# We can not use pytest here, because we run
# build_tools/azure/test_pytest_soft_dependency.sh on these
# tests to make sure estimator_checks works without pytest.

import importlib
import sys
import unittest
import warnings
from numbers import Integral, Real

import joblib
import numpy as np
import scipy.sparse as sp

from sklearn import config_context, get_config
from sklearn.base import BaseEstimator, ClassifierMixin, OutlierMixin
from sklearn.cluster import MiniBatchKMeans
from sklearn.datasets import make_multilabel_classification
from sklearn.decomposition import PCA
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.exceptions import SkipTestWarning
from sklearn.linear_model import (
    LinearRegression,
    LogisticRegression,
    MultiTaskElasticNet,
    SGDClassifier,
)
from sklearn.mixture import GaussianMixture
from sklearn.neighbors import KNeighborsRegressor
from sklearn.svm import SVC, NuSVC
from sklearn.utils import _array_api, all_estimators, deprecated
from sklearn.utils._param_validation import Interval, StrOptions
from sklearn.utils._testing import (
    MinimalClassifier,
    MinimalRegressor,
    MinimalTransformer,
    SkipTest,
    ignore_warnings,
    raises,
)
from sklearn.utils.estimator_checks import (
    _NotAnArray,
    _set_checking_parameters,
    _yield_all_checks,
    check_array_api_input,
    check_class_weight_balanced_linear_classifier,
    check_classifier_data_not_an_array,
    check_classifiers_multilabel_output_format_decision_function,
    check_classifiers_multilabel_output_format_predict,
    check_classifiers_multilabel_output_format_predict_proba,
    check_dataframe_column_names_consistency,
    check_decision_proba_consistency,
    check_estimator,
    check_estimator_get_tags_default_keys,
    check_estimators_unfitted,
    check_fit_check_is_fitted,
    check_fit_score_takes_y,
    check_methods_sample_order_invariance,
    check_methods_subset_invariance,
    check_no_attributes_set_in_init,
    check_outlier_contamination,
    check_outlier_corruption,
    check_regressor_data_not_an_array,
    check_requires_y_none,
    set_random_state,
)
from sklearn.utils.metaestimators import available_if
from sklearn.utils.validation import check_array, check_is_fitted, check_X_y


class CorrectNotFittedError(ValueError):
    """Exception class to raise if estimator is used before fitting.

    Like NotFittedError, it inherits from ValueError, but not from
    AttributeError. Used for testing only.
    """


class BaseBadClassifier(ClassifierMixin, BaseEstimator):
    def fit(self, X, y):
        return self

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


class ChangesDict(BaseEstimator):
    def __init__(self, key=0):
        self.key = key

    def fit(self, X, y=None):
        X, y = self._validate_data(X, y)
        return self

    def predict(self, X):
        X = check_array(X)
        self.key = 1000
        return np.ones(X.shape[0])


class SetsWrongAttribute(BaseEstimator):
    def __init__(self, acceptable_key=0):
        self.acceptable_key = acceptable_key

    def fit(self, X, y=None):
        self.wrong_attribute = 0
        X, y = self._validate_data(X, y)
        return self


class ChangesWrongAttribute(BaseEstimator):
    def __init__(self, wrong_attribute=0):
        self.wrong_attribute = wrong_attribute

    def fit(self, X, y=None):
        self.wrong_attribute = 1
        X, y = self._validate_data(X, y)
        return self


class ChangesUnderscoreAttribute(BaseEstimator):
    def fit(self, X, y=None):
        self._good_attribute = 1
        X, y = self._validate_data(X, y)
        return self


class RaisesErrorInSetParams(BaseEstimator):
    def __init__(self, p=0):
        self.p = p

    def set_params(self, **kwargs):
        if "p" in kwargs:
            p = kwargs.pop("p")
            if p < 0:
                raise ValueError("p can't be less than 0")
            self.p = p
        return super().set_params(**kwargs)

    def fit(self, X, y=None):
        X, y = self._validate_data(X, y)
        return self


class HasMutableParameters(BaseEstimator):
    def __init__(self, p=object()):
        self.p = p

    def fit(self, X, y=None):
        X, y = self._validate_data(X, y)
        return self


class HasImmutableParameters(BaseEstimator):
    # Note that object is an uninitialized class, thus immutable.
    def __init__(self, p=42, q=np.int32(42), r=object):
        self.p = p
        self.q = q
        self.r = r

    def fit(self, X, y=None):
        X, y = self._validate_data(X, y)
        return self


class ModifiesValueInsteadOfRaisingError(BaseEstimator):
    def __init__(self, p=0):
        self.p = p

    def set_params(self, **kwargs):
        if "p" in kwargs:
            p = kwargs.pop("p")
            if p < 0:
                p = 0
            self.p = p
        return super().set_params(**kwargs)

    def fit(self, X, y=None):
        X, y = self._validate_data(X, y)
        return self


class ModifiesAnotherValue(BaseEstimator):
    def __init__(self, a=0, b="method1"):
        self.a = a
        self.b = b

    def set_params(self, **kwargs):
        if "a" in kwargs:
            a = kwargs.pop("a")
            self.a = a
            if a is None:
                kwargs.pop("b")
                self.b = "method2"
        return super().set_params(**kwargs)

    def fit(self, X, y=None):
        X, y = self._validate_data(X, y)
        return self


class NoCheckinPredict(BaseBadClassifier):
    def fit(self, X, y):
        X, y = self._validate_data(X, y)
        return self


class NoSparseClassifier(BaseBadClassifier):
    def fit(self, X, y):
        X, y = self._validate_data(X, y, accept_sparse=["csr", "csc"])
        if sp.issparse(X):
            raise ValueError("Nonsensical Error")
        return self

    def predict(self, X):
        X = check_array(X)
        return np.ones(X.shape[0])


class CorrectNotFittedErrorClassifier(BaseBadClassifier):
    def fit(self, X, y):
        X, y = self._validate_data(X, y)
        self.coef_ = np.ones(X.shape[1])
        return self

    def predict(self, X):
        check_is_fitted(self)
        X = check_array(X)
        return np.ones(X.shape[0])


class NoSampleWeightPandasSeriesType(BaseEstimator):
    def fit(self, X, y, sample_weight=None):
        # Convert data
        X, y = self._validate_data(
            X, y, accept_sparse=("csr", "csc"), multi_output=True, y_numeric=True
        )
        # Function is only called after we verify that pandas is installed
        from pandas import Series

        if isinstance(sample_weight, Series):
            raise ValueError(
                "Estimator does not accept 'sample_weight'of type pandas.Series"
            )
        return self

    def predict(self, X):
        X = check_array(X)
        return np.ones(X.shape[0])


class BadBalancedWeightsClassifier(BaseBadClassifier):
    def __init__(self, class_weight=None):
        self.class_weight = class_weight

    def fit(self, X, y):
        from sklearn.preprocessing import LabelEncoder
        from sklearn.utils import compute_class_weight

        label_encoder = LabelEncoder().fit(y)
        classes = label_encoder.classes_
        class_weight = compute_class_weight(self.class_weight, classes=classes, y=y)

        # Intentionally modify the balanced class_weight
        # to simulate a bug and raise an exception
        if self.class_weight == "balanced":
            class_weight += 1.0

        # Simply assigning coef_ to the class_weight
        self.coef_ = class_weight
        return self


class BadTransformerWithoutMixin(BaseEstimator):
    def fit(self, X, y=None):
        X = self._validate_data(X)
        return self

    def transform(self, X):
        X = check_array(X)
        return X


class NotInvariantPredict(BaseEstimator):
    def fit(self, X, y):
        # Convert data
        X, y = self._validate_data(
            X, y, accept_sparse=("csr", "csc"), multi_output=True, y_numeric=True
        )
        return self

    def predict(self, X):
        # return 1 if X has more than one element else return 0
        X = check_array(X)
        if X.shape[0] > 1:
            return np.ones(X.shape[0])
        return np.zeros(X.shape[0])


class NotInvariantSampleOrder(BaseEstimator):
    def fit(self, X, y):
        X, y = self._validate_data(
            X, y, accept_sparse=("csr", "csc"), multi_output=True, y_numeric=True
        )
        # store the original X to check for sample order later
        self._X = X
        return self

    def predict(self, X):
        X = check_array(X)
        # if the input contains the same elements but different sample order,
        # then just return zeros.
        if (
            np.array_equiv(np.sort(X, axis=0), np.sort(self._X, axis=0))
            and (X != self._X).any()
        ):
            return np.zeros(X.shape[0])
        return X[:, 0]


class OneClassSampleErrorClassifier(BaseBadClassifier):
    """Classifier allowing to trigger different behaviors when `sample_weight` reduces
    the number of classes to 1."""

    def __init__(self, raise_when_single_class=False):
        self.raise_when_single_class = raise_when_single_class

    def fit(self, X, y, sample_weight=None):
        X, y = check_X_y(
            X, y, accept_sparse=("csr", "csc"), multi_output=True, y_numeric=True
        )

        self.has_single_class_ = False
        self.classes_, y = np.unique(y, return_inverse=True)
        n_classes_ = self.classes_.shape[0]
        if n_classes_ < 2 and self.raise_when_single_class:
            self.has_single_class_ = True
            raise ValueError("normal class error")

        # find the number of class after trimming
        if sample_weight is not None:
            if isinstance(sample_weight, np.ndarray) and len(sample_weight) > 0:
                n_classes_ = np.count_nonzero(np.bincount(y, sample_weight))
            if n_classes_ < 2:
                self.has_single_class_ = True
                raise ValueError("Nonsensical Error")

        return self

    def predict(self, X):
        check_is_fitted(self)
        X = check_array(X)
        if self.has_single_class_:
            return np.zeros(X.shape[0])
        return np.ones(X.shape[0])


class LargeSparseNotSupportedClassifier(BaseEstimator):
    def fit(self, X, y):
        X, y = self._validate_data(
            X,
            y,
            accept_sparse=("csr", "csc", "coo"),
            accept_large_sparse=True,
            multi_output=True,
            y_numeric=True,
        )
        if sp.issparse(X):
            if X.getformat() == "coo":
                if X.row.dtype == "int64" or X.col.dtype == "int64":
                    raise ValueError("Estimator doesn't support 64-bit indices")
            elif X.getformat() in ["csc", "csr"]:
                assert "int64" not in (
                    X.indices.dtype,
                    X.indptr.dtype,
                ), "Estimator doesn't support 64-bit indices"

        return self


class SparseTransformer(BaseEstimator):
    def fit(self, X, y=None):
        self.X_shape_ = self._validate_data(X).shape
        return self

    def fit_transform(self, X, y=None):
        return self.fit(X, y).transform(X)

    def transform(self, X):
        X = check_array(X)
        if X.shape[1] != self.X_shape_[1]:
            raise ValueError("Bad number of features")
        return sp.csr_matrix(X)


class EstimatorInconsistentForPandas(BaseEstimator):
    def fit(self, X, y):
        try:
            from pandas import DataFrame

            if isinstance(X, DataFrame):
                self.value_ = X.iloc[0, 0]
            else:
                X = check_array(X)
                self.value_ = X[1, 0]
            return self

        except ImportError:
            X = check_array(X)
            self.value_ = X[1, 0]
            return self

    def predict(self, X):
        X = check_array(X)
        return np.array([self.value_] * X.shape[0])


class UntaggedBinaryClassifier(SGDClassifier):
    # Toy classifier that only supports binary classification, will fail tests.
    def fit(self, X, y, coef_init=None, intercept_init=None, sample_weight=None):
        super().fit(X, y, coef_init, intercept_init, sample_weight)
        if len(self.classes_) > 2:
            raise ValueError("Only 2 classes are supported")
        return self

    def partial_fit(self, X, y, classes=None, sample_weight=None):
        super().partial_fit(X=X, y=y, classes=classes, sample_weight=sample_weight)
        if len(self.classes_) > 2:
            raise ValueError("Only 2 classes are supported")
        return self


class TaggedBinaryClassifier(UntaggedBinaryClassifier):
    # Toy classifier that only supports binary classification.
    def _more_tags(self):
        return {"binary_only": True}


class EstimatorMissingDefaultTags(BaseEstimator):
    def _get_tags(self):
        tags = super()._get_tags().copy()
        del tags["allow_nan"]
        return tags


class RequiresPositiveXRegressor(LinearRegression):
    def fit(self, X, y):
        X, y = self._validate_data(X, y, multi_output=True)
        if (X < 0).any():
            raise ValueError("negative X values not supported!")
        return super().fit(X, y)

    def _more_tags(self):
        return {"requires_positive_X": True}


class RequiresPositiveYRegressor(LinearRegression):
    def fit(self, X, y):
        X, y = self._validate_data(X, y, multi_output=True)
        if (y <= 0).any():
            raise ValueError("negative y values not supported!")
        return super().fit(X, y)

    def _more_tags(self):
        return {"requires_positive_y": True}


class PoorScoreLogisticRegression(LogisticRegression):
    def decision_function(self, X):
        return super().decision_function(X) + 1

    def _more_tags(self):
        return {"poor_score": True}


class PartialFitChecksName(BaseEstimator):
    def fit(self, X, y):
        self._validate_data(X, y)
        return self

    def partial_fit(self, X, y):
        reset = not hasattr(self, "_fitted")
        self._validate_data(X, y, reset=reset)
        self._fitted = True
        return self


class BrokenArrayAPI(BaseEstimator):
    """Make different predictions when using Numpy and the Array API"""

    def fit(self, X, y):
        return self

    def predict(self, X):
        enabled = get_config()["array_api_dispatch"]
        xp, _ = _array_api.get_namespace(X)
        if enabled:
            return xp.asarray([1, 2, 3])
        else:
            return np.array([3, 2, 1])


def test_check_array_api_input():
    try:
        importlib.import_module("array_api_compat")
    except ModuleNotFoundError:
        raise SkipTest("array_api_compat is required to run this test")
    try:
        importlib.import_module("numpy.array_api")
    except ModuleNotFoundError:  # pragma: nocover
        raise SkipTest("numpy.array_api is required to run this test")

    with raises(AssertionError, match="Not equal to tolerance"):
        check_array_api_input(
            "BrokenArrayAPI", BrokenArrayAPI(), array_namespace="numpy.array_api"
        )


def test_not_an_array_array_function():
    not_array = _NotAnArray(np.ones(10))
    msg = "Don't want to call array_function sum!"
    with raises(TypeError, match=msg):
        np.sum(not_array)
    # always returns True
    assert np.may_share_memory(not_array, None)


def test_check_fit_score_takes_y_works_on_deprecated_fit():
    # Tests that check_fit_score_takes_y works on a class with
    # a deprecated fit method

    class TestEstimatorWithDeprecatedFitMethod(BaseEstimator):
        @deprecated("Deprecated for the purpose of testing check_fit_score_takes_y")
        def fit(self, X, y):
            return self

    check_fit_score_takes_y("test", TestEstimatorWithDeprecatedFitMethod())


def test_check_estimator():
    # tests that the estimator actually fails on "bad" estimators.
    # not a complete test of all checks, which are very extensive.

    # check that we have a set_params and can clone
    msg = "Passing a class was deprecated"
    with raises(TypeError, match=msg):
        check_estimator(object)
    msg = (
        "Parameter 'p' of estimator 'HasMutableParameters' is of type "
        "object which is not allowed"
    )
    # check that the "default_constructible" test checks for mutable parameters
    check_estimator(HasImmutableParameters())  # should pass
    with raises(AssertionError, match=msg):
        check_estimator(HasMutableParameters())
    # check that values returned by get_params match set_params
    msg = "get_params result does not match what was passed to set_params"
    with raises(AssertionError, match=msg):
        check_estimator(ModifiesValueInsteadOfRaisingError())
    with warnings.catch_warnings(record=True) as records:
        check_estimator(RaisesErrorInSetParams())
    assert UserWarning in [rec.category for rec in records]

    with raises(AssertionError, match=msg):
        check_estimator(ModifiesAnotherValue())
    # check that we have a fit method
    msg = "object has no attribute 'fit'"
    with raises(AttributeError, match=msg):
        check_estimator(BaseEstimator())
    # check that fit does input validation
    msg = "Did not raise"
    with raises(AssertionError, match=msg):
        check_estimator(BaseBadClassifier())
    # check that sample_weights in fit accepts pandas.Series type
    try:
        from pandas import Series  # noqa

        msg = (
            "Estimator NoSampleWeightPandasSeriesType raises error if "
            "'sample_weight' parameter is of type pandas.Series"
        )
        with raises(ValueError, match=msg):
            check_estimator(NoSampleWeightPandasSeriesType())
    except ImportError:
        pass
    # check that predict does input validation (doesn't accept dicts in input)
    msg = "Estimator NoCheckinPredict doesn't check for NaN and inf in predict"
    with raises(AssertionError, match=msg):
        check_estimator(NoCheckinPredict())
    # check that estimator state does not change
    # at transform/predict/predict_proba time
    msg = "Estimator changes __dict__ during predict"
    with raises(AssertionError, match=msg):
        check_estimator(ChangesDict())
    # check that `fit` only changes attributes that
    # are private (start with an _ or end with a _).
    msg = (
        "Estimator ChangesWrongAttribute should not change or mutate  "
        "the parameter wrong_attribute from 0 to 1 during fit."
    )
    with raises(AssertionError, match=msg):
        check_estimator(ChangesWrongAttribute())
    check_estimator(ChangesUnderscoreAttribute())
    # check that `fit` doesn't add any public attribute
    msg = (
        r"Estimator adds public attribute\(s\) during the fit method."
        " Estimators are only allowed to add private attributes"
        " either started with _ or ended"
        " with _ but wrong_attribute added"
    )
    with raises(AssertionError, match=msg):
        check_estimator(SetsWrongAttribute())
    # check for sample order invariance
    name = NotInvariantSampleOrder.__name__
    method = "predict"
    msg = (
        "{method} of {name} is not invariant when applied to a dataset"
        "with different sample order."
    ).format(method=method, name=name)
    with raises(AssertionError, match=msg):
        check_estimator(NotInvariantSampleOrder())
    # check for invariant method
    name = NotInvariantPredict.__name__
    method = "predict"
    msg = ("{method} of {name} is not invariant when applied to a subset.").format(
        method=method, name=name
    )
    with raises(AssertionError, match=msg):
        check_estimator(NotInvariantPredict())
    # check for sparse matrix input handling
    name = NoSparseClassifier.__name__
    msg = "Estimator %s doesn't seem to fail gracefully on sparse data" % name
    with raises(AssertionError, match=msg):
        check_estimator(NoSparseClassifier())

    # check for classifiers reducing to less than two classes via sample weights
    name = OneClassSampleErrorClassifier.__name__
    msg = (
        f"{name} failed when fitted on one label after sample_weight "
        "trimming. Error message is not explicit, it should have "
        "'class'."
    )
    with raises(AssertionError, match=msg):
        check_estimator(OneClassSampleErrorClassifier())

    # Large indices test on bad estimator
    msg = (
        "Estimator LargeSparseNotSupportedClassifier doesn't seem to "
        r"support \S{3}_64 matrix, and is not failing gracefully.*"
    )
    with raises(AssertionError, match=msg):
        check_estimator(LargeSparseNotSupportedClassifier())

    # does error on binary_only untagged estimator
    msg = "Only 2 classes are supported"
    with raises(ValueError, match=msg):
        check_estimator(UntaggedBinaryClassifier())

    # non-regression test for estimators transforming to sparse data
    check_estimator(SparseTransformer())

    # doesn't error on actual estimator
    check_estimator(LogisticRegression())
    check_estimator(LogisticRegression(C=0.01))
    check_estimator(MultiTaskElasticNet())

    # doesn't error on binary_only tagged estimator
    check_estimator(TaggedBinaryClassifier())
    check_estimator(RequiresPositiveXRegressor())

    # Check regressor with requires_positive_y estimator tag
    msg = "negative y values not supported!"
    with raises(ValueError, match=msg):
        check_estimator(RequiresPositiveYRegressor())

    # Does not raise error on classifier with poor_score tag
    check_estimator(PoorScoreLogisticRegression())


def test_check_outlier_corruption():
    # should raise AssertionError
    decision = np.array([0.0, 1.0, 1.5, 2.0])
    with raises(AssertionError):
        check_outlier_corruption(1, 2, decision)
    # should pass
    decision = np.array([0.0, 1.0, 1.0, 2.0])
    check_outlier_corruption(1, 2, decision)


def test_check_estimator_transformer_no_mixin():
    # check that TransformerMixin is not required for transformer tests to run
    with raises(AttributeError, ".*fit_transform.*"):
        check_estimator(BadTransformerWithoutMixin())


def test_check_estimator_clones():
    # check that check_estimator doesn't modify the estimator it receives
    from sklearn.datasets import load_iris

    iris = load_iris()

    for Estimator in [
        GaussianMixture,
        LinearRegression,
        SGDClassifier,
        PCA,
        ExtraTreesClassifier,
        MiniBatchKMeans,
    ]:
        with ignore_warnings(category=FutureWarning):
            # when 'est = SGDClassifier()'
            est = Estimator()
            _set_checking_parameters(est)
            set_random_state(est)
            # without fitting
            old_hash = joblib.hash(est)
            check_estimator(est)
        assert old_hash == joblib.hash(est)

        with ignore_warnings(category=FutureWarning):
            # when 'est = SGDClassifier()'
            est = Estimator()
            _set_checking_parameters(est)
            set_random_state(est)
            # with fitting
            est.fit(iris.data + 10, iris.target)
            old_hash = joblib.hash(est)
            check_estimator(est)
        assert old_hash == joblib.hash(est)


def test_check_estimators_unfitted():
    # check that a ValueError/AttributeError is raised when calling predict
    # on an unfitted estimator
    msg = "Did not raise"
    with raises(AssertionError, match=msg):
        check_estimators_unfitted("estimator", NoSparseClassifier())

    # check that CorrectNotFittedError inherit from either ValueError
    # or AttributeError
    check_estimators_unfitted("estimator", CorrectNotFittedErrorClassifier())


def test_check_no_attributes_set_in_init():
    class NonConformantEstimatorPrivateSet(BaseEstimator):
        def __init__(self):
            self.you_should_not_set_this_ = None

    class NonConformantEstimatorNoParamSet(BaseEstimator):
        def __init__(self, you_should_set_this_=None):
            pass

    class ConformantEstimatorClassAttribute(BaseEstimator):
        # making sure our __metadata_request__* class attributes are okay!
        __metadata_request__fit = {"foo": True}

    msg = (
        "Estimator estimator_name should not set any"
        " attribute apart from parameters during init."
        r" Found attributes \['you_should_not_set_this_'\]."
    )
    with raises(AssertionError, match=msg):
        check_no_attributes_set_in_init(
            "estimator_name", NonConformantEstimatorPrivateSet()
        )

    msg = (
        "Estimator estimator_name should store all parameters as an attribute"
        " during init"
    )
    with raises(AttributeError, match=msg):
        check_no_attributes_set_in_init(
            "estimator_name", NonConformantEstimatorNoParamSet()
        )

    # a private class attribute is okay!
    check_no_attributes_set_in_init(
        "estimator_name", ConformantEstimatorClassAttribute()
    )
    # also check if cloning an estimator which has non-default set requests is
    # fine. Setting a non-default value via `set_{method}_request` sets the
    # private _metadata_request instance attribute which is copied in `clone`.
    with config_context(enable_metadata_routing=True):
        check_no_attributes_set_in_init(
            "estimator_name",
            ConformantEstimatorClassAttribute().set_fit_request(foo=True),
        )


def test_check_estimator_pairwise():
    # check that check_estimator() works on estimator with _pairwise
    # kernel or metric

    # test precomputed kernel
    est = SVC(kernel="precomputed")
    check_estimator(est)

    # test precomputed metric
    est = KNeighborsRegressor(metric="precomputed")
    check_estimator(est)


def test_check_classifier_data_not_an_array():
    with raises(AssertionError, match="Not equal to tolerance"):
        check_classifier_data_not_an_array(
            "estimator_name", EstimatorInconsistentForPandas()
        )


def test_check_regressor_data_not_an_array():
    with raises(AssertionError, match="Not equal to tolerance"):
        check_regressor_data_not_an_array(
            "estimator_name", EstimatorInconsistentForPandas()
        )


def test_check_estimator_get_tags_default_keys():
    estimator = EstimatorMissingDefaultTags()
    err_msg = (
        r"EstimatorMissingDefaultTags._get_tags\(\) is missing entries"
        r" for the following default tags: {'allow_nan'}"
    )
    with raises(AssertionError, match=err_msg):
        check_estimator_get_tags_default_keys(estimator.__class__.__name__, estimator)

    # noop check when _get_tags is not available
    estimator = MinimalTransformer()
    check_estimator_get_tags_default_keys(estimator.__class__.__name__, estimator)


def test_check_dataframe_column_names_consistency():
    err_msg = "Estimator does not have a feature_names_in_"
    with raises(ValueError, match=err_msg):
        check_dataframe_column_names_consistency("estimator_name", BaseBadClassifier())
    check_dataframe_column_names_consistency("estimator_name", PartialFitChecksName())

    lr = LogisticRegression()
    check_dataframe_column_names_consistency(lr.__class__.__name__, lr)
    lr.__doc__ = "Docstring that does not document the estimator's attributes"
    err_msg = (
        "Estimator LogisticRegression does not document its feature_names_in_ attribute"
    )
    with raises(ValueError, match=err_msg):
        check_dataframe_column_names_consistency(lr.__class__.__name__, lr)


class _BaseMultiLabelClassifierMock(ClassifierMixin, BaseEstimator):
    def __init__(self, response_output):
        self.response_output = response_output

    def fit(self, X, y):
        return self

    def _more_tags(self):
        return {"multilabel": True}


def test_check_classifiers_multilabel_output_format_predict():
    n_samples, test_size, n_outputs = 100, 25, 5
    _, y = make_multilabel_classification(
        n_samples=n_samples,
        n_features=2,
        n_classes=n_outputs,
        n_labels=3,
        length=50,
        allow_unlabeled=True,
        random_state=0,
    )
    y_test = y[-test_size:]

    class MultiLabelClassifierPredict(_BaseMultiLabelClassifierMock):
        def predict(self, X):
            return self.response_output

    # 1. inconsistent array type
    clf = MultiLabelClassifierPredict(response_output=y_test.tolist())
    err_msg = (
        r"MultiLabelClassifierPredict.predict is expected to output a "
        r"NumPy array. Got <class 'list'> instead."
    )
    with raises(AssertionError, match=err_msg):
        check_classifiers_multilabel_output_format_predict(clf.__class__.__name__, clf)
    # 2. inconsistent shape
    clf = MultiLabelClassifierPredict(response_output=y_test[:, :-1])
    err_msg = (
        r"MultiLabelClassifierPredict.predict outputs a NumPy array of "
        r"shape \(25, 4\) instead of \(25, 5\)."
    )
    with raises(AssertionError, match=err_msg):
        check_classifiers_multilabel_output_format_predict(clf.__class__.__name__, clf)
    # 3. inconsistent dtype
    clf = MultiLabelClassifierPredict(response_output=y_test.astype(np.float64))
    err_msg = (
        r"MultiLabelClassifierPredict.predict does not output the same "
        r"dtype than the targets."
    )
    with raises(AssertionError, match=err_msg):
        check_classifiers_multilabel_output_format_predict(clf.__class__.__name__, clf)


def test_check_classifiers_multilabel_output_format_predict_proba():
    n_samples, test_size, n_outputs = 100, 25, 5
    _, y = make_multilabel_classification(
        n_samples=n_samples,
        n_features=2,
        n_classes=n_outputs,
        n_labels=3,
        length=50,
        allow_unlabeled=True,
        random_state=0,
    )
    y_test = y[-test_size:]

    class MultiLabelClassifierPredictProba(_BaseMultiLabelClassifierMock):
        def predict_proba(self, X):
            return self.response_output

    # 1. unknown output type
    clf = MultiLabelClassifierPredictProba(response_output=sp.csr_matrix(y_test))
    err_msg = (
        "Unknown returned type .*csr_matrix.* by "
        r"MultiLabelClassifierPredictProba.predict_proba. A list or a Numpy "
        r"array is expected."
    )
    with raises(ValueError, match=err_msg):
        check_classifiers_multilabel_output_format_predict_proba(
            clf.__class__.__name__,
            clf,
        )
    # 2. for list output
    # 2.1. inconsistent length
    clf = MultiLabelClassifierPredictProba(response_output=y_test.tolist())
    err_msg = (
        "When MultiLabelClassifierPredictProba.predict_proba returns a list, "
        "the list should be of length n_outputs and contain NumPy arrays. Got "
        f"length of {test_size} instead of {n_outputs}."
    )
    with raises(AssertionError, match=err_msg):
        check_classifiers_multilabel_output_format_predict_proba(
            clf.__class__.__name__,
            clf,
        )
    # 2.2. array of inconsistent shape
    response_output = [np.ones_like(y_test) for _ in range(n_outputs)]
    clf = MultiLabelClassifierPredictProba(response_output=response_output)
    err_msg = (
        r"When MultiLabelClassifierPredictProba.predict_proba returns a list, "
        r"this list should contain NumPy arrays of shape \(n_samples, 2\). Got "
        r"NumPy arrays of shape \(25, 5\) instead of \(25, 2\)."
    )
    with raises(AssertionError, match=err_msg):
        check_classifiers_multilabel_output_format_predict_proba(
            clf.__class__.__name__,
            clf,
        )
    # 2.3. array of inconsistent dtype
    response_output = [
        np.ones(shape=(y_test.shape[0], 2), dtype=np.int64) for _ in range(n_outputs)
    ]
    clf = MultiLabelClassifierPredictProba(response_output=response_output)
    err_msg = (
        "When MultiLabelClassifierPredictProba.predict_proba returns a list, "
        "it should contain NumPy arrays with floating dtype."
    )
    with raises(AssertionError, match=err_msg):
        check_classifiers_multilabel_output_format_predict_proba(
            clf.__class__.__name__,
            clf,
        )
    # 2.4. array does not contain probability (each row should sum to 1)
    response_output = [
        np.ones(shape=(y_test.shape[0], 2), dtype=np.float64) for _ in range(n_outputs)
    ]
    clf = MultiLabelClassifierPredictProba(response_output=response_output)
    err_msg = (
        r"When MultiLabelClassifierPredictProba.predict_proba returns a list, "
        r"each NumPy array should contain probabilities for each class and "
        r"thus each row should sum to 1"
    )
    with raises(AssertionError, match=err_msg):
        check_classifiers_multilabel_output_format_predict_proba(
            clf.__class__.__name__,
            clf,
        )
    # 3 for array output
    # 3.1. array of inconsistent shape
    clf = MultiLabelClassifierPredictProba(response_output=y_test[:, :-1])
    err_msg = (
        r"When MultiLabelClassifierPredictProba.predict_proba returns a NumPy "
        r"array, the expected shape is \(n_samples, n_outputs\). Got \(25, 4\)"
        r" instead of \(25, 5\)."
    )
    with raises(AssertionError, match=err_msg):
        check_classifiers_multilabel_output_format_predict_proba(
            clf.__class__.__name__,
            clf,
        )
    # 3.2. array of inconsistent dtype
    response_output = np.zeros_like(y_test, dtype=np.int64)
    clf = MultiLabelClassifierPredictProba(response_output=response_output)
    err_msg = (
        r"When MultiLabelClassifierPredictProba.predict_proba returns a NumPy "
        r"array, the expected data type is floating."
    )
    with raises(AssertionError, match=err_msg):
        check_classifiers_multilabel_output_format_predict_proba(
            clf.__class__.__name__,
            clf,
        )
    # 4. array does not contain probabilities
    clf = MultiLabelClassifierPredictProba(response_output=y_test * 2.0)
    err_msg = (
        r"When MultiLabelClassifierPredictProba.predict_proba returns a NumPy "
        r"array, this array is expected to provide probabilities of the "
        r"positive class and should therefore contain values between 0 and 1."
    )
    with raises(AssertionError, match=err_msg):
        check_classifiers_multilabel_output_format_predict_proba(
            clf.__class__.__name__,
            clf,
        )


def test_check_classifiers_multilabel_output_format_decision_function():
    n_samples, test_size, n_outputs = 100, 25, 5
    _, y = make_multilabel_classification(
        n_samples=n_samples,
        n_features=2,
        n_classes=n_outputs,
        n_labels=3,
        length=50,
        allow_unlabeled=True,
        random_state=0,
    )
    y_test = y[-test_size:]

    class MultiLabelClassifierDecisionFunction(_BaseMultiLabelClassifierMock):
        def decision_function(self, X):
            return self.response_output

    # 1. inconsistent array type
    clf = MultiLabelClassifierDecisionFunction(response_output=y_test.tolist())
    err_msg = (
        r"MultiLabelClassifierDecisionFunction.decision_function is expected "
        r"to output a NumPy array. Got <class 'list'> instead."
    )
    with raises(AssertionError, match=err_msg):
        check_classifiers_multilabel_output_format_decision_function(
            clf.__class__.__name__,
            clf,
        )
    # 2. inconsistent shape
    clf = MultiLabelClassifierDecisionFunction(response_output=y_test[:, :-1])
    err_msg = (
        r"MultiLabelClassifierDecisionFunction.decision_function is expected "
        r"to provide a NumPy array of shape \(n_samples, n_outputs\). Got "
        r"\(25, 4\) instead of \(25, 5\)"
    )
    with raises(AssertionError, match=err_msg):
        check_classifiers_multilabel_output_format_decision_function(
            clf.__class__.__name__,
            clf,
        )
    # 3. inconsistent dtype
    clf = MultiLabelClassifierDecisionFunction(response_output=y_test)
    err_msg = (
        r"MultiLabelClassifierDecisionFunction.decision_function is expected "
        r"to output a floating dtype."
    )
    with raises(AssertionError, match=err_msg):
        check_classifiers_multilabel_output_format_decision_function(
            clf.__class__.__name__,
            clf,
        )


def run_tests_without_pytest():
    """Runs the tests in this file without using pytest."""
    main_module = sys.modules["__main__"]
    test_functions = [
        getattr(main_module, name)
        for name in dir(main_module)
        if name.startswith("test_")
    ]
    test_cases = [unittest.FunctionTestCase(fn) for fn in test_functions]
    suite = unittest.TestSuite()
    suite.addTests(test_cases)
    runner = unittest.TextTestRunner()
    runner.run(suite)


def test_check_class_weight_balanced_linear_classifier():
    # check that ill-computed balanced weights raises an exception
    msg = "Classifier estimator_name is not computing class_weight=balanced properly"
    with raises(AssertionError, match=msg):
        check_class_weight_balanced_linear_classifier(
            "estimator_name", BadBalancedWeightsClassifier
        )


def test_all_estimators_all_public():
    # all_estimator should not fail when pytest is not installed and return
    # only public estimators
    with warnings.catch_warnings(record=True) as record:
        estimators = all_estimators()
    # no warnings are raised
    assert not record
    for est in estimators:
        assert not est.__class__.__name__.startswith("_")


if __name__ == "__main__":
    # This module is run as a script to check that we have no dependency on
    # pytest for estimator checks.
    run_tests_without_pytest()


def test_xfail_ignored_in_check_estimator():
    # Make sure checks marked as xfail are just ignored and not run by
    # check_estimator(), but still raise a warning.
    with warnings.catch_warnings(record=True) as records:
        check_estimator(NuSVC())
    assert SkipTestWarning in [rec.category for rec in records]


# FIXME: this test should be uncommented when the checks will be granular
# enough. In 0.24, these tests fail due to low estimator performance.
def test_minimal_class_implementation_checks():
    # Check that third-party library can run tests without inheriting from
    # BaseEstimator.
    # FIXME
    raise SkipTest
    minimal_estimators = [MinimalTransformer(), MinimalRegressor(), MinimalClassifier()]
    for estimator in minimal_estimators:
        check_estimator(estimator)


def test_check_fit_check_is_fitted():
    class Estimator(BaseEstimator):
        def __init__(self, behavior="attribute"):
            self.behavior = behavior

        def fit(self, X, y, **kwargs):
            if self.behavior == "attribute":
                self.is_fitted_ = True
            elif self.behavior == "method":
                self._is_fitted = True
            return self

        @available_if(lambda self: self.behavior in {"method", "always-true"})
        def __sklearn_is_fitted__(self):
            if self.behavior == "always-true":
                return True
            return hasattr(self, "_is_fitted")

    with raises(Exception, match="passes check_is_fitted before being fit"):
        check_fit_check_is_fitted("estimator", Estimator(behavior="always-true"))

    check_fit_check_is_fitted("estimator", Estimator(behavior="method"))
    check_fit_check_is_fitted("estimator", Estimator(behavior="attribute"))


def test_check_requires_y_none():
    class Estimator(BaseEstimator):
        def fit(self, X, y):
            X, y = check_X_y(X, y)

    with warnings.catch_warnings(record=True) as record:
        check_requires_y_none("estimator", Estimator())

    # no warnings are raised
    assert not [r.message for r in record]


def test_non_deterministic_estimator_skip_tests():
    # check estimators with non_deterministic tag set to True
    # will skip certain tests, refer to issue #22313 for details
    for est in [MinimalTransformer, MinimalRegressor, MinimalClassifier]:
        all_tests = list(_yield_all_checks(est()))
        assert check_methods_sample_order_invariance in all_tests
        assert check_methods_subset_invariance in all_tests

        class Estimator(est):
            def _more_tags(self):
                return {"non_deterministic": True}

        all_tests = list(_yield_all_checks(Estimator()))
        assert check_methods_sample_order_invariance not in all_tests
        assert check_methods_subset_invariance not in all_tests


def test_check_outlier_contamination():
    """Check the test for the contamination parameter in the outlier detectors."""

    # Without any parameter constraints, the estimator will early exit the test by
    # returning None.
    class OutlierDetectorWithoutConstraint(OutlierMixin, BaseEstimator):
        """Outlier detector without parameter validation."""

        def __init__(self, contamination=0.1):
            self.contamination = contamination

        def fit(self, X, y=None, sample_weight=None):
            return self  # pragma: no cover

        def predict(self, X, y=None):
            return np.ones(X.shape[0])

    detector = OutlierDetectorWithoutConstraint()
    assert check_outlier_contamination(detector.__class__.__name__, detector) is None

    # Now, we check that with the parameter constraints, the test should only be valid
    # if an Interval constraint with bound in [0, 1] is provided.
    class OutlierDetectorWithConstraint(OutlierDetectorWithoutConstraint):
        _parameter_constraints = {"contamination": [StrOptions({"auto"})]}

    detector = OutlierDetectorWithConstraint()
    err_msg = "contamination constraints should contain a Real Interval constraint."
    with raises(AssertionError, match=err_msg):
        check_outlier_contamination(detector.__class__.__name__, detector)

    # Add a correct interval constraint and check that the test passes.
    OutlierDetectorWithConstraint._parameter_constraints["contamination"] = [
        Interval(Real, 0, 0.5, closed="right")
    ]
    detector = OutlierDetectorWithConstraint()
    check_outlier_contamination(detector.__class__.__name__, detector)

    incorrect_intervals = [
        Interval(Integral, 0, 1, closed="right"),  # not an integral interval
        Interval(Real, -1, 1, closed="right"),  # lower bound is negative
        Interval(Real, 0, 2, closed="right"),  # upper bound is greater than 1
        Interval(Real, 0, 0.5, closed="left"),  # lower bound include 0
    ]

    err_msg = r"contamination constraint should be an interval in \(0, 0.5\]"
    for interval in incorrect_intervals:
        OutlierDetectorWithConstraint._parameter_constraints["contamination"] = [
            interval
        ]
        detector = OutlierDetectorWithConstraint()
        with raises(AssertionError, match=err_msg):
            check_outlier_contamination(detector.__class__.__name__, detector)


def test_decision_proba_tie_ranking():
    """Check that in case with some probabilities ties, we relax the
    ranking comparison with the decision function.
    Non-regression test for:
    https://github.com/scikit-learn/scikit-learn/issues/24025
    """
    estimator = SGDClassifier(loss="log_loss")
    check_decision_proba_consistency("SGDClassifier", estimator)
