n_targets=3, random_state=0)
+    X_train, y_train = X[:50], y[:50]
+    X_test = X[50:]
+
+    rgr = MultiOutputRegressor(Lasso(random_state=0))
+    rgr_sparse = MultiOutputRegressor(Lasso(random_state=0))
+
+    rgr.fit(X_train, y_train)
+    rgr_sparse.fit(sparse_container(X_train), y_train)
+
+    assert_almost_equal(
+        rgr.predict(X_test), rgr_sparse.predict(sparse_container(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)))
+
+
+# TODO: remove mark once loky bug is fixed:
+# https://github.com/joblib/loky/issues/458
+@pytest.mark.thread_unsafe
+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)
+
+    inner2_msg = "probability estimates are not available for loss='hinge'"
+    inner1_msg = "'SGDClassifier' has no attribute 'predict_proba'"
+    outer_msg = "'MultiOutputClassifier' has no attribute 'predict_proba'"
+    with pytest.raises(AttributeError, match=outer_msg) as exec_info:
+        multi_target_linear.predict_proba(X)
+
+    assert isinstance(exec_info.value.__cause__, AttributeError)
+    assert inner1_msg in str(exec_info.value.__cause__)
+
+    assert isinstance(exec_info.value.__cause__.__cause__, AttributeError)
+    assert inner2_msg in str(exec_info.value.__cause__.__cause__)
+
+
+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(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(random_state=seed))
+
+    clf.fit(X, Y)
+
+    y_result = clf.predict_proba(X)
+    y_actual = [
+        np.array(
+            [
+                [0.31525135, 0.68474865],
+                [0.81004803, 0.18995197],
+                [0.65664086, 0.34335914],
+                [0.38584929, 0.61415071],
+                [0.83234285, 0.16765715],
+            ]
+        ),
+        np.array(
+            [
+                [0.65759215, 0.20976588, 0.13264197],
+                [0.14996984, 0.82591444, 0.02411571],
+                [0.13111876, 0.13294966, 0.73593158],
+                [0.24663053, 0.65860244, 0.09476703],
+                [0.81458885, 0.1728158, 0.01259535],
+            ]
+        ),
+    ]
+
+    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, tol=None)
+    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, tol=None)
+    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(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())
+    assert not hasattr(moc, "predict_proba")
+
+    outer_msg = "'MultiOutputClassifier' has no attribute 'predict_proba'"
+    inner_msg = "'LinearSVC' object has no attribute 'predict_proba'"
+    with pytest.raises(AttributeError, match=outer_msg) as exec_info:
+        moc.predict_proba(X)
+    assert isinstance(exec_info.value.__cause__, AttributeError)
+    assert inner_msg == str(exec_info.value.__cause__)
+
+    moc.fit(X, y)
+    assert not hasattr(moc, "predict_proba")
+    with pytest.raises(AttributeError, match=outer_msg) as exec_info:
+        moc.predict_proba(X)
+    assert isinstance(exec_info.value.__cause__, AttributeError)
+    assert inner_msg == str(exec_info.value.__cause__)
+
+
+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
+
+
+@pytest.mark.parametrize("chain_method", ["predict", "decision_function"])
+def test_classifier_chain_fit_and_predict_with_linear_svc(chain_method):
+    # Fit classifier chain and verify predict performance using LinearSVC
+    X, Y = generate_multilabel_dataset_with_correlations()
+    classifier_chain = ClassifierChain(
+        LinearSVC(),
+        chain_method=chain_method,
+    ).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")
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_classifier_chain_fit_and_predict_with_sparse_data(csr_container):
+    # Fit classifier chain with sparse data
+    X, Y = generate_multilabel_dataset_with_correlations()
+    X_sparse = csr_container(X)
+
+    classifier_chain = ClassifierChain(LogisticRegression()).fit(X_sparse, Y)
+    Y_pred_sparse = classifier_chain.predict(X_sparse)
+
+    classifier_chain = ClassifierChain(LogisticRegression()).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"
+    )
+
+
+@pytest.mark.parametrize(
+    "chain_method",
+    ["predict", "predict_proba", "predict_log_proba", "decision_function"],
+)
+@pytest.mark.parametrize("response_method", ["predict_proba", "predict_log_proba"])
+def test_classifier_chain_fit_and_predict(chain_method, response_method):
+    # Fit classifier chain and verify predict performance
+    X, Y = generate_multilabel_dataset_with_correlations()
+    chain = ClassifierChain(LogisticRegression(), chain_method=chain_method)
+    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 = getattr(chain, response_method)(X)
+    if response_method == "predict_log_proba":
+        Y_prob = np.exp(Y_prob)
+    Y_binary = Y_prob >= 0.5
+    assert_array_equal(Y_binary, Y_pred)
+
+    assert isinstance(chain, ClassifierMixin)
+
+
+def test_regressor_chain_fit_and_predict():
+    # Fit regressor chain and verify Y and estimator coefficients shape
+    X, Y = generate_multilabel_dataset_with_correlations()
+    chain = RegressorChain(Ridge())
+    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])
+    )
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_base_chain_fit_and_predict_with_sparse_data_and_cv(csr_container):
+    # Fit base chain with sparse data cross_val_predict
+    X, Y = generate_multilabel_dataset_with_correlations()
+    X_sparse = csr_container(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_)
+
+
+@pytest.mark.parametrize(
+    "chain_type, chain_method",
+    [
+        ("classifier", "predict"),
+        ("classifier", "predict_proba"),
+        ("classifier", "predict_log_proba"),
+        ("classifier", "decision_function"),
+        ("regressor", ""),
+    ],
+)
+def test_base_chain_crossval_fit_and_predict(chain_type, chain_method):
+    # Fit chain with cross_val_predict and verify predict
+    # performance
+    X, Y = generate_multilabel_dataset_with_correlations()
+
+    if chain_type == "classifier":
+        chain = ClassifierChain(LogisticRegression(), chain_method=chain_method)
+    else:
+        chain = 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 = clone(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.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):
+    estimator = clone(estimator)  # Avoid side effects from shared instances
+    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(n_estimators=2, random_state=0), 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(SGDClassifier())
+
+    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")
+
+
+# TODO(1.9):  remove when deprecated `base_estimator` is removed
+@pytest.mark.parametrize("Estimator", [ClassifierChain, RegressorChain])
+def test_base_estimator_deprecation(Estimator):
+    """Check that we warn about the deprecation of `base_estimator`."""
+    X = np.array([[1, 2], [3, 4]])
+    y = np.array([[1, 0], [0, 1]])
+
+    estimator = LogisticRegression()
+
+    with pytest.warns(FutureWarning):
+        Estimator(base_estimator=estimator).fit(X, y)
+
+    with pytest.raises(ValueError):
+        Estimator(base_estimator=estimator, estimator=estimator).fit(X, y)