"""Test the validation module"""

import os
import re
import sys
import tempfile
import warnings
from functools import partial
from io import StringIO
from time import sleep

import numpy as np
import pytest
from scipy.sparse import issparse

from xlearn import config_context
from xlearn.base import BaseEstimator, ClassifierMixin, clone
from xlearn.cluster import KMeans
from xlearn.datasets import (
    load_diabetes,
    load_digits,
    load_iris,
    make_classification,
    make_multilabel_classification,
    make_regression,
)
from xlearn.ensemble import RandomForestClassifier
from xlearn.exceptions import FitFailedWarning, UnsetMetadataPassedError
from xlearn.impute import SimpleImputer
from xlearn.linear_model import (
    LogisticRegression,
    PassiveAggressiveClassifier,
    Ridge,
    RidgeClassifier,
    SGDClassifier,
)
from xlearn.metrics import (
    accuracy_score,
    check_scoring,
    confusion_matrix,
    explained_variance_score,
    make_scorer,
    mean_squared_error,
    precision_recall_fscore_support,
    precision_score,
    r2_score,
)
from xlearn.metrics._scorer import _MultimetricScorer
from xlearn.model_selection import (
    GridSearchCV,
    GroupKFold,
    GroupShuffleSplit,
    KFold,
    LeaveOneGroupOut,
    LeaveOneOut,
    LeavePGroupsOut,
    ShuffleSplit,
    StratifiedKFold,
    cross_val_predict,
    cross_val_score,
    cross_validate,
    learning_curve,
    permutation_test_score,
    validation_curve,
)
from xlearn.model_selection._validation import (
    _check_is_permutation,
    _fit_and_score,
    _score,
)
from xlearn.model_selection.tests.common import OneTimeSplitter
from xlearn.model_selection.tests.test_search import FailingClassifier
from xlearn.multiclass import OneVsRestClassifier
from xlearn.neighbors import KNeighborsClassifier
from xlearn.neural_network import MLPRegressor
from xlearn.pipeline import Pipeline
from xlearn.preprocessing import LabelEncoder, scale
from xlearn.svm import SVC, LinearSVC
from xlearn.tests.metadata_routing_common import (
    ConsumingClassifier,
    ConsumingScorer,
    ConsumingSplitter,
    _Registry,
    check_recorded_metadata,
)
from xlearn.utils import shuffle
from xlearn.utils._mocking import CheckingClassifier, MockDataFrame
from xlearn.utils._testing import (
    assert_allclose,
    assert_almost_equal,
    assert_array_almost_equal,
    assert_array_equal,
)
from xlearn.utils.fixes import COO_CONTAINERS, CSR_CONTAINERS
from xlearn.utils.validation import _num_samples


class MockImprovingEstimator(BaseEstimator):
    """Dummy classifier to test the learning curve"""

    def __init__(self, n_max_train_sizes):
        self.n_max_train_sizes = n_max_train_sizes
        self.train_sizes = 0
        self.X_subset = None

    def fit(self, X_subset, y_subset=None):
        self.X_subset = X_subset
        self.train_sizes = X_subset.shape[0]
        return self

    def predict(self, X):
        raise NotImplementedError

    def score(self, X=None, Y=None):
        # training score becomes worse (2 -> 1), test error better (0 -> 1)
        if self._is_training_data(X):
            return 2.0 - float(self.train_sizes) / self.n_max_train_sizes
        else:
            return float(self.train_sizes) / self.n_max_train_sizes

    def _is_training_data(self, X):
        return X is self.X_subset


class MockIncrementalImprovingEstimator(MockImprovingEstimator):
    """Dummy classifier that provides partial_fit"""

    def __init__(self, n_max_train_sizes, expected_fit_params=None):
        super().__init__(n_max_train_sizes)
        self.x = None
        self.expected_fit_params = expected_fit_params

    def _is_training_data(self, X):
        return self.x in X

    def partial_fit(self, X, y=None, **params):
        self.train_sizes += X.shape[0]
        self.x = X[0]
        if self.expected_fit_params:
            missing = set(self.expected_fit_params) - set(params)
            if missing:
                raise AssertionError(
                    f"Expected fit parameter(s) {list(missing)} not seen."
                )
            for key, value in params.items():
                if key in self.expected_fit_params and _num_samples(
                    value
                ) != _num_samples(X):
                    raise AssertionError(
                        f"Fit parameter {key} has length {_num_samples(value)}"
                        f"; expected {_num_samples(X)}."
                    )


class MockEstimatorWithParameter(BaseEstimator):
    """Dummy classifier to test the validation curve"""

    def __init__(self, param=0.5):
        self.X_subset = None
        self.param = param

    def fit(self, X_subset, y_subset):
        self.X_subset = X_subset
        self.train_sizes = X_subset.shape[0]
        return self

    def predict(self, X):
        raise NotImplementedError

    def score(self, X=None, y=None):
        return self.param if self._is_training_data(X) else 1 - self.param

    def _is_training_data(self, X):
        return X is self.X_subset


class MockEstimatorWithSingleFitCallAllowed(MockEstimatorWithParameter):
    """Dummy classifier that disallows repeated calls of fit method"""

    def fit(self, X_subset, y_subset):
        assert not hasattr(self, "fit_called_"), "fit is called the second time"
        self.fit_called_ = True
        return super().fit(X_subset, y_subset)

    def predict(self, X):
        raise NotImplementedError


class MockClassifier(ClassifierMixin, BaseEstimator):
    """Dummy classifier to test the cross-validation"""

    def __init__(self, a=0, allow_nd=False):
        self.a = a
        self.allow_nd = allow_nd

    def fit(
        self,
        X,
        Y=None,
        sample_weight=None,
        class_prior=None,
        sparse_sample_weight=None,
        sparse_param=None,
        dummy_int=None,
        dummy_str=None,
        dummy_obj=None,
        callback=None,
    ):
        """The dummy arguments are to test that this fit function can
        accept non-array arguments through cross-validation, such as:
            - int
            - str (this is actually array-like)
            - object
            - function
        """
        self.dummy_int = dummy_int
        self.dummy_str = dummy_str
        self.dummy_obj = dummy_obj
        if callback is not None:
            callback(self)

        if self.allow_nd:
            X = X.reshape(len(X), -1)
        if X.ndim >= 3 and not self.allow_nd:
            raise ValueError("X cannot be d")
        if sample_weight is not None:
            assert sample_weight.shape[0] == X.shape[0], (
                "MockClassifier extra fit_param "
                "sample_weight.shape[0] is {0}, should be {1}".format(
                    sample_weight.shape[0], X.shape[0]
                )
            )
        if class_prior is not None:
            assert class_prior.shape[0] == len(np.unique(y)), (
                "MockClassifier extra fit_param class_prior.shape[0]"
                " is {0}, should be {1}".format(class_prior.shape[0], len(np.unique(y)))
            )
        if sparse_sample_weight is not None:
            fmt = (
                "MockClassifier extra fit_param sparse_sample_weight"
                ".shape[0] is {0}, should be {1}"
            )
            assert sparse_sample_weight.shape[0] == X.shape[0], fmt.format(
                sparse_sample_weight.shape[0], X.shape[0]
            )
        if sparse_param is not None:
            fmt = (
                "MockClassifier extra fit_param sparse_param.shape "
                "is ({0}, {1}), should be ({2}, {3})"
            )
            assert sparse_param.shape == P.shape, fmt.format(
                sparse_param.shape[0],
                sparse_param.shape[1],
                P.shape[0],
                P.shape[1],
            )
        self.classes_ = np.unique(y)
        return self

    def predict(self, T):
        if self.allow_nd:
            T = T.reshape(len(T), -1)
        return T[:, 0]

    def predict_proba(self, T):
        return T

    def score(self, X=None, Y=None):
        return 1.0 / (1 + np.abs(self.a))

    def get_params(self, deep=False):
        return {"a": self.a, "allow_nd": self.allow_nd}


# XXX: use 2D array, since 1D X is being detected as a single sample in
# check_consistent_length
X = np.ones((15, 2))
y = np.array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 6])
# The number of samples per class needs to be > n_splits,
# for StratifiedKFold(n_splits=3)
y2 = np.array([1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3])
P = np.eye(5)


@pytest.mark.parametrize("coo_container", COO_CONTAINERS)
def test_cross_val_score(coo_container):
    clf = MockClassifier()
    X_sparse = coo_container(X)

    for a in range(-10, 10):
        clf.a = a
        # Smoke test
        scores = cross_val_score(clf, X, y2)
        assert_array_equal(scores, clf.score(X, y2))

        # test with multioutput y
        multioutput_y = np.column_stack([y2, y2[::-1]])
        scores = cross_val_score(clf, X_sparse, multioutput_y)
        assert_array_equal(scores, clf.score(X_sparse, multioutput_y))

        scores = cross_val_score(clf, X_sparse, y2)
        assert_array_equal(scores, clf.score(X_sparse, y2))

        # test with multioutput y
        scores = cross_val_score(clf, X_sparse, multioutput_y)
        assert_array_equal(scores, clf.score(X_sparse, multioutput_y))

    # test with X and y as list
    list_check = lambda x: isinstance(x, list)
    clf = CheckingClassifier(check_X=list_check)
    scores = cross_val_score(clf, X.tolist(), y2.tolist(), cv=3)

    clf = CheckingClassifier(check_y=list_check)
    scores = cross_val_score(clf, X, y2.tolist(), cv=3)

    # test with 3d X and
    X_3d = X[:, :, np.newaxis]
    clf = MockClassifier(allow_nd=True)
    scores = cross_val_score(clf, X_3d, y2)

    clf = MockClassifier(allow_nd=False)
    with pytest.raises(ValueError):
        cross_val_score(clf, X_3d, y2, error_score="raise")


def test_cross_validate_many_jobs():
    # regression test for #12154: cv='warn' with n_jobs>1 trigger a copy of
    # the parameters leading to a failure in check_cv due to cv is 'warn'
    # instead of cv == 'warn'.
    X, y = load_iris(return_X_y=True)
    clf = SVC(gamma="auto")
    grid = GridSearchCV(clf, param_grid={"C": [1, 10]})
    cross_validate(grid, X, y, n_jobs=2)


def test_cross_validate_invalid_scoring_param():
    X, y = make_classification(random_state=0)
    estimator = MockClassifier()

    # Test the errors
    error_message_regexp = ".*must be unique strings.*"

    # List/tuple of callables should raise a message advising users to use
    # dict of names to callables mapping
    with pytest.raises(ValueError, match=error_message_regexp):
        cross_validate(
            estimator,
            X,
            y,
            scoring=(make_scorer(precision_score), make_scorer(accuracy_score)),
        )
    with pytest.raises(ValueError, match=error_message_regexp):
        cross_validate(estimator, X, y, scoring=(make_scorer(precision_score),))

    # So should empty lists/tuples
    with pytest.raises(ValueError, match=error_message_regexp + "Empty list.*"):
        cross_validate(estimator, X, y, scoring=())

    # So should duplicated entries
    with pytest.raises(ValueError, match=error_message_regexp + "Duplicate.*"):
        cross_validate(estimator, X, y, scoring=("f1_micro", "f1_micro"))

    # Nested Lists should raise a generic error message
    with pytest.raises(ValueError, match=error_message_regexp):
        cross_validate(estimator, X, y, scoring=[[make_scorer(precision_score)]])

    # Empty dict should raise invalid scoring error
    with pytest.raises(ValueError, match="An empty dict"):
        cross_validate(estimator, X, y, scoring=(dict()))

    multiclass_scorer = make_scorer(precision_recall_fscore_support)

    # Multiclass Scorers that return multiple values are not supported yet
    # the warning message we're expecting to see
    warning_message = (
        "Scoring failed. The score on this train-test "
        f"partition for these parameters will be set to {np.nan}. "
        "Details: \n"
    )

    with pytest.warns(UserWarning, match=warning_message):
        cross_validate(estimator, X, y, scoring=multiclass_scorer)

    with pytest.warns(UserWarning, match=warning_message):
        cross_validate(estimator, X, y, scoring={"foo": multiclass_scorer})


def test_cross_validate_nested_estimator():
    # Non-regression test to ensure that nested
    # estimators are properly returned in a list
    # https://github.com/chenxingqiang/jax-sklearn/pull/17745
    (X, y) = load_iris(return_X_y=True)
    pipeline = Pipeline(
        [
            ("imputer", SimpleImputer()),
            ("classifier", MockClassifier()),
        ]
    )

    results = cross_validate(pipeline, X, y, return_estimator=True)
    estimators = results["estimator"]

    assert isinstance(estimators, list)
    assert all(isinstance(estimator, Pipeline) for estimator in estimators)


@pytest.mark.parametrize("use_sparse", [False, True])
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_cross_validate(use_sparse: bool, csr_container):
    # Compute train and test mse/r2 scores
    cv = KFold()

    # Regression
    X_reg, y_reg = make_regression(n_samples=30, random_state=0)
    reg = Ridge(random_state=0)

    # Classification
    X_clf, y_clf = make_classification(n_samples=30, random_state=0)
    clf = SVC(kernel="linear", random_state=0)

    if use_sparse:
        X_reg = csr_container(X_reg)
        X_clf = csr_container(X_clf)

    for X, y, est in ((X_reg, y_reg, reg), (X_clf, y_clf, clf)):
        # It's okay to evaluate regression metrics on classification too
        mse_scorer = check_scoring(est, scoring="neg_mean_squared_error")
        r2_scorer = check_scoring(est, scoring="r2")
        train_mse_scores = []
        test_mse_scores = []
        train_r2_scores = []
        test_r2_scores = []
        fitted_estimators = []

        for train, test in cv.split(X, y):
            est = clone(est).fit(X[train], y[train])
            train_mse_scores.append(mse_scorer(est, X[train], y[train]))
            train_r2_scores.append(r2_scorer(est, X[train], y[train]))
            test_mse_scores.append(mse_scorer(est, X[test], y[test]))
            test_r2_scores.append(r2_scorer(est, X[test], y[test]))
            fitted_estimators.append(est)

        train_mse_scores = np.array(train_mse_scores)
        test_mse_scores = np.array(test_mse_scores)
        train_r2_scores = np.array(train_r2_scores)
        test_r2_scores = np.array(test_r2_scores)
        fitted_estimators = np.array(fitted_estimators)

        scores = (
            train_mse_scores,
            test_mse_scores,
            train_r2_scores,
            test_r2_scores,
            fitted_estimators,
        )

        # To ensure that the test does not suffer from
        # large statistical fluctuations due to slicing small datasets,
        # we pass the cross-validation instance
        check_cross_validate_single_metric(est, X, y, scores, cv)
        check_cross_validate_multi_metric(est, X, y, scores, cv)


def check_cross_validate_single_metric(clf, X, y, scores, cv):
    (
        train_mse_scores,
        test_mse_scores,
        train_r2_scores,
        test_r2_scores,
        fitted_estimators,
    ) = scores
    # Test single metric evaluation when scoring is string or singleton list
    for return_train_score, dict_len in ((True, 4), (False, 3)):
        # Single metric passed as a string
        if return_train_score:
            mse_scores_dict = cross_validate(
                clf,
                X,
                y,
                scoring="neg_mean_squared_error",
                return_train_score=True,
                cv=cv,
            )
            assert_array_almost_equal(mse_scores_dict["train_score"], train_mse_scores)
        else:
            mse_scores_dict = cross_validate(
                clf,
                X,
                y,
                scoring="neg_mean_squared_error",
                return_train_score=False,
                cv=cv,
            )
        assert isinstance(mse_scores_dict, dict)
        assert len(mse_scores_dict) == dict_len
        assert_array_almost_equal(mse_scores_dict["test_score"], test_mse_scores)

        # Single metric passed as a list
        if return_train_score:
            # It must be True by default - deprecated
            r2_scores_dict = cross_validate(
                clf, X, y, scoring=["r2"], return_train_score=True, cv=cv
            )
            assert_array_almost_equal(r2_scores_dict["train_r2"], train_r2_scores, True)
        else:
            r2_scores_dict = cross_validate(
                clf, X, y, scoring=["r2"], return_train_score=False, cv=cv
            )
        assert isinstance(r2_scores_dict, dict)
        assert len(r2_scores_dict) == dict_len
        assert_array_almost_equal(r2_scores_dict["test_r2"], test_r2_scores)

    # Test return_estimator option
    mse_scores_dict = cross_validate(
        clf, X, y, scoring="neg_mean_squared_error", return_estimator=True, cv=cv
    )
    for k, est in enumerate(mse_scores_dict["estimator"]):
        est_coef = est.coef_.copy()
        if issparse(est_coef):
            est_coef = est_coef.toarray()

        fitted_est_coef = fitted_estimators[k].coef_.copy()
        if issparse(fitted_est_coef):
            fitted_est_coef = fitted_est_coef.toarray()

        assert_almost_equal(est_coef, fitted_est_coef)
        assert_almost_equal(est.intercept_, fitted_estimators[k].intercept_)


def check_cross_validate_multi_metric(clf, X, y, scores, cv):
    # Test multimetric evaluation when scoring is a list / dict
    (
        train_mse_scores,
        test_mse_scores,
        train_r2_scores,
        test_r2_scores,
        fitted_estimators,
    ) = scores

    def custom_scorer(clf, X, y):
        y_pred = clf.predict(X)
        return {
            "r2": r2_score(y, y_pred),
            "neg_mean_squared_error": -mean_squared_error(y, y_pred),
        }

    all_scoring = (
        ("r2", "neg_mean_squared_error"),
        {
            "r2": make_scorer(r2_score),
            "neg_mean_squared_error": "neg_mean_squared_error",
        },
        custom_scorer,
    )

    keys_sans_train = {
        "test_r2",
        "test_neg_mean_squared_error",
        "fit_time",
        "score_time",
    }
    keys_with_train = keys_sans_train.union(
        {"train_r2", "train_neg_mean_squared_error"}
    )

    for return_train_score in (True, False):
        for scoring in all_scoring:
            if return_train_score:
                # return_train_score must be True by default - deprecated
                cv_results = cross_validate(
                    clf, X, y, scoring=scoring, return_train_score=True, cv=cv
                )
                assert_array_almost_equal(cv_results["train_r2"], train_r2_scores)
                assert_array_almost_equal(
                    cv_results["train_neg_mean_squared_error"], train_mse_scores
                )
            else:
                cv_results = cross_validate(
                    clf, X, y, scoring=scoring, return_train_score=False, cv=cv
                )
            assert isinstance(cv_results, dict)
            assert set(cv_results.keys()) == (
                keys_with_train if return_train_score else keys_sans_train
            )
            assert_array_almost_equal(cv_results["test_r2"], test_r2_scores)
            assert_array_almost_equal(
                cv_results["test_neg_mean_squared_error"], test_mse_scores
            )

            # Make sure all the arrays are of np.ndarray type
            assert isinstance(cv_results["test_r2"], np.ndarray)
            assert isinstance(cv_results["test_neg_mean_squared_error"], np.ndarray)
            assert isinstance(cv_results["fit_time"], np.ndarray)
            assert isinstance(cv_results["score_time"], np.ndarray)

            # Ensure all the times are within sane limits
            assert np.all(cv_results["fit_time"] >= 0)
            assert np.all(cv_results["fit_time"] < 10)
            assert np.all(cv_results["score_time"] >= 0)
            assert np.all(cv_results["score_time"] < 10)


def test_cross_val_score_predict_groups():
    # Check if ValueError (when groups is None) propagates to cross_val_score
    # and cross_val_predict
    # And also check if groups is correctly passed to the cv object
    X, y = make_classification(n_samples=20, n_classes=2, random_state=0)

    clf = SVC(kernel="linear")

    group_cvs = [
        LeaveOneGroupOut(),
        LeavePGroupsOut(2),
        GroupKFold(),
        GroupShuffleSplit(),
    ]
    error_message = "The 'groups' parameter should not be None."
    for cv in group_cvs:
        with pytest.raises(ValueError, match=error_message):
            cross_val_score(estimator=clf, X=X, y=y, cv=cv)
        with pytest.raises(ValueError, match=error_message):
            cross_val_predict(estimator=clf, X=X, y=y, cv=cv)


def test_cross_val_score_pandas():
    # check cross_val_score doesn't destroy pandas dataframe
    types = [(MockDataFrame, MockDataFrame)]
    try:
        from pandas import DataFrame, Series

        types.append((Series, DataFrame))
    except ImportError:
        pass
    for TargetType, InputFeatureType in types:
        # X dataframe, y series
        # 3 fold cross val is used so we need at least 3 samples per class
        X_df, y_ser = InputFeatureType(X), TargetType(y2)
        check_df = lambda x: isinstance(x, InputFeatureType)
        check_series = lambda x: isinstance(x, TargetType)
        clf = CheckingClassifier(check_X=check_df, check_y=check_series)
        cross_val_score(clf, X_df, y_ser, cv=3)


def test_cross_val_score_mask():
    # test that cross_val_score works with boolean masks
    svm = SVC(kernel="linear")
    iris = load_iris()
    X, y = iris.data, iris.target
    kfold = KFold(5)
    scores_indices = cross_val_score(svm, X, y, cv=kfold)
    kfold = KFold(5)
    cv_masks = []
    for train, test in kfold.split(X, y):
        mask_train = np.zeros(len(y), dtype=bool)
        mask_test = np.zeros(len(y), dtype=bool)
        mask_train[train] = 1
        mask_test[test] = 1
        cv_masks.append((train, test))
    scores_masks = cross_val_score(svm, X, y, cv=cv_masks)
    assert_array_equal(scores_indices, scores_masks)


def test_cross_val_score_precomputed():
    # test for svm with precomputed kernel
    svm = SVC(kernel="precomputed")
    iris = load_iris()
    X, y = iris.data, iris.target
    linear_kernel = np.dot(X, X.T)
    score_precomputed = cross_val_score(svm, linear_kernel, y)
    svm = SVC(kernel="linear")
    score_linear = cross_val_score(svm, X, y)
    assert_array_almost_equal(score_precomputed, score_linear)

    # test with callable
    svm = SVC(kernel=lambda x, y: np.dot(x, y.T))
    score_callable = cross_val_score(svm, X, y)
    assert_array_almost_equal(score_precomputed, score_callable)

    # Error raised for non-square X
    svm = SVC(kernel="precomputed")
    with pytest.raises(ValueError):
        cross_val_score(svm, X, y)

    # test error is raised when the precomputed kernel is not array-like
    # or sparse
    with pytest.raises(ValueError):
        cross_val_score(svm, linear_kernel.tolist(), y)


@pytest.mark.parametrize("coo_container", COO_CONTAINERS)
def test_cross_val_score_fit_params(coo_container):
    clf = MockClassifier()
    n_samples = X.shape[0]
    n_classes = len(np.unique(y))

    W_sparse = coo_container(
        (np.array([1]), (np.array([1]), np.array([0]))), shape=(15, 1)
    )
    P_sparse = coo_container(np.eye(5))

    DUMMY_INT = 42
    DUMMY_STR = "42"
    DUMMY_OBJ = object()

    def assert_fit_params(clf):
        # Function to test that the values are passed correctly to the
        # classifier arguments for non-array type

        assert clf.dummy_int == DUMMY_INT
        assert clf.dummy_str == DUMMY_STR
        assert clf.dummy_obj == DUMMY_OBJ

    fit_params = {
        "sample_weight": np.ones(n_samples),
        "class_prior": np.full(n_classes, 1.0 / n_classes),
        "sparse_sample_weight": W_sparse,
        "sparse_param": P_sparse,
        "dummy_int": DUMMY_INT,
        "dummy_str": DUMMY_STR,
        "dummy_obj": DUMMY_OBJ,
        "callback": assert_fit_params,
    }
    cross_val_score(clf, X, y2, params=fit_params)


def test_cross_val_score_score_func():
    clf = MockClassifier()
    _score_func_args = []

    def score_func(y_test, y_predict):
        _score_func_args.append((y_test, y_predict))
        return 1.0

    with warnings.catch_warnings(record=True):
        scoring = make_scorer(score_func)
        score = cross_val_score(clf, X, y, scoring=scoring, cv=3)
    assert_array_equal(score, [1.0, 1.0, 1.0])
    # Test that score function is called only 3 times (for cv=3)
    assert len(_score_func_args) == 3


def test_cross_val_score_with_score_func_classification():
    iris = load_iris()
    clf = SVC(kernel="linear")

    # Default score (should be the accuracy score)
    scores = cross_val_score(clf, iris.data, iris.target)
    assert_array_almost_equal(scores, [0.97, 1.0, 0.97, 0.97, 1.0], 2)

    # Correct classification score (aka. zero / one score) - should be the
    # same as the default estimator score
    zo_scores = cross_val_score(clf, iris.data, iris.target, scoring="accuracy")
    assert_array_almost_equal(zo_scores, [0.97, 1.0, 0.97, 0.97, 1.0], 2)

    # F1 score (class are balanced so f1_score should be equal to zero/one
    # score
    f1_scores = cross_val_score(clf, iris.data, iris.target, scoring="f1_weighted")
    assert_array_almost_equal(f1_scores, [0.97, 1.0, 0.97, 0.97, 1.0], 2)


def test_cross_val_score_with_score_func_regression():
    X, y = make_regression(n_samples=30, n_features=20, n_informative=5, random_state=0)
    reg = Ridge()

    # Default score of the Ridge regression estimator
    scores = cross_val_score(reg, X, y)
    assert_array_almost_equal(scores, [0.94, 0.97, 0.97, 0.99, 0.92], 2)

    # R2 score (aka. determination coefficient) - should be the
    # same as the default estimator score
    r2_scores = cross_val_score(reg, X, y, scoring="r2")
    assert_array_almost_equal(r2_scores, [0.94, 0.97, 0.97, 0.99, 0.92], 2)

    # Mean squared error; this is a loss function, so "scores" are negative
    neg_mse_scores = cross_val_score(reg, X, y, scoring="neg_mean_squared_error")
    expected_neg_mse = np.array([-763.07, -553.16, -274.38, -273.26, -1681.99])
    assert_array_almost_equal(neg_mse_scores, expected_neg_mse, 2)

    # Explained variance
    scoring = make_scorer(explained_variance_score)
    ev_scores = cross_val_score(reg, X, y, scoring=scoring)
    assert_array_almost_equal(ev_scores, [0.94, 0.97, 0.97, 0.99, 0.92], 2)


@pytest.mark.parametrize("coo_container", COO_CONTAINERS)
def test_permutation_score(coo_container):
    iris = load_iris()
    X = iris.data
    X_sparse = coo_container(X)
    y = iris.target
    svm = SVC(kernel="linear")
    cv = StratifiedKFold(2)

    score, scores, pvalue = permutation_test_score(
        svm, X, y, n_permutations=30, cv=cv, scoring="accuracy"
    )
    assert score > 0.9
    assert_almost_equal(pvalue, 0.0, 1)

    score_group, _, pvalue_group = permutation_test_score(
        svm,
        X,
        y,
        n_permutations=30,
        cv=cv,
        scoring="accuracy",
        groups=np.ones(y.size),
        random_state=0,
    )
    assert score_group == score
    assert pvalue_group == pvalue

    # check that we obtain the same results with a sparse representation
    svm_sparse = SVC(kernel="linear")
    cv_sparse = StratifiedKFold(2)
    score_group, _, pvalue_group = permutation_test_score(
        svm_sparse,
        X_sparse,
        y,
        n_permutations=30,
        cv=cv_sparse,
        scoring="accuracy",
        groups=np.ones(y.size),
        random_state=0,
    )

    assert score_group == score
    assert pvalue_group == pvalue

    # test with custom scoring object
    def custom_score(y_true, y_pred):
        return ((y_true == y_pred).sum() - (y_true != y_pred).sum()) / y_true.shape[0]

    scorer = make_scorer(custom_score)
    score, _, pvalue = permutation_test_score(
        svm, X, y, n_permutations=100, scoring=scorer, cv=cv, random_state=0
    )
    assert_almost_equal(score, 0.93, 2)
    assert_almost_equal(pvalue, 0.01, 3)

    # set random y
    y = np.mod(np.arange(len(y)), 3)

    score, scores, pvalue = permutation_test_score(
        svm, X, y, n_permutations=30, cv=cv, scoring="accuracy"
    )

    assert score < 0.5
    assert pvalue > 0.2


def test_permutation_test_score_allow_nans():
    # Check that permutation_test_score allows input data with NaNs
    X = np.arange(200, dtype=np.float64).reshape(10, -1)
    X[2, :] = np.nan
    y = np.repeat([0, 1], X.shape[0] / 2)
    p = Pipeline(
        [
            ("imputer", SimpleImputer(strategy="mean", missing_values=np.nan)),
            ("classifier", MockClassifier()),
        ]
    )
    permutation_test_score(p, X, y)


def test_permutation_test_score_params():
    X = np.arange(100).reshape(10, 10)
    y = np.array([0] * 5 + [1] * 5)
    clf = CheckingClassifier(expected_sample_weight=True)

    err_msg = r"Expected sample_weight to be passed"
    with pytest.raises(AssertionError, match=err_msg):
        permutation_test_score(clf, X, y)

    err_msg = r"sample_weight.shape == \(1,\), expected \(8,\)!"
    with pytest.raises(ValueError, match=err_msg):
        permutation_test_score(clf, X, y, params={"sample_weight": np.ones(1)})
    permutation_test_score(clf, X, y, params={"sample_weight": np.ones(10)})


def test_cross_val_score_allow_nans():
    # Check that cross_val_score allows input data with NaNs
    X = np.arange(200, dtype=np.float64).reshape(10, -1)
    X[2, :] = np.nan
    y = np.repeat([0, 1], X.shape[0] / 2)
    p = Pipeline(
        [
            ("imputer", SimpleImputer(strategy="mean", missing_values=np.nan)),
            ("classifier", MockClassifier()),
        ]
    )
    cross_val_score(p, X, y)


def test_cross_val_score_multilabel():
    X = np.array(
        [
            [-3, 4],
            [2, 4],
            [3, 3],
            [0, 2],
            [-3, 1],
            [-2, 1],
            [0, 0],
            [-2, -1],
            [-1, -2],
            [1, -2],
        ]
    )
    y = np.array(
        [[1, 1], [0, 1], [0, 1], [0, 1], [1, 1], [0, 1], [1, 0], [1, 1], [1, 0], [0, 0]]
    )
    clf = KNeighborsClassifier(n_neighbors=1)
    scoring_micro = make_scorer(precision_score, average="micro")
    scoring_macro = make_scorer(precision_score, average="macro")
    scoring_samples = make_scorer(precision_score, average="samples")
    score_micro = cross_val_score(clf, X, y, scoring=scoring_micro)
    score_macro = cross_val_score(clf, X, y, scoring=scoring_macro)
    score_samples = cross_val_score(clf, X, y, scoring=scoring_samples)
    assert_almost_equal(score_micro, [1, 1 / 2, 3 / 4, 1 / 2, 1 / 3])
    assert_almost_equal(score_macro, [1, 1 / 2, 3 / 4, 1 / 2, 1 / 4])
    assert_almost_equal(score_samples, [1, 1 / 2, 3 / 4, 1 / 2, 1 / 4])


@pytest.mark.parametrize("coo_container", COO_CONTAINERS)
def test_cross_val_predict(coo_container):
    X, y = load_diabetes(return_X_y=True)
    cv = KFold()

    est = Ridge()

    # Naive loop (should be same as cross_val_predict):
    preds2 = np.zeros_like(y)
    for train, test in cv.split(X, y):
        est.fit(X[train], y[train])
        preds2[test] = est.predict(X[test])

    preds = cross_val_predict(est, X, y, cv=cv)
    assert_array_almost_equal(preds, preds2)

    preds = cross_val_predict(est, X, y)
    assert len(preds) == len(y)

    cv = LeaveOneOut()
    preds = cross_val_predict(est, X, y, cv=cv)
    assert len(preds) == len(y)

    Xsp = X.copy()
    Xsp *= Xsp > np.median(Xsp)
    Xsp = coo_container(Xsp)
    preds = cross_val_predict(est, Xsp, y)
    assert_array_almost_equal(len(preds), len(y))

    preds = cross_val_predict(KMeans(n_init="auto"), X)
    assert len(preds) == len(y)

    class BadCV:
        def split(self, X, y=None, groups=None):
            for i in range(4):
                yield np.array([0, 1, 2, 3]), np.array([4, 5, 6, 7, 8])

    with pytest.raises(ValueError):
        cross_val_predict(est, X, y, cv=BadCV())

    X, y = load_iris(return_X_y=True)

    warning_message = (
        r"Number of classes in training fold \(2\) does "
        r"not match total number of classes \(3\). "
        "Results may not be appropriate for your use case."
    )
    with pytest.warns(RuntimeWarning, match=warning_message):
        cross_val_predict(
            LogisticRegression(solver="liblinear"),
            X,
            y,
            method="predict_proba",
            cv=KFold(2),
        )


def test_cross_val_predict_decision_function_shape():
    X, y = make_classification(n_classes=2, n_samples=50, random_state=0)

    preds = cross_val_predict(LogisticRegression(), X, y, method="decision_function")
    assert preds.shape == (50,)

    X, y = load_iris(return_X_y=True)

    preds = cross_val_predict(LogisticRegression(), X, y, method="decision_function")
    assert preds.shape == (150, 3)

    # This specifically tests imbalanced splits for binary
    # classification with decision_function. This is only
    # applicable to classifiers that can be fit on a single
    # class.
    X = X[:100]
    y = y[:100]
    error_message = (
        "Only 1 class/es in training fold,"
        " but 2 in overall dataset. This"
        " is not supported for decision_function"
        " with imbalanced folds. To fix "
        "this, use a cross-validation technique "
        "resulting in properly stratified folds"
    )
    with pytest.raises(ValueError, match=error_message):
        cross_val_predict(
            RidgeClassifier(), X, y, method="decision_function", cv=KFold(2)
        )

    X, y = load_digits(return_X_y=True)
    est = SVC(kernel="linear", decision_function_shape="ovo")

    preds = cross_val_predict(est, X, y, method="decision_function")
    assert preds.shape == (1797, 45)

    ind = np.argsort(y)
    X, y = X[ind], y[ind]
    error_message_regexp = (
        r"Output shape \(599L?, 21L?\) of "
        "decision_function does not match number of "
        r"classes \(7\) in fold. Irregular "
        "decision_function .*"
    )
    with pytest.raises(ValueError, match=error_message_regexp):
        cross_val_predict(est, X, y, cv=KFold(n_splits=3), method="decision_function")


def test_cross_val_predict_predict_proba_shape():
    X, y = make_classification(n_classes=2, n_samples=50, random_state=0)

    preds = cross_val_predict(LogisticRegression(), X, y, method="predict_proba")
    assert preds.shape == (50, 2)

    X, y = load_iris(return_X_y=True)

    preds = cross_val_predict(LogisticRegression(), X, y, method="predict_proba")
    assert preds.shape == (150, 3)


def test_cross_val_predict_predict_log_proba_shape():
    X, y = make_classification(n_classes=2, n_samples=50, random_state=0)

    preds = cross_val_predict(LogisticRegression(), X, y, method="predict_log_proba")
    assert preds.shape == (50, 2)

    X, y = load_iris(return_X_y=True)

    preds = cross_val_predict(LogisticRegression(), X, y, method="predict_log_proba")
    assert preds.shape == (150, 3)


@pytest.mark.parametrize("coo_container", COO_CONTAINERS)
def test_cross_val_predict_input_types(coo_container):
    iris = load_iris()
    X, y = iris.data, iris.target
    X_sparse = coo_container(X)
    multioutput_y = np.column_stack([y, y[::-1]])

    clf = Ridge(fit_intercept=False, random_state=0)
    # 3 fold cv is used --> at least 3 samples per class
    # Smoke test
    predictions = cross_val_predict(clf, X, y)
    assert predictions.shape == (150,)

    # test with multioutput y
    predictions = cross_val_predict(clf, X_sparse, multioutput_y)
    assert predictions.shape == (150, 2)

    predictions = cross_val_predict(clf, X_sparse, y)
    assert_array_equal(predictions.shape, (150,))

    # test with multioutput y
    predictions = cross_val_predict(clf, X_sparse, multioutput_y)
    assert_array_equal(predictions.shape, (150, 2))

    # test with X and y as list
    list_check = lambda x: isinstance(x, list)
    clf = CheckingClassifier(check_X=list_check)
    predictions = cross_val_predict(clf, X.tolist(), y.tolist())

    clf = CheckingClassifier(check_y=list_check)
    predictions = cross_val_predict(clf, X, y.tolist())

    # test with X and y as list and non empty method
    predictions = cross_val_predict(
        LogisticRegression(),
        X.tolist(),
        y.tolist(),
        method="decision_function",
    )
    predictions = cross_val_predict(
        LogisticRegression(),
        X,
        y.tolist(),
        method="decision_function",
    )

    # test with 3d X and
    X_3d = X[:, :, np.newaxis]
    check_3d = lambda x: x.ndim == 3
    clf = CheckingClassifier(check_X=check_3d)
    predictions = cross_val_predict(clf, X_3d, y)
    assert_array_equal(predictions.shape, (150,))


def test_cross_val_predict_pandas():
    # check cross_val_score doesn't destroy pandas dataframe
    types = [(MockDataFrame, MockDataFrame)]
    try:
        from pandas import DataFrame, Series

        types.append((Series, DataFrame))
    except ImportError:
        pass
    for TargetType, InputFeatureType in types:
        # X dataframe, y series
        X_df, y_ser = InputFeatureType(X), TargetType(y2)
        check_df = lambda x: isinstance(x, InputFeatureType)
        check_series = lambda x: isinstance(x, TargetType)
        clf = CheckingClassifier(check_X=check_df, check_y=check_series)
        cross_val_predict(clf, X_df, y_ser, cv=3)


def test_cross_val_predict_unbalanced():
    X, y = make_classification(
        n_samples=100,
        n_features=2,
        n_redundant=0,
        n_informative=2,
        n_clusters_per_class=1,
        random_state=1,
    )
    # Change the first sample to a new class
    y[0] = 2
    clf = LogisticRegression(random_state=1)
    cv = StratifiedKFold(n_splits=2)
    train, test = list(cv.split(X, y))
    yhat_proba = cross_val_predict(clf, X, y, cv=cv, method="predict_proba")
    assert y[test[0]][0] == 2  # sanity check for further assertions
    assert np.all(yhat_proba[test[0]][:, 2] == 0)
    assert np.all(yhat_proba[test[0]][:, 0:1] > 0)
    assert np.all(yhat_proba[test[1]] > 0)
    assert_array_almost_equal(yhat_proba.sum(axis=1), np.ones(y.shape), decimal=12)


def test_cross_val_predict_y_none():
    # ensure that cross_val_predict works when y is None
    mock_classifier = MockClassifier()
    rng = np.random.RandomState(42)
    X = rng.rand(100, 10)
    y_hat = cross_val_predict(mock_classifier, X, y=None, cv=5, method="predict")
    assert_allclose(X[:, 0], y_hat)
    y_hat_proba = cross_val_predict(
        mock_classifier, X, y=None, cv=5, method="predict_proba"
    )
    assert_allclose(X, y_hat_proba)


@pytest.mark.parametrize("coo_container", COO_CONTAINERS)
def test_cross_val_score_sparse_fit_params(coo_container):
    iris = load_iris()
    X, y = iris.data, iris.target
    clf = MockClassifier()
    fit_params = {"sparse_sample_weight": coo_container(np.eye(X.shape[0]))}
    a = cross_val_score(clf, X, y, params=fit_params, cv=3)
    assert_array_equal(a, np.ones(3))


def test_learning_curve():
    n_samples = 30
    n_splits = 3
    X, y = make_classification(
        n_samples=n_samples,
        n_features=1,
        n_informative=1,
        n_redundant=0,
        n_classes=2,
        n_clusters_per_class=1,
        random_state=0,
    )
    estimator = MockImprovingEstimator(n_samples * ((n_splits - 1) / n_splits))
    for shuffle_train in [False, True]:
        with warnings.catch_warnings(record=True) as w:
            (
                train_sizes,
                train_scores,
                test_scores,
                fit_times,
                score_times,
            ) = learning_curve(
                estimator,
                X,
                y,
                cv=KFold(n_splits=n_splits),
                train_sizes=np.linspace(0.1, 1.0, 10),
                shuffle=shuffle_train,
                return_times=True,
            )
        if len(w) > 0:
            raise RuntimeError("Unexpected warning: %r" % w[0].message)
        assert train_scores.shape == (10, 3)
        assert test_scores.shape == (10, 3)
        assert fit_times.shape == (10, 3)
        assert score_times.shape == (10, 3)
        assert_array_equal(train_sizes, np.linspace(2, 20, 10))
        assert_array_almost_equal(train_scores.mean(axis=1), np.linspace(1.9, 1.0, 10))
        assert_array_almost_equal(test_scores.mean(axis=1), np.linspace(0.1, 1.0, 10))

        # Cannot use assert_array_almost_equal for fit and score times because
        # the values are hardware-dependant
        assert fit_times.dtype == "float64"
        assert score_times.dtype == "float64"

        # Test a custom cv splitter that can iterate only once
        with warnings.catch_warnings(record=True) as w:
            train_sizes2, train_scores2, test_scores2 = learning_curve(
                estimator,
                X,
                y,
                cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples),
                train_sizes=np.linspace(0.1, 1.0, 10),
                shuffle=shuffle_train,
            )
        if len(w) > 0:
            raise RuntimeError("Unexpected warning: %r" % w[0].message)
        assert_array_almost_equal(train_scores2, train_scores)
        assert_array_almost_equal(test_scores2, test_scores)


def test_learning_curve_unsupervised():
    X, _ = make_classification(
        n_samples=30,
        n_features=1,
        n_informative=1,
        n_redundant=0,
        n_classes=2,
        n_clusters_per_class=1,
        random_state=0,
    )
    estimator = MockImprovingEstimator(20)
    train_sizes, train_scores, test_scores = learning_curve(
        estimator, X, y=None, cv=3, train_sizes=np.linspace(0.1, 1.0, 10)
    )
    assert_array_equal(train_sizes, np.linspace(2, 20, 10))
    assert_array_almost_equal(train_scores.mean(axis=1), np.linspace(1.9, 1.0, 10))
    assert_array_almost_equal(test_scores.mean(axis=1), np.linspace(0.1, 1.0, 10))


def test_learning_curve_verbose():
    X, y = make_classification(
        n_samples=30,
        n_features=1,
        n_informative=1,
        n_redundant=0,
        n_classes=2,
        n_clusters_per_class=1,
        random_state=0,
    )
    estimator = MockImprovingEstimator(20)

    old_stdout = sys.stdout
    sys.stdout = StringIO()
    try:
        train_sizes, train_scores, test_scores = learning_curve(
            estimator, X, y, cv=3, verbose=1
        )
    finally:
        out = sys.stdout.getvalue()
        sys.stdout.close()
        sys.stdout = old_stdout

    assert "[learning_curve]" in out


def test_learning_curve_incremental_learning_not_possible():
    X, y = make_classification(
        n_samples=2,
        n_features=1,
        n_informative=1,
        n_redundant=0,
        n_classes=2,
        n_clusters_per_class=1,
        random_state=0,
    )
    # The mockup does not have partial_fit()
    estimator = MockImprovingEstimator(1)
    with pytest.raises(ValueError):
        learning_curve(estimator, X, y, exploit_incremental_learning=True)


def test_learning_curve_incremental_learning():
    X, y = make_classification(
        n_samples=30,
        n_features=1,
        n_informative=1,
        n_redundant=0,
        n_classes=2,
        n_clusters_per_class=1,
        random_state=0,
    )
    estimator = MockIncrementalImprovingEstimator(20)
    for shuffle_train in [False, True]:
        train_sizes, train_scores, test_scores = learning_curve(
            estimator,
            X,
            y,
            cv=3,
            exploit_incremental_learning=True,
            train_sizes=np.linspace(0.1, 1.0, 10),
            shuffle=shuffle_train,
        )
        assert_array_equal(train_sizes, np.linspace(2, 20, 10))
        assert_array_almost_equal(train_scores.mean(axis=1), np.linspace(1.9, 1.0, 10))
        assert_array_almost_equal(test_scores.mean(axis=1), np.linspace(0.1, 1.0, 10))


def test_learning_curve_incremental_learning_unsupervised():
    X, _ = make_classification(
        n_samples=30,
        n_features=1,
        n_informative=1,
        n_redundant=0,
        n_classes=2,
        n_clusters_per_class=1,
        random_state=0,
    )
    estimator = MockIncrementalImprovingEstimator(20)
    train_sizes, train_scores, test_scores = learning_curve(
        estimator,
        X,
        y=None,
        cv=3,
        exploit_incremental_learning=True,
        train_sizes=np.linspace(0.1, 1.0, 10),
    )
    assert_array_equal(train_sizes, np.linspace(2, 20, 10))
    assert_array_almost_equal(train_scores.mean(axis=1), np.linspace(1.9, 1.0, 10))
    assert_array_almost_equal(test_scores.mean(axis=1), np.linspace(0.1, 1.0, 10))


def test_learning_curve_batch_and_incremental_learning_are_equal():
    X, y = make_classification(
        n_samples=30,
        n_features=1,
        n_informative=1,
        n_redundant=0,
        n_classes=2,
        n_clusters_per_class=1,
        random_state=0,
    )
    train_sizes = np.linspace(0.2, 1.0, 5)
    estimator = PassiveAggressiveClassifier(max_iter=1, tol=None, shuffle=False)

    train_sizes_inc, train_scores_inc, test_scores_inc = learning_curve(
        estimator,
        X,
        y,
        train_sizes=train_sizes,
        cv=3,
        exploit_incremental_learning=True,
    )
    train_sizes_batch, train_scores_batch, test_scores_batch = learning_curve(
        estimator,
        X,
        y,
        cv=3,
        train_sizes=train_sizes,
        exploit_incremental_learning=False,
    )

    assert_array_equal(train_sizes_inc, train_sizes_batch)
    assert_array_almost_equal(
        train_scores_inc.mean(axis=1), train_scores_batch.mean(axis=1)
    )
    assert_array_almost_equal(
        test_scores_inc.mean(axis=1), test_scores_batch.mean(axis=1)
    )


def test_learning_curve_n_sample_range_out_of_bounds():
    X, y = make_classification(
        n_samples=30,
        n_features=1,
        n_informative=1,
        n_redundant=0,
        n_classes=2,
        n_clusters_per_class=1,
        random_state=0,
    )
    estimator = MockImprovingEstimator(20)
    with pytest.raises(ValueError):
        learning_curve(estimator, X, y, cv=3, train_sizes=[0, 1])
    with pytest.raises(ValueError):
        learning_curve(estimator, X, y, cv=3, train_sizes=[0.0, 1.0])
    with pytest.raises(ValueError):
        learning_curve(estimator, X, y, cv=3, train_sizes=[0.1, 1.1])
    with pytest.raises(ValueError):
        learning_curve(estimator, X, y, cv=3, train_sizes=[0, 20])
    with pytest.raises(ValueError):
        learning_curve(estimator, X, y, cv=3, train_sizes=[1, 21])


def test_learning_curve_remove_duplicate_sample_sizes():
    X, y = make_classification(
        n_samples=3,
        n_features=1,
        n_informative=1,
        n_redundant=0,
        n_classes=2,
        n_clusters_per_class=1,
        random_state=0,
    )
    estimator = MockImprovingEstimator(2)
    warning_message = (
        "Removed duplicate entries from 'train_sizes'. Number of ticks "
        "will be less than the size of 'train_sizes': 2 instead of 3."
    )
    with pytest.warns(RuntimeWarning, match=warning_message):
        train_sizes, _, _ = learning_curve(
            estimator, X, y, cv=3, train_sizes=np.linspace(0.33, 1.0, 3)
        )
    assert_array_equal(train_sizes, [1, 2])


def test_learning_curve_with_boolean_indices():
    X, y = make_classification(
        n_samples=30,
        n_features=1,
        n_informative=1,
        n_redundant=0,
        n_classes=2,
        n_clusters_per_class=1,
        random_state=0,
    )
    estimator = MockImprovingEstimator(20)
    cv = KFold(n_splits=3)
    train_sizes, train_scores, test_scores = learning_curve(
        estimator, X, y, cv=cv, train_sizes=np.linspace(0.1, 1.0, 10)
    )
    assert_array_equal(train_sizes, np.linspace(2, 20, 10))
    assert_array_almost_equal(train_scores.mean(axis=1), np.linspace(1.9, 1.0, 10))
    assert_array_almost_equal(test_scores.mean(axis=1), np.linspace(0.1, 1.0, 10))


def test_learning_curve_with_shuffle():
    # Following test case was designed this way to verify the code
    # changes made in pull request: #7506.
    X = np.array(
        [
            [1, 2],
            [3, 4],
            [5, 6],
            [7, 8],
            [11, 12],
            [13, 14],
            [15, 16],
            [17, 18],
            [19, 20],
            [7, 8],
            [9, 10],
            [11, 12],
            [13, 14],
            [15, 16],
            [17, 18],
        ]
    )
    y = np.array([1, 1, 1, 2, 3, 4, 1, 1, 2, 3, 4, 1, 2, 3, 4])
    groups = np.array([1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 4, 4, 4, 4])
    # Splits on these groups fail without shuffle as the first iteration
    # of the learning curve doesn't contain label 4 in the training set.
    estimator = PassiveAggressiveClassifier(max_iter=5, tol=None, shuffle=False)

    cv = GroupKFold(n_splits=2)
    train_sizes_batch, train_scores_batch, test_scores_batch = learning_curve(
        estimator,
        X,
        y,
        cv=cv,
        n_jobs=1,
        train_sizes=np.linspace(0.3, 1.0, 3),
        groups=groups,
        shuffle=True,
        random_state=2,
    )
    assert_array_almost_equal(
        train_scores_batch.mean(axis=1), np.array([0.75, 0.3, 0.36111111])
    )
    assert_array_almost_equal(
        test_scores_batch.mean(axis=1), np.array([0.36111111, 0.25, 0.25])
    )
    with pytest.raises(ValueError):
        learning_curve(
            estimator,
            X,
            y,
            cv=cv,
            n_jobs=1,
            train_sizes=np.linspace(0.3, 1.0, 3),
            groups=groups,
            error_score="raise",
        )

    train_sizes_inc, train_scores_inc, test_scores_inc = learning_curve(
        estimator,
        X,
        y,
        cv=cv,
        n_jobs=1,
        train_sizes=np.linspace(0.3, 1.0, 3),
        groups=groups,
        shuffle=True,
        random_state=2,
        exploit_incremental_learning=True,
    )
    assert_array_almost_equal(
        train_scores_inc.mean(axis=1), train_scores_batch.mean(axis=1)
    )
    assert_array_almost_equal(
        test_scores_inc.mean(axis=1), test_scores_batch.mean(axis=1)
    )


def test_learning_curve_params():
    X = np.arange(100).reshape(10, 10)
    y = np.array([0] * 5 + [1] * 5)
    clf = CheckingClassifier(expected_sample_weight=True)

    err_msg = r"Expected sample_weight to be passed"
    with pytest.raises(AssertionError, match=err_msg):
        learning_curve(clf, X, y, error_score="raise")

    err_msg = r"sample_weight.shape == \(1,\), expected \(2,\)!"
    with pytest.raises(ValueError, match=err_msg):
        learning_curve(
            clf, X, y, error_score="raise", params={"sample_weight": np.ones(1)}
        )
    learning_curve(
        clf, X, y, error_score="raise", params={"sample_weight": np.ones(10)}
    )


def test_learning_curve_incremental_learning_params():
    X, y = make_classification(
        n_samples=30,
        n_features=1,
        n_informative=1,
        n_redundant=0,
        n_classes=2,
        n_clusters_per_class=1,
        random_state=0,
    )
    estimator = MockIncrementalImprovingEstimator(20, ["sample_weight"])
    err_msg = r"Expected fit parameter\(s\) \['sample_weight'\] not seen."
    with pytest.raises(AssertionError, match=err_msg):
        learning_curve(
            estimator,
            X,
            y,
            cv=3,
            exploit_incremental_learning=True,
            train_sizes=np.linspace(0.1, 1.0, 10),
            error_score="raise",
        )

    err_msg = "Fit parameter sample_weight has length 3; expected"
    with pytest.raises(AssertionError, match=err_msg):
        learning_curve(
            estimator,
            X,
            y,
            cv=3,
            exploit_incremental_learning=True,
            train_sizes=np.linspace(0.1, 1.0, 10),
            error_score="raise",
            params={"sample_weight": np.ones(3)},
        )

    learning_curve(
        estimator,
        X,
        y,
        cv=3,
        exploit_incremental_learning=True,
        train_sizes=np.linspace(0.1, 1.0, 10),
        error_score="raise",
        params={"sample_weight": np.ones(2)},
    )


def test_validation_curve():
    X, y = make_classification(
        n_samples=2,
        n_features=1,
        n_informative=1,
        n_redundant=0,
        n_classes=2,
        n_clusters_per_class=1,
        random_state=0,
    )
    param_range = np.linspace(0, 1, 10)
    with warnings.catch_warnings(record=True) as w:
        train_scores, test_scores = validation_curve(
            MockEstimatorWithParameter(),
            X,
            y,
            param_name="param",
            param_range=param_range,
            cv=2,
        )
    if len(w) > 0:
        raise RuntimeError("Unexpected warning: %r" % w[0].message)

    assert_array_almost_equal(train_scores.mean(axis=1), param_range)
    assert_array_almost_equal(test_scores.mean(axis=1), 1 - param_range)


def test_validation_curve_clone_estimator():
    X, y = make_classification(
        n_samples=2,
        n_features=1,
        n_informative=1,
        n_redundant=0,
        n_classes=2,
        n_clusters_per_class=1,
        random_state=0,
    )

    param_range = np.linspace(1, 0, 10)
    _, _ = validation_curve(
        MockEstimatorWithSingleFitCallAllowed(),
        X,
        y,
        param_name="param",
        param_range=param_range,
        cv=2,
    )


def test_validation_curve_cv_splits_consistency():
    n_samples = 100
    n_splits = 5
    X, y = make_classification(n_samples=100, random_state=0)

    scores1 = validation_curve(
        SVC(kernel="linear", random_state=0),
        X,
        y,
        param_name="C",
        param_range=[0.1, 0.1, 0.2, 0.2],
        cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples),
    )
    # The OneTimeSplitter is a non-re-entrant cv splitter. Unless, the
    # `split` is called for each parameter, the following should produce
    # identical results for param setting 1 and param setting 2 as both have
    # the same C value.
    assert_array_almost_equal(*np.vsplit(np.hstack(scores1)[(0, 2, 1, 3), :], 2))

    scores2 = validation_curve(
        SVC(kernel="linear", random_state=0),
        X,
        y,
        param_name="C",
        param_range=[0.1, 0.1, 0.2, 0.2],
        cv=KFold(n_splits=n_splits, shuffle=True),
    )

    # For scores2, compare the 1st and 2nd parameter's scores
    # (Since the C value for 1st two param setting is 0.1, they must be
    # consistent unless the train test folds differ between the param settings)
    assert_array_almost_equal(*np.vsplit(np.hstack(scores2)[(0, 2, 1, 3), :], 2))

    scores3 = validation_curve(
        SVC(kernel="linear", random_state=0),
        X,
        y,
        param_name="C",
        param_range=[0.1, 0.1, 0.2, 0.2],
        cv=KFold(n_splits=n_splits),
    )

    # OneTimeSplitter is basically unshuffled KFold(n_splits=5). Sanity check.
    assert_array_almost_equal(np.array(scores3), np.array(scores1))


def test_validation_curve_params():
    X = np.arange(100).reshape(10, 10)
    y = np.array([0] * 5 + [1] * 5)
    clf = CheckingClassifier(expected_sample_weight=True)

    err_msg = r"Expected sample_weight to be passed"
    with pytest.raises(AssertionError, match=err_msg):
        validation_curve(
            clf,
            X,
            y,
            param_name="foo_param",
            param_range=[1, 2, 3],
            error_score="raise",
        )

    err_msg = r"sample_weight.shape == \(1,\), expected \(8,\)!"
    with pytest.raises(ValueError, match=err_msg):
        validation_curve(
            clf,
            X,
            y,
            param_name="foo_param",
            param_range=[1, 2, 3],
            error_score="raise",
            params={"sample_weight": np.ones(1)},
        )
    validation_curve(
        clf,
        X,
        y,
        param_name="foo_param",
        param_range=[1, 2, 3],
        error_score="raise",
        params={"sample_weight": np.ones(10)},
    )


def test_check_is_permutation():
    rng = np.random.RandomState(0)
    p = np.arange(100)
    rng.shuffle(p)
    assert _check_is_permutation(p, 100)
    assert not _check_is_permutation(np.delete(p, 23), 100)

    p[0] = 23
    assert not _check_is_permutation(p, 100)

    # Check if the additional duplicate indices are caught
    assert not _check_is_permutation(np.hstack((p, 0)), 100)


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_cross_val_predict_sparse_prediction(csr_container):
    # check that cross_val_predict gives same result for sparse and dense input
    X, y = make_multilabel_classification(
        n_classes=2,
        n_labels=1,
        allow_unlabeled=False,
        return_indicator=True,
        random_state=1,
    )
    X_sparse = csr_container(X)
    y_sparse = csr_container(y)
    classif = OneVsRestClassifier(SVC(kernel="linear"))
    preds = cross_val_predict(classif, X, y, cv=10)
    preds_sparse = cross_val_predict(classif, X_sparse, y_sparse, cv=10)
    preds_sparse = preds_sparse.toarray()
    assert_array_almost_equal(preds_sparse, preds)


def check_cross_val_predict_binary(est, X, y, method):
    """Helper for tests of cross_val_predict with binary classification"""
    cv = KFold(n_splits=3, shuffle=False)

    # Generate expected outputs
    if y.ndim == 1:
        exp_shape = (len(X),) if method == "decision_function" else (len(X), 2)
    else:
        exp_shape = y.shape
    expected_predictions = np.zeros(exp_shape)
    for train, test in cv.split(X, y):
        est = clone(est).fit(X[train], y[train])
        expected_predictions[test] = getattr(est, method)(X[test])

    # Check actual outputs for several representations of y
    for tg in [y, y + 1, y - 2, y.astype("str")]:
        assert_allclose(
            cross_val_predict(est, X, tg, method=method, cv=cv), expected_predictions
        )


def check_cross_val_predict_multiclass(est, X, y, method):
    """Helper for tests of cross_val_predict with multiclass classification"""
    cv = KFold(n_splits=3, shuffle=False)

    # Generate expected outputs
    float_min = np.finfo(np.float64).min
    default_values = {
        "decision_function": float_min,
        "predict_log_proba": float_min,
        "predict_proba": 0,
    }
    expected_predictions = np.full(
        (len(X), len(set(y))), default_values[method], dtype=np.float64
    )
    _, y_enc = np.unique(y, return_inverse=True)
    for train, test in cv.split(X, y_enc):
        est = clone(est).fit(X[train], y_enc[train])
        fold_preds = getattr(est, method)(X[test])
        i_cols_fit = np.unique(y_enc[train])
        expected_predictions[np.ix_(test, i_cols_fit)] = fold_preds

    # Check actual outputs for several representations of y
    for tg in [y, y + 1, y - 2, y.astype("str")]:
        assert_allclose(
            cross_val_predict(est, X, tg, method=method, cv=cv), expected_predictions
        )


def check_cross_val_predict_multilabel(est, X, y, method):
    """Check the output of cross_val_predict for 2D targets using
    Estimators which provide a predictions as a list with one
    element per class.
    """
    cv = KFold(n_splits=3, shuffle=False)

    # Create empty arrays of the correct size to hold outputs
    float_min = np.finfo(np.float64).min
    default_values = {
        "decision_function": float_min,
        "predict_log_proba": float_min,
        "predict_proba": 0,
    }
    n_targets = y.shape[1]
    expected_preds = []
    for i_col in range(n_targets):
        n_classes_in_label = len(set(y[:, i_col]))
        if n_classes_in_label == 2 and method == "decision_function":
            exp_shape = (len(X),)
        else:
            exp_shape = (len(X), n_classes_in_label)
        expected_preds.append(
            np.full(exp_shape, default_values[method], dtype=np.float64)
        )

    # Generate expected outputs
    y_enc_cols = [
        np.unique(y[:, i], return_inverse=True)[1][:, np.newaxis]
        for i in range(y.shape[1])
    ]
    y_enc = np.concatenate(y_enc_cols, axis=1)
    for train, test in cv.split(X, y_enc):
        est = clone(est).fit(X[train], y_enc[train])
        fold_preds = getattr(est, method)(X[test])
        for i_col in range(n_targets):
            fold_cols = np.unique(y_enc[train][:, i_col])
            if expected_preds[i_col].ndim == 1:
                # Decision function with <=2 classes
                expected_preds[i_col][test] = fold_preds[i_col]
            else:
                idx = np.ix_(test, fold_cols)
                expected_preds[i_col][idx] = fold_preds[i_col]

    # Check actual outputs for several representations of y
    for tg in [y, y + 1, y - 2, y.astype("str")]:
        cv_predict_output = cross_val_predict(est, X, tg, method=method, cv=cv)
        assert len(cv_predict_output) == len(expected_preds)
        for i in range(len(cv_predict_output)):
            assert_allclose(cv_predict_output[i], expected_preds[i])


def check_cross_val_predict_with_method_binary(est):
    # This test includes the decision_function with two classes.
    # This is a special case: it has only one column of output.
    X, y = make_classification(n_classes=2, random_state=0)
    for method in ["decision_function", "predict_proba", "predict_log_proba"]:
        check_cross_val_predict_binary(est, X, y, method)


def check_cross_val_predict_with_method_multiclass(est):
    iris = load_iris()
    X, y = iris.data, iris.target
    X, y = shuffle(X, y, random_state=0)
    for method in ["decision_function", "predict_proba", "predict_log_proba"]:
        check_cross_val_predict_multiclass(est, X, y, method)


def test_cross_val_predict_with_method():
    check_cross_val_predict_with_method_binary(LogisticRegression())
    check_cross_val_predict_with_method_multiclass(LogisticRegression())


def test_cross_val_predict_method_checking():
    # Regression test for issue #9639. Tests that cross_val_predict does not
    # check estimator methods (e.g. predict_proba) before fitting
    iris = load_iris()
    X, y = iris.data, iris.target
    X, y = shuffle(X, y, random_state=0)
    for method in ["decision_function", "predict_proba", "predict_log_proba"]:
        est = SGDClassifier(loss="log_loss", random_state=2)
        check_cross_val_predict_multiclass(est, X, y, method)


def test_gridsearchcv_cross_val_predict_with_method():
    iris = load_iris()
    X, y = iris.data, iris.target
    X, y = shuffle(X, y, random_state=0)
    est = GridSearchCV(LogisticRegression(random_state=42), {"C": [0.1, 1]}, cv=2)
    for method in ["decision_function", "predict_proba", "predict_log_proba"]:
        check_cross_val_predict_multiclass(est, X, y, method)


def test_cross_val_predict_with_method_multilabel_ovr():
    # OVR does multilabel predictions, but only arrays of
    # binary indicator columns. The output of predict_proba
    # is a 2D array with shape (n_samples, n_classes).
    n_samp = 100
    n_classes = 4
    X, y = make_multilabel_classification(
        n_samples=n_samp, n_labels=3, n_classes=n_classes, n_features=5, random_state=42
    )
    est = OneVsRestClassifier(LogisticRegression(solver="liblinear", random_state=0))
    for method in ["predict_proba", "decision_function"]:
        check_cross_val_predict_binary(est, X, y, method=method)


class RFWithDecisionFunction(RandomForestClassifier):
    # None of the current multioutput-multiclass estimators have
    # decision function methods. Create a mock decision function
    # to test the cross_val_predict function's handling of this case.
    def decision_function(self, X):
        probs = self.predict_proba(X)
        msg = "This helper should only be used on multioutput-multiclass tasks"
        assert isinstance(probs, list), msg
        probs = [p[:, -1] if p.shape[1] == 2 else p for p in probs]
        return probs


def test_cross_val_predict_with_method_multilabel_rf():
    # The RandomForest allows multiple classes in each label.
    # Output of predict_proba is a list of outputs of predict_proba
    # for each individual label.
    n_classes = 4
    X, y = make_multilabel_classification(
        n_samples=100, n_labels=3, n_classes=n_classes, n_features=5, random_state=42
    )
    y[:, 0] += y[:, 1]  # Put three classes in the first column
    for method in ["predict_proba", "predict_log_proba", "decision_function"]:
        est = RFWithDecisionFunction(n_estimators=5, random_state=0)
        with warnings.catch_warnings():
            # Suppress "RuntimeWarning: divide by zero encountered in log"
            warnings.simplefilter("ignore")
            check_cross_val_predict_multilabel(est, X, y, method=method)


def test_cross_val_predict_with_method_rare_class():
    # Test a multiclass problem where one class will be missing from
    # one of the CV training sets.
    rng = np.random.RandomState(0)
    X = rng.normal(0, 1, size=(14, 10))
    y = np.array([0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 3])
    est = LogisticRegression()
    for method in ["predict_proba", "predict_log_proba", "decision_function"]:
        with warnings.catch_warnings():
            # Suppress warning about too few examples of a class
            warnings.simplefilter("ignore")
            check_cross_val_predict_multiclass(est, X, y, method)


def test_cross_val_predict_with_method_multilabel_rf_rare_class():
    # The RandomForest allows anything for the contents of the labels.
    # Output of predict_proba is a list of outputs of predict_proba
    # for each individual label.
    # In this test, the first label has a class with a single example.
    # We'll have one CV fold where the training data don't include it.
    rng = np.random.RandomState(0)
    X = rng.normal(0, 1, size=(5, 10))
    y = np.array([[0, 0], [1, 1], [2, 1], [0, 1], [1, 0]])
    for method in ["predict_proba", "predict_log_proba"]:
        est = RFWithDecisionFunction(n_estimators=5, random_state=0)
        with warnings.catch_warnings():
            # Suppress "RuntimeWarning: divide by zero encountered in log"
            warnings.simplefilter("ignore")
            check_cross_val_predict_multilabel(est, X, y, method=method)


def get_expected_predictions(X, y, cv, classes, est, method):
    expected_predictions = np.zeros([len(y), classes])
    func = getattr(est, method)

    for train, test in cv.split(X, y):
        est.fit(X[train], y[train])
        expected_predictions_ = func(X[test])
        # To avoid 2 dimensional indexing
        if method == "predict_proba":
            exp_pred_test = np.zeros((len(test), classes))
        else:
            exp_pred_test = np.full(
                (len(test), classes), np.finfo(expected_predictions.dtype).min
            )
        exp_pred_test[:, est.classes_] = expected_predictions_
        expected_predictions[test] = exp_pred_test

    return expected_predictions


def test_cross_val_predict_class_subset():
    X = np.arange(200).reshape(100, 2)
    y = np.array([x // 10 for x in range(100)])
    classes = 10

    kfold3 = KFold(n_splits=3)
    kfold4 = KFold(n_splits=4)

    le = LabelEncoder()

    methods = ["decision_function", "predict_proba", "predict_log_proba"]
    for method in methods:
        est = LogisticRegression()

        # Test with n_splits=3
        predictions = cross_val_predict(est, X, y, method=method, cv=kfold3)

        # Runs a naive loop (should be same as cross_val_predict):
        expected_predictions = get_expected_predictions(
            X, y, kfold3, classes, est, method
        )
        assert_array_almost_equal(expected_predictions, predictions)

        # Test with n_splits=4
        predictions = cross_val_predict(est, X, y, method=method, cv=kfold4)
        expected_predictions = get_expected_predictions(
            X, y, kfold4, classes, est, method
        )
        assert_array_almost_equal(expected_predictions, predictions)

        # Testing unordered labels
        y = shuffle(np.repeat(range(10), 10), random_state=0)
        predictions = cross_val_predict(est, X, y, method=method, cv=kfold3)
        y = le.fit_transform(y)
        expected_predictions = get_expected_predictions(
            X, y, kfold3, classes, est, method
        )
        assert_array_almost_equal(expected_predictions, predictions)


def test_score_memmap():
    # Ensure a scalar score of memmap type is accepted
    iris = load_iris()
    X, y = iris.data, iris.target
    clf = MockClassifier()
    tf = tempfile.NamedTemporaryFile(mode="wb", delete=False)
    tf.write(b"Hello world!!!!!")
    tf.close()
    scores = np.memmap(tf.name, dtype=np.float64)
    score = np.memmap(tf.name, shape=(), mode="r", dtype=np.float64)
    try:
        cross_val_score(clf, X, y, scoring=lambda est, X, y: score)
        with pytest.raises(ValueError):
            cross_val_score(clf, X, y, scoring=lambda est, X, y: scores)
    finally:
        # Best effort to release the mmap file handles before deleting the
        # backing file under Windows
        scores, score = None, None
        for _ in range(3):
            try:
                os.unlink(tf.name)
                break
            except OSError:
                sleep(1.0)


def test_permutation_test_score_pandas():
    # check permutation_test_score doesn't destroy pandas dataframe
    types = [(MockDataFrame, MockDataFrame)]
    try:
        from pandas import DataFrame, Series

        types.append((Series, DataFrame))
    except ImportError:
        pass
    for TargetType, InputFeatureType in types:
        # X dataframe, y series
        iris = load_iris()
        X, y = iris.data, iris.target
        X_df, y_ser = InputFeatureType(X), TargetType(y)
        check_df = lambda x: isinstance(x, InputFeatureType)
        check_series = lambda x: isinstance(x, TargetType)
        clf = CheckingClassifier(check_X=check_df, check_y=check_series)
        permutation_test_score(clf, X_df, y_ser)


def test_fit_and_score_failing():
    # Create a failing classifier to deliberately fail
    failing_clf = FailingClassifier(FailingClassifier.FAILING_PARAMETER)
    # dummy X data
    X = np.arange(1, 10)
    train, test = np.arange(0, 5), np.arange(5, 9)
    fit_and_score_args = dict(
        estimator=failing_clf,
        X=X,
        y=None,
        scorer=dict(),
        train=train,
        test=test,
        verbose=0,
        parameters=None,
        fit_params=None,
        score_params=None,
    )
    # passing error score to trigger the warning message
    fit_and_score_args["error_score"] = "raise"
    # check if exception was raised, with default error_score='raise'
    with pytest.raises(ValueError, match="Failing classifier failed as required"):
        _fit_and_score(**fit_and_score_args)

    assert failing_clf.score() == 0.0  # FailingClassifier coverage


def test_fit_and_score_working():
    X, y = make_classification(n_samples=30, random_state=0)
    clf = SVC(kernel="linear", random_state=0)
    train, test = next(ShuffleSplit().split(X))
    # Test return_parameters option
    fit_and_score_args = dict(
        estimator=clf,
        X=X,
        y=y,
        scorer=dict(),
        train=train,
        test=test,
        verbose=0,
        parameters={"max_iter": 100, "tol": 0.1},
        fit_params=None,
        score_params=None,
        return_parameters=True,
    )
    result = _fit_and_score(**fit_and_score_args)
    assert result["parameters"] == fit_and_score_args["parameters"]


class DataDependentFailingClassifier(BaseEstimator):
    def __init__(self, max_x_value=None):
        self.max_x_value = max_x_value

    def fit(self, X, y=None):
        num_values_too_high = (X > self.max_x_value).sum()
        if num_values_too_high:
            raise ValueError(
                f"Classifier fit failed with {num_values_too_high} values too high"
            )

    def score(self, X=None, Y=None):
        return 0.0


@pytest.mark.parametrize("error_score", [np.nan, 0])
def test_cross_validate_some_failing_fits_warning(error_score):
    # Create a failing classifier to deliberately fail
    failing_clf = DataDependentFailingClassifier(max_x_value=8)
    # dummy X data
    X = np.arange(1, 10)
    y = np.ones(9)
    # passing error score to trigger the warning message
    cross_validate_args = [failing_clf, X, y]
    cross_validate_kwargs = {"cv": 3, "error_score": error_score}
    # check if the warning message type is as expected

    individual_fit_error_message = (
        "ValueError: Classifier fit failed with 1 values too high"
    )
    warning_message = re.compile(
        (
            "2 fits failed.+total of 3.+The score on these"
            " train-test partitions for these parameters will be set to"
            f" {cross_validate_kwargs['error_score']}.+{individual_fit_error_message}"
        ),
        flags=re.DOTALL,
    )

    with pytest.warns(FitFailedWarning, match=warning_message):
        cross_validate(*cross_validate_args, **cross_validate_kwargs)


@pytest.mark.parametrize("error_score", [np.nan, 0])
def test_cross_validate_all_failing_fits_error(error_score):
    # Create a failing classifier to deliberately fail
    failing_clf = FailingClassifier(FailingClassifier.FAILING_PARAMETER)
    # dummy X data
    X = np.arange(1, 10)
    y = np.ones(9)

    cross_validate_args = [failing_clf, X, y]
    cross_validate_kwargs = {"cv": 7, "error_score": error_score}

    individual_fit_error_message = "ValueError: Failing classifier failed as required"
    error_message = re.compile(
        (
            "All the 7 fits failed.+your model is misconfigured.+"
            f"{individual_fit_error_message}"
        ),
        flags=re.DOTALL,
    )

    with pytest.raises(ValueError, match=error_message):
        cross_validate(*cross_validate_args, **cross_validate_kwargs)


def _failing_scorer(estimator, X, y, error_msg):
    raise ValueError(error_msg)


@pytest.mark.filterwarnings("ignore:lbfgs failed to converge")
@pytest.mark.parametrize("error_score", [np.nan, 0, "raise"])
def test_cross_val_score_failing_scorer(error_score):
    # check that an estimator can fail during scoring in `cross_val_score` and
    # that we can optionally replaced it with `error_score`
    X, y = load_iris(return_X_y=True)
    clf = LogisticRegression(max_iter=5).fit(X, y)

    error_msg = "This scorer is supposed to fail!!!"
    failing_scorer = partial(_failing_scorer, error_msg=error_msg)

    if error_score == "raise":
        with pytest.raises(ValueError, match=error_msg):
            cross_val_score(
                clf, X, y, cv=3, scoring=failing_scorer, error_score=error_score
            )
    else:
        warning_msg = (
            "Scoring failed. The score on this train-test partition for "
            f"these parameters will be set to {error_score}"
        )
        with pytest.warns(UserWarning, match=warning_msg):
            scores = cross_val_score(
                clf, X, y, cv=3, scoring=failing_scorer, error_score=error_score
            )
            assert_allclose(scores, error_score)


@pytest.mark.filterwarnings("ignore:lbfgs failed to converge")
@pytest.mark.parametrize("error_score", [np.nan, 0, "raise"])
@pytest.mark.parametrize("return_train_score", [True, False])
@pytest.mark.parametrize("with_multimetric", [False, True])
def test_cross_validate_failing_scorer(
    error_score, return_train_score, with_multimetric
):
    # Check that an estimator can fail during scoring in `cross_validate` and
    # that we can optionally replace it with `error_score`. In the multimetric
    # case also check the result of a non-failing scorer where the other scorers
    # are failing.
    X, y = load_iris(return_X_y=True)
    clf = LogisticRegression(max_iter=5).fit(X, y)

    error_msg = "This scorer is supposed to fail!!!"
    failing_scorer = partial(_failing_scorer, error_msg=error_msg)
    if with_multimetric:
        non_failing_scorer = make_scorer(mean_squared_error)
        scoring = {
            "score_1": failing_scorer,
            "score_2": non_failing_scorer,
            "score_3": failing_scorer,
        }
    else:
        scoring = failing_scorer

    if error_score == "raise":
        with pytest.raises(ValueError, match=error_msg):
            cross_validate(
                clf,
                X,
                y,
                cv=3,
                scoring=scoring,
                return_train_score=return_train_score,
                error_score=error_score,
            )
    else:
        warning_msg = (
            "Scoring failed. The score on this train-test partition for "
            f"these parameters will be set to {error_score}"
        )
        with pytest.warns(UserWarning, match=warning_msg):
            results = cross_validate(
                clf,
                X,
                y,
                cv=3,
                scoring=scoring,
                return_train_score=return_train_score,
                error_score=error_score,
            )
            for key in results:
                if "_score" in key:
                    if "_score_2" in key:
                        # check the test (and optionally train) score for the
                        # scorer that should be non-failing
                        for i in results[key]:
                            assert isinstance(i, float)
                    else:
                        # check the test (and optionally train) score for all
                        # scorers that should be assigned to `error_score`.
                        assert_allclose(results[key], error_score)


def three_params_scorer(i, j, k):
    return 3.4213


@pytest.mark.parametrize(
    "train_score, scorer, verbose, split_prg, cdt_prg, expected",
    [
        (
            False,
            three_params_scorer,
            2,
            (1, 3),
            (0, 1),
            r"\[CV\] END ...................................................."
            r" total time=   0.\ds",
        ),
        (
            True,
            _MultimetricScorer(
                scorers={"sc1": three_params_scorer, "sc2": three_params_scorer}
            ),
            3,
            (1, 3),
            (0, 1),
            r"\[CV 2/3\] END  sc1: \(train=3.421, test=3.421\) sc2: "
            r"\(train=3.421, test=3.421\) total time=   0.\ds",
        ),
        (
            False,
            _MultimetricScorer(
                scorers={"sc1": three_params_scorer, "sc2": three_params_scorer}
            ),
            10,
            (1, 3),
            (0, 1),
            r"\[CV 2/3; 1/1\] END ....... sc1: \(test=3.421\) sc2: \(test=3.421\)"
            r" total time=   0.\ds",
        ),
    ],
)
def test_fit_and_score_verbosity(
    capsys, train_score, scorer, verbose, split_prg, cdt_prg, expected
):
    X, y = make_classification(n_samples=30, random_state=0)
    clf = SVC(kernel="linear", random_state=0)
    train, test = next(ShuffleSplit().split(X))

    # test print without train score
    fit_and_score_args = dict(
        estimator=clf,
        X=X,
        y=y,
        scorer=scorer,
        train=train,
        test=test,
        verbose=verbose,
        parameters=None,
        fit_params=None,
        score_params=None,
        return_train_score=train_score,
        split_progress=split_prg,
        candidate_progress=cdt_prg,
    )
    _fit_and_score(**fit_and_score_args)
    out, _ = capsys.readouterr()
    outlines = out.split("\n")
    if len(outlines) > 2:
        assert re.match(expected, outlines[1])
    else:
        assert re.match(expected, outlines[0])


def test_score():
    error_message = "scoring must return a number, got None"

    def two_params_scorer(estimator, X_test):
        return None

    with pytest.raises(ValueError, match=error_message):
        _score(
            estimator=None,
            X_test=None,
            y_test=None,
            scorer=two_params_scorer,
            score_params=None,
            error_score=np.nan,
        )


def test_callable_multimetric_confusion_matrix_cross_validate():
    def custom_scorer(clf, X, y):
        y_pred = clf.predict(X)
        cm = confusion_matrix(y, y_pred)
        return {"tn": cm[0, 0], "fp": cm[0, 1], "fn": cm[1, 0], "tp": cm[1, 1]}

    X, y = make_classification(n_samples=40, n_features=4, random_state=42)
    est = LinearSVC(random_state=42)
    est.fit(X, y)
    cv_results = cross_validate(est, X, y, cv=5, scoring=custom_scorer)

    score_names = ["tn", "fp", "fn", "tp"]
    for name in score_names:
        assert "test_{}".format(name) in cv_results


def test_learning_curve_partial_fit_regressors():
    """Check that regressors with partial_fit is supported.

    Non-regression test for #22981.
    """
    X, y = make_regression(random_state=42)

    # Does not error
    learning_curve(MLPRegressor(), X, y, exploit_incremental_learning=True, cv=2)


def test_learning_curve_some_failing_fits_warning(global_random_seed):
    """Checks for fit failures in `learning_curve` and raises the required warning"""

    X, y = make_classification(
        n_samples=30,
        n_classes=3,
        n_informative=6,
        shuffle=False,
        random_state=global_random_seed,
    )
    # sorting the target to trigger SVC error on the 2 first splits because a single
    # class is present
    sorted_idx = np.argsort(y)
    X, y = X[sorted_idx], y[sorted_idx]

    svc = SVC()
    warning_message = "10 fits failed out of a total of 25"

    with pytest.warns(FitFailedWarning, match=warning_message):
        _, train_score, test_score, *_ = learning_curve(
            svc, X, y, cv=5, error_score=np.nan
        )

    # the first 2 splits should lead to warnings and thus np.nan scores
    for idx in range(2):
        assert np.isnan(train_score[idx]).all()
        assert np.isnan(test_score[idx]).all()

    for idx in range(2, train_score.shape[0]):
        assert not np.isnan(train_score[idx]).any()
        assert not np.isnan(test_score[idx]).any()


def test_cross_validate_return_indices(global_random_seed):
    """Check the behaviour of `return_indices` in `cross_validate`."""
    X, y = load_iris(return_X_y=True)
    X = scale(X)  # scale features for better convergence
    estimator = LogisticRegression()

    cv = KFold(n_splits=3, shuffle=True, random_state=global_random_seed)
    cv_results = cross_validate(estimator, X, y, cv=cv, n_jobs=2, return_indices=False)
    assert "indices" not in cv_results

    cv_results = cross_validate(estimator, X, y, cv=cv, n_jobs=2, return_indices=True)
    assert "indices" in cv_results
    train_indices = cv_results["indices"]["train"]
    test_indices = cv_results["indices"]["test"]
    assert len(train_indices) == cv.n_splits
    assert len(test_indices) == cv.n_splits

    assert_array_equal([indices.size for indices in train_indices], 100)
    assert_array_equal([indices.size for indices in test_indices], 50)

    for split_idx, (expected_train_idx, expected_test_idx) in enumerate(cv.split(X, y)):
        assert_array_equal(train_indices[split_idx], expected_train_idx)
        assert_array_equal(test_indices[split_idx], expected_test_idx)


# Tests for metadata routing in cross_val* and in *curve
# ======================================================


# TODO(1.8): remove `learning_curve`, `validation_curve` and `permutation_test_score`.
@pytest.mark.parametrize(
    "func, extra_args",
    [
        (learning_curve, {}),
        (permutation_test_score, {}),
        (validation_curve, {"param_name": "alpha", "param_range": np.array([1])}),
    ],
)
def test_fit_param_deprecation(func, extra_args):
    """Check that we warn about deprecating `fit_params`."""
    with pytest.warns(FutureWarning, match="`fit_params` is deprecated"):
        func(
            estimator=ConsumingClassifier(), X=X, y=y, cv=2, fit_params={}, **extra_args
        )

    with pytest.raises(
        ValueError, match="`params` and `fit_params` cannot both be provided"
    ):
        func(
            estimator=ConsumingClassifier(),
            X=X,
            y=y,
            fit_params={},
            params={},
            **extra_args,
        )


@pytest.mark.parametrize(
    "func, extra_args",
    [
        (cross_validate, {}),
        (cross_val_score, {}),
        (cross_val_predict, {}),
        (learning_curve, {}),
        (permutation_test_score, {}),
        (validation_curve, {"param_name": "alpha", "param_range": np.array([1])}),
    ],
)
@config_context(enable_metadata_routing=True)
def test_groups_with_routing_validation(func, extra_args):
    """Check that we raise an error if `groups` are passed to the cv method instead
    of `params` when metadata routing is enabled.
    """
    with pytest.raises(ValueError, match="`groups` can only be passed if"):
        func(
            estimator=ConsumingClassifier(),
            X=X,
            y=y,
            groups=[],
            **extra_args,
        )


@pytest.mark.parametrize(
    "func, extra_args",
    [
        (cross_validate, {}),
        (cross_val_score, {}),
        (cross_val_predict, {}),
        (learning_curve, {}),
        (permutation_test_score, {}),
        (validation_curve, {"param_name": "alpha", "param_range": np.array([1])}),
    ],
)
@config_context(enable_metadata_routing=True)
def test_cross_validate_params_none(func, extra_args):
    """Test that no errors are raised when passing `params=None`, which is the
    default value.
    Non-regression test for: https://github.com/chenxingqiang/jax-sklearn/issues/30447
    """
    X, y = make_classification(n_samples=100, n_classes=2, random_state=0)
    func(estimator=ConsumingClassifier(), X=X, y=y, **extra_args)


@pytest.mark.parametrize(
    "func, extra_args",
    [
        (cross_validate, {}),
        (cross_val_score, {}),
        (cross_val_predict, {}),
        (learning_curve, {}),
        (permutation_test_score, {}),
        (validation_curve, {"param_name": "alpha", "param_range": np.array([1])}),
    ],
)
@config_context(enable_metadata_routing=True)
def test_passed_unrequested_metadata(func, extra_args):
    """Check that we raise an error when passing metadata that is not
    requested."""

    err_msg = re.escape(
        "[metadata] are passed but are not explicitly set as requested or not "
        "requested for ConsumingClassifier.fit, which is used within"
    )
    with pytest.raises(UnsetMetadataPassedError, match=err_msg):
        func(
            estimator=ConsumingClassifier(),
            X=X,
            y=y2,
            params=dict(metadata=[]),
            **extra_args,
        )

    # cross_val_predict doesn't use scoring
    if func == cross_val_predict:
        return

    err_msg = re.escape(
        "[metadata] are passed but are not explicitly set as requested or not "
        "requested for ConsumingClassifier.score, which is used within"
    )
    with pytest.raises(UnsetMetadataPassedError, match=err_msg):
        func(
            estimator=ConsumingClassifier()
            .set_fit_request(metadata=True)
            .set_partial_fit_request(metadata=True),
            X=X,
            y=y2,
            params=dict(metadata=[]),
            **extra_args,
        )


@pytest.mark.parametrize(
    "func, extra_args",
    [
        (cross_validate, {}),
        (cross_val_score, {}),
        (cross_val_predict, {}),
        (learning_curve, {}),
        (permutation_test_score, {}),
        (validation_curve, {"param_name": "alpha", "param_range": np.array([1])}),
    ],
)
@config_context(enable_metadata_routing=True)
def test_validation_functions_routing(func, extra_args):
    """Check that the respective cv method is properly dispatching the metadata
    to the consumer."""
    scorer_registry = _Registry()
    scorer = ConsumingScorer(registry=scorer_registry).set_score_request(
        sample_weight="score_weights", metadata="score_metadata"
    )
    splitter_registry = _Registry()
    splitter = ConsumingSplitter(registry=splitter_registry).set_split_request(
        groups="split_groups", metadata="split_metadata"
    )
    estimator_registry = _Registry()
    estimator = ConsumingClassifier(registry=estimator_registry).set_fit_request(
        sample_weight="fit_sample_weight", metadata="fit_metadata"
    )

    n_samples = _num_samples(X)
    rng = np.random.RandomState(0)
    score_weights = rng.rand(n_samples)
    score_metadata = rng.rand(n_samples)
    split_groups = rng.randint(0, 3, n_samples)
    split_metadata = rng.rand(n_samples)
    fit_sample_weight = rng.rand(n_samples)
    fit_metadata = rng.rand(n_samples)

    scoring_args = {
        cross_validate: dict(scoring=dict(my_scorer=scorer, accuracy="accuracy")),
        cross_val_score: dict(scoring=scorer),
        learning_curve: dict(scoring=scorer),
        validation_curve: dict(scoring=scorer),
        permutation_test_score: dict(scoring=scorer),
        cross_val_predict: dict(),
    }

    params = dict(
        split_groups=split_groups,
        split_metadata=split_metadata,
        fit_sample_weight=fit_sample_weight,
        fit_metadata=fit_metadata,
    )

    if func is not cross_val_predict:
        params.update(
            score_weights=score_weights,
            score_metadata=score_metadata,
        )

    func(
        estimator,
        X=X,
        y=y,
        cv=splitter,
        **scoring_args[func],
        **extra_args,
        params=params,
    )

    if func is not cross_val_predict:
        # cross_val_predict doesn't need a scorer
        assert len(scorer_registry)
    for _scorer in scorer_registry:
        check_recorded_metadata(
            obj=_scorer,
            method="score",
            parent=func.__name__,
            split_params=("sample_weight", "metadata"),
            sample_weight=score_weights,
            metadata=score_metadata,
        )

    assert len(splitter_registry)
    for _splitter in splitter_registry:
        check_recorded_metadata(
            obj=_splitter,
            method="split",
            parent=func.__name__,
            groups=split_groups,
            metadata=split_metadata,
        )

    assert len(estimator_registry)
    for _estimator in estimator_registry:
        check_recorded_metadata(
            obj=_estimator,
            method="fit",
            parent=func.__name__,
            split_params=("sample_weight", "metadata"),
            sample_weight=fit_sample_weight,
            metadata=fit_metadata,
        )


@config_context(enable_metadata_routing=True)
def test_learning_curve_exploit_incremental_learning_routing():
    """Test that learning_curve routes metadata to the estimator correctly while
    partial_fitting it with `exploit_incremental_learning=True`."""

    n_samples = _num_samples(X)
    rng = np.random.RandomState(0)
    fit_sample_weight = rng.rand(n_samples)
    fit_metadata = rng.rand(n_samples)

    estimator_registry = _Registry()
    estimator = ConsumingClassifier(
        registry=estimator_registry
    ).set_partial_fit_request(
        sample_weight="fit_sample_weight", metadata="fit_metadata"
    )

    learning_curve(
        estimator,
        X=X,
        y=y,
        cv=ConsumingSplitter(),
        exploit_incremental_learning=True,
        params=dict(fit_sample_weight=fit_sample_weight, fit_metadata=fit_metadata),
    )

    assert len(estimator_registry)
    for _estimator in estimator_registry:
        check_recorded_metadata(
            obj=_estimator,
            method="partial_fit",
            parent="learning_curve",
            split_params=("sample_weight", "metadata"),
            sample_weight=fit_sample_weight,
            metadata=fit_metadata,
        )


# End of metadata routing tests
# =============================
