"""
The :mod:`sklearn.metrics.scorer` submodule implements a flexible
interface for model selection and evaluation using
arbitrary score functions.

A scorer object is a callable that can be passed to
:class:`~sklearn.model_selection.GridSearchCV` or
:func:`sklearn.model_selection.cross_val_score` as the ``scoring``
parameter, to specify how a model should be evaluated.

The signature of the call is ``(estimator, X, y)`` where ``estimator``
is the model to be evaluated, ``X`` is the test data and ``y`` is the
ground truth labeling (or ``None`` in the case of unsupervised models).
"""

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import copy
import warnings
from collections import Counter
from functools import partial
from inspect import signature
from numbers import Integral
from traceback import format_exc

import numpy as np

from ..base import is_regressor
from ..utils import Bunch
from ..utils._param_validation import HasMethods, Hidden, StrOptions, validate_params
from ..utils._response import _get_response_values
from ..utils.metadata_routing import (
    MetadataRequest,
    MetadataRouter,
    MethodMapping,
    _MetadataRequester,
    _raise_for_params,
    _routing_enabled,
    get_routing_for_object,
    process_routing,
)
from ..utils.validation import _check_response_method
from . import (
    accuracy_score,
    average_precision_score,
    balanced_accuracy_score,
    brier_score_loss,
    class_likelihood_ratios,
    d2_absolute_error_score,
    explained_variance_score,
    f1_score,
    jaccard_score,
    log_loss,
    matthews_corrcoef,
    max_error,
    mean_absolute_error,
    mean_absolute_percentage_error,
    mean_gamma_deviance,
    mean_poisson_deviance,
    mean_squared_error,
    mean_squared_log_error,
    median_absolute_error,
    precision_score,
    r2_score,
    recall_score,
    roc_auc_score,
    root_mean_squared_error,
    root_mean_squared_log_error,
    top_k_accuracy_score,
)
from .cluster import (
    adjusted_mutual_info_score,
    adjusted_rand_score,
    completeness_score,
    fowlkes_mallows_score,
    homogeneity_score,
    mutual_info_score,
    normalized_mutual_info_score,
    rand_score,
    v_measure_score,
)


def _cached_call(cache, estimator, response_method, *args, **kwargs):
    """Call estimator with method and args and kwargs."""
    if cache is not None and response_method in cache:
        return cache[response_method]

    result, _ = _get_response_values(
        estimator, *args, response_method=response_method, **kwargs
    )

    if cache is not None:
        cache[response_method] = result

    return result


class _MultimetricScorer:
    """Callable for multimetric scoring used to avoid repeated calls
    to `predict_proba`, `predict`, and `decision_function`.

    `_MultimetricScorer` will return a dictionary of scores corresponding to
    the scorers in the dictionary. Note that `_MultimetricScorer` can be
    created with a dictionary with one key  (i.e. only one actual scorer).

    Parameters
    ----------
    scorers : dict
        Dictionary mapping names to callable scorers.

    raise_exc : bool, default=True
        Whether to raise the exception in `__call__` or not. If set to `False`
        a formatted string of the exception details is passed as result of
        the failing scorer.
    """

    def __init__(self, *, scorers, raise_exc=True):
        self._scorers = scorers
        self._raise_exc = raise_exc

    def __call__(self, estimator, *args, **kwargs):
        """Evaluate predicted target values."""
        scores = {}
        cache = {} if self._use_cache(estimator) else None
        cached_call = partial(_cached_call, cache)

        if _routing_enabled():
            routed_params = process_routing(self, "score", **kwargs)
        else:
            # Scorers all get the same args, and get all of them except sample_weight.
            # Only the ones having `sample_weight` in their signature will receive it.
            # This does not work for metadata other than sample_weight, and for those
            # users have to enable metadata routing.
            common_kwargs = {
                arg: value for arg, value in kwargs.items() if arg != "sample_weight"
            }
            routed_params = Bunch(
                **{name: Bunch(score=common_kwargs.copy()) for name in self._scorers}
            )
            if "sample_weight" in kwargs:
                for name, scorer in self._scorers.items():
                    if scorer._accept_sample_weight():
                        routed_params[name].score["sample_weight"] = kwargs[
                            "sample_weight"
                        ]

        for name, scorer in self._scorers.items():
            try:
                if isinstance(scorer, _BaseScorer):
                    score = scorer._score(
                        cached_call, estimator, *args, **routed_params.get(name).score
                    )
                else:
                    score = scorer(estimator, *args, **routed_params.get(name).score)
                scores[name] = score
            except Exception as e:
                if self._raise_exc:
                    raise e
                else:
                    scores[name] = format_exc()
        return scores

    def __repr__(self):
        scorers = ", ".join([f'"{s}"' for s in self._scorers])
        return f"MultiMetricScorer({scorers})"

    def _accept_sample_weight(self):
        # TODO(slep006): remove when metadata routing is the only way
        return any(scorer._accept_sample_weight() for scorer in self._scorers.values())

    def _use_cache(self, estimator):
        """Return True if using a cache is beneficial, thus when a response method will
        be called several time.
        """
        if len(self._scorers) == 1:  # Only one scorer
            return False

        counter = Counter(
            [
                _check_response_method(estimator, scorer._response_method).__name__
                for scorer in self._scorers.values()
                if isinstance(scorer, _BaseScorer)
            ]
        )
        if any(val > 1 for val in counter.values()):
            # The exact same response method or iterable of response methods
            # will be called more than once.
            return True

        return False

    def get_metadata_routing(self):
        """Get metadata routing of this object.

        Please check :ref:`User Guide <metadata_routing>` on how the routing
        mechanism works.

        .. versionadded:: 1.3

        Returns
        -------
        routing : MetadataRouter
            A :class:`~utils.metadata_routing.MetadataRouter` encapsulating
            routing information.
        """
        return MetadataRouter(owner=self.__class__.__name__).add(
            **self._scorers,
            method_mapping=MethodMapping().add(caller="score", callee="score"),
        )


class _BaseScorer(_MetadataRequester):
    """Base scorer that is used as `scorer(estimator, X, y_true)`.

    Parameters
    ----------
    score_func : callable
        The score function to use. It will be called as
        `score_func(y_true, y_pred, **kwargs)`.

    sign : int
        Either 1 or -1 to returns the score with `sign * score_func(estimator, X, y)`.
        Thus, `sign` defined if higher scores are better or worse.

    kwargs : dict
        Additional parameters to pass to the score function.

    response_method : str
        The method to call on the estimator to get the response values.
    """

    def __init__(self, score_func, sign, kwargs, response_method="predict"):
        self._score_func = score_func
        self._sign = sign
        self._kwargs = kwargs
        self._response_method = response_method
        # TODO (1.8): remove in 1.8 (scoring="max_error" has been deprecated in 1.6)
        self._deprecation_msg = None

    def _get_pos_label(self):
        if "pos_label" in self._kwargs:
            return self._kwargs["pos_label"]
        score_func_params = signature(self._score_func).parameters
        if "pos_label" in score_func_params:
            return score_func_params["pos_label"].default
        return None

    def _accept_sample_weight(self):
        # TODO(slep006): remove when metadata routing is the only way
        return "sample_weight" in signature(self._score_func).parameters

    def __repr__(self):
        sign_string = "" if self._sign > 0 else ", greater_is_better=False"
        response_method_string = f", response_method={self._response_method!r}"
        kwargs_string = "".join([f", {k}={v}" for k, v in self._kwargs.items()])

        return (
            f"make_scorer({self._score_func.__name__}{sign_string}"
            f"{response_method_string}{kwargs_string})"
        )

    def __call__(self, estimator, X, y_true, sample_weight=None, **kwargs):
        """Evaluate predicted target values for X relative to y_true.

        Parameters
        ----------
        estimator : object
            Trained estimator to use for scoring. Must have a predict_proba
            method; the output of that is used to compute the score.

        X : {array-like, sparse matrix}
            Test data that will be fed to estimator.predict.

        y_true : array-like
            Gold standard target values for X.

        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights.

        **kwargs : dict
            Other parameters passed to the scorer. Refer to
            :func:`set_score_request` for more details.

            Only available if `enable_metadata_routing=True`. See the
            :ref:`User Guide <metadata_routing>`.

            .. versionadded:: 1.3

        Returns
        -------
        score : float
            Score function applied to prediction of estimator on X.
        """
        # TODO (1.8): remove in 1.8 (scoring="max_error" has been deprecated in 1.6)
        if self._deprecation_msg is not None:
            warnings.warn(
                self._deprecation_msg, category=DeprecationWarning, stacklevel=2
            )

        _raise_for_params(kwargs, self, None)

        _kwargs = copy.deepcopy(kwargs)
        if sample_weight is not None:
            _kwargs["sample_weight"] = sample_weight

        return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)

    def _warn_overlap(self, message, kwargs):
        """Warn if there is any overlap between ``self._kwargs`` and ``kwargs``.

        This method is intended to be used to check for overlap between
        ``self._kwargs`` and ``kwargs`` passed as metadata.
        """
        _kwargs = set() if self._kwargs is None else set(self._kwargs.keys())
        overlap = _kwargs.intersection(kwargs.keys())
        if overlap:
            warnings.warn(
                f"{message} Overlapping parameters are: {overlap}", UserWarning
            )

    def set_score_request(self, **kwargs):
        """Set requested parameters by the scorer.

        Please see :ref:`User Guide <metadata_routing>` on how the routing
        mechanism works.

        .. versionadded:: 1.3

        Parameters
        ----------
        kwargs : dict
            Arguments should be of the form ``param_name=alias``, and `alias`
            can be one of ``{True, False, None, str}``.
        """
        if not _routing_enabled():
            raise RuntimeError(
                "This method is only available when metadata routing is enabled."
                " You can enable it using"
                " sklearn.set_config(enable_metadata_routing=True)."
            )

        self._warn_overlap(
            message=(
                "You are setting metadata request for parameters which are "
                "already set as kwargs for this metric. These set values will be "
                "overridden by passed metadata if provided. Please pass them either "
                "as metadata or kwargs to `make_scorer`."
            ),
            kwargs=kwargs,
        )
        self._metadata_request = MetadataRequest(owner=self.__class__.__name__)
        for param, alias in kwargs.items():
            self._metadata_request.score.add_request(param=param, alias=alias)
        return self


class _Scorer(_BaseScorer):
    def _score(self, method_caller, estimator, X, y_true, **kwargs):
        """Evaluate the response method of `estimator` on `X` and `y_true`.

        Parameters
        ----------
        method_caller : callable
            Returns predictions given an estimator, method name, and other
            arguments, potentially caching results.

        estimator : object
            Trained estimator to use for scoring.

        X : {array-like, sparse matrix}
            Test data that will be fed to clf.decision_function or
            clf.predict_proba.

        y_true : array-like
            Gold standard target values for X. These must be class labels,
            not decision function values.

        **kwargs : dict
            Other parameters passed to the scorer. Refer to
            :func:`set_score_request` for more details.

        Returns
        -------
        score : float
            Score function applied to prediction of estimator on X.
        """
        self._warn_overlap(
            message=(
                "There is an overlap between set kwargs of this scorer instance and"
                " passed metadata. Please pass them either as kwargs to `make_scorer`"
                " or metadata, but not both."
            ),
            kwargs=kwargs,
        )

        pos_label = None if is_regressor(estimator) else self._get_pos_label()
        response_method = _check_response_method(estimator, self._response_method)
        y_pred = method_caller(
            estimator,
            _get_response_method_name(response_method),
            X,
            pos_label=pos_label,
        )

        scoring_kwargs = {**self._kwargs, **kwargs}
        return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs)


@validate_params(
    {
        "scoring": [str, callable, None],
    },
    prefer_skip_nested_validation=True,
)
def get_scorer(scoring):
    """Get a scorer from string.

    Read more in the :ref:`User Guide <scoring_parameter>`.
    :func:`~sklearn.metrics.get_scorer_names` can be used to retrieve the names
    of all available scorers.

    Parameters
    ----------
    scoring : str, callable or None
        Scoring method as string. If callable it is returned as is.
        If None, returns None.

    Returns
    -------
    scorer : callable
        The scorer.

    Notes
    -----
    When passed a string, this function always returns a copy of the scorer
    object. Calling `get_scorer` twice for the same scorer results in two
    separate scorer objects.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.dummy import DummyClassifier
    >>> from sklearn.metrics import get_scorer
    >>> X = np.reshape([0, 1, -1, -0.5, 2], (-1, 1))
    >>> y = np.array([0, 1, 1, 0, 1])
    >>> classifier = DummyClassifier(strategy="constant", constant=0).fit(X, y)
    >>> accuracy = get_scorer("accuracy")
    >>> accuracy(classifier, X, y)
    0.4
    """
    if isinstance(scoring, str):
        try:
            if scoring == "max_error":
                # TODO (1.8): scoring="max_error" has been deprecated in 1.6,
                # remove in 1.8
                scorer = max_error_scorer
            else:
                scorer = copy.deepcopy(_SCORERS[scoring])
        except KeyError:
            raise ValueError(
                "%r is not a valid scoring value. "
                "Use sklearn.metrics.get_scorer_names() "
                "to get valid options." % scoring
            )
    else:
        scorer = scoring
    return scorer


class _PassthroughScorer(_MetadataRequester):
    # Passes scoring of estimator's `score` method back to estimator if scoring
    # is `None`.

    def __init__(self, estimator):
        self._estimator = estimator

        requests = MetadataRequest(owner=self.__class__.__name__)
        try:
            requests.score = copy.deepcopy(estimator._metadata_request.score)
        except AttributeError:
            try:
                requests.score = copy.deepcopy(estimator._get_default_requests().score)
            except AttributeError:
                pass

        self._metadata_request = requests

    def __call__(self, estimator, *args, **kwargs):
        """Method that wraps estimator.score"""
        return estimator.score(*args, **kwargs)

    def __repr__(self):
        return f"{self._estimator.__class__}.score"

    def _accept_sample_weight(self):
        # TODO(slep006): remove when metadata routing is the only way
        return "sample_weight" in signature(self._estimator.score).parameters

    def get_metadata_routing(self):
        """Get requested data properties.

        Please check :ref:`User Guide <metadata_routing>` on how the routing
        mechanism works.

        .. versionadded:: 1.3

        Returns
        -------
        routing : MetadataRouter
            A :class:`~utils.metadata_routing.MetadataRouter` encapsulating
            routing information.
        """
        return get_routing_for_object(self._metadata_request)

    def set_score_request(self, **kwargs):
        """Set requested parameters by the scorer.

        Please see :ref:`User Guide <metadata_routing>` on how the routing
        mechanism works.

        .. versionadded:: 1.5

        Parameters
        ----------
        kwargs : dict
            Arguments should be of the form ``param_name=alias``, and `alias`
            can be one of ``{True, False, None, str}``.
        """
        if not _routing_enabled():
            raise RuntimeError(
                "This method is only available when metadata routing is enabled."
                " You can enable it using"
                " sklearn.set_config(enable_metadata_routing=True)."
            )

        for param, alias in kwargs.items():
            self._metadata_request.score.add_request(param=param, alias=alias)
        return self


def _check_multimetric_scoring(estimator, scoring):
    """Check the scoring parameter in cases when multiple metrics are allowed.

    In addition, multimetric scoring leverages a caching mechanism to not call the same
    estimator response method multiple times. Hence, the scorer is modified to only use
    a single response method given a list of response methods and the estimator.

    Parameters
    ----------
    estimator : sklearn estimator instance
        The estimator for which the scoring will be applied.

    scoring : list, tuple or dict
        Strategy to evaluate the performance of the cross-validated model on
        the test set.

        The possibilities are:

        - a list or tuple of unique strings;
        - a callable returning a dictionary where they keys are the metric
          names and the values are the metric scores;
        - a dictionary with metric names as keys and callables a values.

        See :ref:`multimetric_grid_search` for an example.

    Returns
    -------
    scorers_dict : dict
        A dict mapping each scorer name to its validated scorer.
    """
    err_msg_generic = (
        f"scoring is invalid (got {scoring!r}). Refer to the "
        "scoring glossary for details: "
        "https://scikit-learn.org/stable/glossary.html#term-scoring"
    )

    if isinstance(scoring, (list, tuple, set)):
        err_msg = (
            "The list/tuple elements must be unique strings of predefined scorers. "
        )
        try:
            keys = set(scoring)
        except TypeError as e:
            raise ValueError(err_msg) from e

        if len(keys) != len(scoring):
            raise ValueError(
                f"{err_msg} Duplicate elements were found in"
                f" the given list. {scoring!r}"
            )
        elif len(keys) > 0:
            if not all(isinstance(k, str) for k in keys):
                if any(callable(k) for k in keys):
                    raise ValueError(
                        f"{err_msg} One or more of the elements "
                        "were callables. Use a dict of score "
                        "name mapped to the scorer callable. "
                        f"Got {scoring!r}"
                    )
                else:
                    raise ValueError(
                        f"{err_msg} Non-string types were found "
                        f"in the given list. Got {scoring!r}"
                    )
            scorers = {
                scorer: check_scoring(estimator, scoring=scorer) for scorer in scoring
            }
        else:
            raise ValueError(f"{err_msg} Empty list was given. {scoring!r}")

    elif isinstance(scoring, dict):
        keys = set(scoring)
        if not all(isinstance(k, str) for k in keys):
            raise ValueError(
                "Non-string types were found in the keys of "
                f"the given dict. scoring={scoring!r}"
            )
        if len(keys) == 0:
            raise ValueError(f"An empty dict was passed. {scoring!r}")
        scorers = {
            key: check_scoring(estimator, scoring=scorer)
            for key, scorer in scoring.items()
        }
    else:
        raise ValueError(err_msg_generic)

    return scorers


def _get_response_method_name(response_method):
    try:
        return response_method.__name__
    except AttributeError:
        return _get_response_method_name(response_method.func)


@validate_params(
    {
        "score_func": [callable],
        "response_method": [
            None,
            list,
            tuple,
            StrOptions({"predict", "predict_proba", "decision_function"}),
            Hidden(StrOptions({"default"})),
        ],
        "greater_is_better": ["boolean"],
    },
    prefer_skip_nested_validation=True,
)
def make_scorer(
    score_func, *, response_method="default", greater_is_better=True, **kwargs
):
    """Make a scorer from a performance metric or loss function.

    A scorer is a wrapper around an arbitrary metric or loss function that is called
    with the signature `scorer(estimator, X, y_true, **kwargs)`.

    It is accepted in all scikit-learn estimators or functions allowing a `scoring`
    parameter.

    The parameter `response_method` allows to specify which method of the estimator
    should be used to feed the scoring/loss function.

    Read more in the :ref:`User Guide <scoring_callable>`.

    Parameters
    ----------
    score_func : callable
        Score function (or loss function) with signature
        ``score_func(y, y_pred, **kwargs)``.

    response_method : {"predict_proba", "decision_function", "predict"} or \
            list/tuple of such str, default=None

        Specifies the response method to use get prediction from an estimator
        (i.e. :term:`predict_proba`, :term:`decision_function` or
        :term:`predict`). Possible choices are:

        - if `str`, it corresponds to the name to the method to return;
        - if a list or tuple of `str`, it provides the method names in order of
          preference. The method returned corresponds to the first method in
          the list and which is implemented by `estimator`.
        - if `None`, it is equivalent to `"predict"`.

        .. versionadded:: 1.4

        .. deprecated:: 1.6
            None is equivalent to 'predict' and is deprecated. It will be removed in
            version 1.8.

    greater_is_better : bool, default=True
        Whether `score_func` is a score function (default), meaning high is
        good, or a loss function, meaning low is good. In the latter case, the
        scorer object will sign-flip the outcome of the `score_func`.

    **kwargs : additional arguments
        Additional parameters to be passed to `score_func`.

    Returns
    -------
    scorer : callable
        Callable object that returns a scalar score; greater is better.

    Examples
    --------
    >>> from sklearn.metrics import fbeta_score, make_scorer
    >>> ftwo_scorer = make_scorer(fbeta_score, beta=2)
    >>> ftwo_scorer
    make_scorer(fbeta_score, response_method='predict', beta=2)
    >>> from sklearn.model_selection import GridSearchCV
    >>> from sklearn.svm import LinearSVC
    >>> grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]},
    ...                     scoring=ftwo_scorer)
    """
    sign = 1 if greater_is_better else -1

    if response_method is None:
        warnings.warn(
            "response_method=None is deprecated in version 1.6 and will be removed "
            "in version 1.8. Leave it to its default value to avoid this warning.",
            FutureWarning,
        )
        response_method = "predict"
    elif response_method == "default":
        response_method = "predict"

    return _Scorer(score_func, sign, kwargs, response_method)


# Standard regression scores
explained_variance_scorer = make_scorer(explained_variance_score)
r2_scorer = make_scorer(r2_score)
neg_max_error_scorer = make_scorer(max_error, greater_is_better=False)
max_error_scorer = make_scorer(max_error, greater_is_better=False)
# TODO (1.8): remove in 1.8 (scoring="max_error" has been deprecated in 1.6)
deprecation_msg = (
    "Scoring method max_error was renamed to "
    "neg_max_error in version 1.6 and will "
    "be removed in 1.8."
)
max_error_scorer._deprecation_msg = deprecation_msg
neg_mean_squared_error_scorer = make_scorer(mean_squared_error, greater_is_better=False)
neg_mean_squared_log_error_scorer = make_scorer(
    mean_squared_log_error, greater_is_better=False
)
neg_mean_absolute_error_scorer = make_scorer(
    mean_absolute_error, greater_is_better=False
)
neg_mean_absolute_percentage_error_scorer = make_scorer(
    mean_absolute_percentage_error, greater_is_better=False
)
neg_median_absolute_error_scorer = make_scorer(
    median_absolute_error, greater_is_better=False
)
neg_root_mean_squared_error_scorer = make_scorer(
    root_mean_squared_error, greater_is_better=False
)
neg_root_mean_squared_log_error_scorer = make_scorer(
    root_mean_squared_log_error, greater_is_better=False
)
neg_mean_poisson_deviance_scorer = make_scorer(
    mean_poisson_deviance, greater_is_better=False
)

neg_mean_gamma_deviance_scorer = make_scorer(
    mean_gamma_deviance, greater_is_better=False
)
d2_absolute_error_scorer = make_scorer(d2_absolute_error_score)

# Standard Classification Scores
accuracy_scorer = make_scorer(accuracy_score)
balanced_accuracy_scorer = make_scorer(balanced_accuracy_score)
matthews_corrcoef_scorer = make_scorer(matthews_corrcoef)


def positive_likelihood_ratio(y_true, y_pred):
    return class_likelihood_ratios(y_true, y_pred, replace_undefined_by=1.0)[0]


def negative_likelihood_ratio(y_true, y_pred):
    return class_likelihood_ratios(y_true, y_pred, replace_undefined_by=1.0)[1]


positive_likelihood_ratio_scorer = make_scorer(positive_likelihood_ratio)
neg_negative_likelihood_ratio_scorer = make_scorer(
    negative_likelihood_ratio, greater_is_better=False
)

# Score functions that need decision values
top_k_accuracy_scorer = make_scorer(
    top_k_accuracy_score,
    greater_is_better=True,
    response_method=("decision_function", "predict_proba"),
)
roc_auc_scorer = make_scorer(
    roc_auc_score,
    greater_is_better=True,
    response_method=("decision_function", "predict_proba"),
)
average_precision_scorer = make_scorer(
    average_precision_score,
    response_method=("decision_function", "predict_proba"),
)
roc_auc_ovo_scorer = make_scorer(
    roc_auc_score, response_method="predict_proba", multi_class="ovo"
)
roc_auc_ovo_weighted_scorer = make_scorer(
    roc_auc_score,
    response_method="predict_proba",
    multi_class="ovo",
    average="weighted",
)
roc_auc_ovr_scorer = make_scorer(
    roc_auc_score, response_method="predict_proba", multi_class="ovr"
)
roc_auc_ovr_weighted_scorer = make_scorer(
    roc_auc_score,
    response_method="predict_proba",
    multi_class="ovr",
    average="weighted",
)

# Score function for probabilistic classification
neg_log_loss_scorer = make_scorer(
    log_loss, greater_is_better=False, response_method="predict_proba"
)
neg_brier_score_scorer = make_scorer(
    brier_score_loss, greater_is_better=False, response_method="predict_proba"
)
brier_score_loss_scorer = make_scorer(
    brier_score_loss, greater_is_better=False, response_method="predict_proba"
)


# Clustering scores
adjusted_rand_scorer = make_scorer(adjusted_rand_score)
rand_scorer = make_scorer(rand_score)
homogeneity_scorer = make_scorer(homogeneity_score)
completeness_scorer = make_scorer(completeness_score)
v_measure_scorer = make_scorer(v_measure_score)
mutual_info_scorer = make_scorer(mutual_info_score)
adjusted_mutual_info_scorer = make_scorer(adjusted_mutual_info_score)
normalized_mutual_info_scorer = make_scorer(normalized_mutual_info_score)
fowlkes_mallows_scorer = make_scorer(fowlkes_mallows_score)


_SCORERS = dict(
    explained_variance=explained_variance_scorer,
    r2=r2_scorer,
    neg_max_error=neg_max_error_scorer,
    matthews_corrcoef=matthews_corrcoef_scorer,
    neg_median_absolute_error=neg_median_absolute_error_scorer,
    neg_mean_absolute_error=neg_mean_absolute_error_scorer,
    neg_mean_absolute_percentage_error=neg_mean_absolute_percentage_error_scorer,
    neg_mean_squared_error=neg_mean_squared_error_scorer,
    neg_mean_squared_log_error=neg_mean_squared_log_error_scorer,
    neg_root_mean_squared_error=neg_root_mean_squared_error_scorer,
    neg_root_mean_squared_log_error=neg_root_mean_squared_log_error_scorer,
    neg_mean_poisson_deviance=neg_mean_poisson_deviance_scorer,
    neg_mean_gamma_deviance=neg_mean_gamma_deviance_scorer,
    d2_absolute_error_score=d2_absolute_error_scorer,
    accuracy=accuracy_scorer,
    top_k_accuracy=top_k_accuracy_scorer,
    roc_auc=roc_auc_scorer,
    roc_auc_ovr=roc_auc_ovr_scorer,
    roc_auc_ovo=roc_auc_ovo_scorer,
    roc_auc_ovr_weighted=roc_auc_ovr_weighted_scorer,
    roc_auc_ovo_weighted=roc_auc_ovo_weighted_scorer,
    balanced_accuracy=balanced_accuracy_scorer,
    average_precision=average_precision_scorer,
    neg_log_loss=neg_log_loss_scorer,
    neg_brier_score=neg_brier_score_scorer,
    positive_likelihood_ratio=positive_likelihood_ratio_scorer,
    neg_negative_likelihood_ratio=neg_negative_likelihood_ratio_scorer,
    # Cluster metrics that use supervised evaluation
    adjusted_rand_score=adjusted_rand_scorer,
    rand_score=rand_scorer,
    homogeneity_score=homogeneity_scorer,
    completeness_score=completeness_scorer,
    v_measure_score=v_measure_scorer,
    mutual_info_score=mutual_info_scorer,
    adjusted_mutual_info_score=adjusted_mutual_info_scorer,
    normalized_mutual_info_score=normalized_mutual_info_scorer,
    fowlkes_mallows_score=fowlkes_mallows_scorer,
)


def get_scorer_names():
    """Get the names of all available scorers.

    These names can be passed to :func:`~sklearn.metrics.get_scorer` to
    retrieve the scorer object.

    Returns
    -------
    list of str
        Names of all available scorers.

    Examples
    --------
    >>> from sklearn.metrics import get_scorer_names
    >>> all_scorers = get_scorer_names()
    >>> type(all_scorers)
    <class 'list'>
    >>> all_scorers[:3]
    ['accuracy', 'adjusted_mutual_info_score', 'adjusted_rand_score']
    >>> "roc_auc" in all_scorers
    True
    """
    return sorted(_SCORERS.keys())


for name, metric in [
    ("precision", precision_score),
    ("recall", recall_score),
    ("f1", f1_score),
    ("jaccard", jaccard_score),
]:
    _SCORERS[name] = make_scorer(metric, average="binary")
    for average in ["macro", "micro", "samples", "weighted"]:
        qualified_name = "{0}_{1}".format(name, average)
        _SCORERS[qualified_name] = make_scorer(metric, pos_label=None, average=average)


@validate_params(
    {
        "estimator": [HasMethods("fit"), None],
        "scoring": [
            StrOptions(set(get_scorer_names())),
            callable,
            list,
            set,
            tuple,
            dict,
            None,
        ],
        "allow_none": ["boolean"],
        "raise_exc": ["boolean"],
    },
    prefer_skip_nested_validation=True,
)
def check_scoring(estimator=None, scoring=None, *, allow_none=False, raise_exc=True):
    """Determine scorer from user options.

    A TypeError will be thrown if the estimator cannot be scored.

    Parameters
    ----------
    estimator : estimator object implementing 'fit' or None, default=None
        The object to use to fit the data. If `None`, then this function may error
        depending on `allow_none`.

    scoring : str, callable, list, tuple, set, or dict, default=None
        Scorer to use. If `scoring` represents a single score, one can use:

        - a single string (see :ref:`scoring_string_names`);
        - a callable (see :ref:`scoring_callable`) that returns a single value;
        - `None`, the `estimator`'s
          :ref:`default evaluation criterion <scoring_api_overview>` is used.

        If `scoring` represents multiple scores, one can use:

        - a list, tuple or set of unique strings;
        - a callable returning a dictionary where the keys are the metric names and the
          values are the metric scorers;
        - a dictionary with metric names as keys and callables a values. The callables
          need to have the signature `callable(estimator, X, y)`.

    allow_none : bool, default=False
        Whether to return None or raise an error if no `scoring` is specified and the
        estimator has no `score` method.

    raise_exc : bool, default=True
        Whether to raise an exception (if a subset of the scorers in multimetric scoring
        fails) or to return an error code.

        - If set to `True`, raises the failing scorer's exception.
        - If set to `False`, a formatted string of the exception details is passed as
          result of the failing scorer(s).

        This applies if `scoring` is list, tuple, set, or dict. Ignored if `scoring` is
        a str or a callable.

        .. versionadded:: 1.6

    Returns
    -------
    scoring : callable
        A scorer callable object / function with signature ``scorer(estimator, X, y)``.

    Examples
    --------
    >>> from sklearn.datasets import load_iris
    >>> from sklearn.metrics import check_scoring
    >>> from sklearn.tree import DecisionTreeClassifier
    >>> X, y = load_iris(return_X_y=True)
    >>> classifier = DecisionTreeClassifier(max_depth=2).fit(X, y)
    >>> scorer = check_scoring(classifier, scoring='accuracy')
    >>> scorer(classifier, X, y)
    0.96...

    >>> from sklearn.metrics import make_scorer, accuracy_score, mean_squared_log_error
    >>> X, y = load_iris(return_X_y=True)
    >>> y *= -1
    >>> clf = DecisionTreeClassifier().fit(X, y)
    >>> scoring = {
    ...     "accuracy": make_scorer(accuracy_score),
    ...     "mean_squared_log_error": make_scorer(mean_squared_log_error),
    ... }
    >>> scoring_call = check_scoring(estimator=clf, scoring=scoring, raise_exc=False)
    >>> scores = scoring_call(clf, X, y)
    >>> scores
    {'accuracy': 1.0, 'mean_squared_log_error': 'Traceback ...'}
    """
    if isinstance(scoring, str):
        return get_scorer(scoring)
    if callable(scoring):
        # Heuristic to ensure user has not passed a metric
        module = getattr(scoring, "__module__", None)
        if (
            hasattr(module, "startswith")
            and module.startswith("sklearn.metrics.")
            and not module.startswith("sklearn.metrics._scorer")
            and not module.startswith("sklearn.metrics.tests.")
        ):
            raise ValueError(
                "scoring value %r looks like it is a metric "
                "function rather than a scorer. A scorer should "
                "require an estimator as its first parameter. "
                "Please use `make_scorer` to convert a metric "
                "to a scorer." % scoring
            )
        return get_scorer(scoring)
    if isinstance(scoring, (list, tuple, set, dict)):
        scorers = _check_multimetric_scoring(estimator, scoring=scoring)
        return _MultimetricScorer(scorers=scorers, raise_exc=raise_exc)
    if scoring is None:
        if hasattr(estimator, "score"):
            return _PassthroughScorer(estimator)
        elif allow_none:
            return None
        else:
            raise TypeError(
                "If no scoring is specified, the estimator passed should "
                "have a 'score' method. The estimator %r does not." % estimator
            )


def _threshold_scores_to_class_labels(y_score, threshold, classes, pos_label):
    """Threshold `y_score` and return the associated class labels."""
    if pos_label is None:
        map_thresholded_score_to_label = np.array([0, 1])
    else:
        pos_label_idx = np.flatnonzero(classes == pos_label)[0]
        neg_label_idx = np.flatnonzero(classes != pos_label)[0]
        map_thresholded_score_to_label = np.array([neg_label_idx, pos_label_idx])

    return classes[map_thresholded_score_to_label[(y_score >= threshold).astype(int)]]


class _CurveScorer(_BaseScorer):
    """Scorer taking a continuous response and output a score for each threshold.

    Parameters
    ----------
    score_func : callable
        The score function to use. It will be called as
        `score_func(y_true, y_pred, **kwargs)`.

    sign : int
        Either 1 or -1 to returns the score with `sign * score_func(estimator, X, y)`.
        Thus, `sign` defined if higher scores are better or worse.

    kwargs : dict
        Additional parameters to pass to the score function.

    thresholds : int or array-like
        Related to the number of decision thresholds for which we want to compute the
        score. If an integer, it will be used to generate `thresholds` thresholds
        uniformly distributed between the minimum and maximum predicted scores. If an
        array-like, it will be used as the thresholds.

    response_method : str
        The method to call on the estimator to get the response values.
    """

    def __init__(self, score_func, sign, kwargs, thresholds, response_method):
        super().__init__(
            score_func=score_func,
            sign=sign,
            kwargs=kwargs,
            response_method=response_method,
        )
        self._thresholds = thresholds

    @classmethod
    def from_scorer(cls, scorer, response_method, thresholds):
        """Create a continuous scorer from a normal scorer."""
        instance = cls(
            score_func=scorer._score_func,
            sign=scorer._sign,
            response_method=response_method,
            thresholds=thresholds,
            kwargs=scorer._kwargs,
        )
        # transfer the metadata request
        instance._metadata_request = scorer._get_metadata_request()
        return instance

    def _score(self, method_caller, estimator, X, y_true, **kwargs):
        """Evaluate predicted target values for X relative to y_true.

        Parameters
        ----------
        method_caller : callable
            Returns predictions given an estimator, method name, and other
            arguments, potentially caching results.

        estimator : object
            Trained estimator to use for scoring.

        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            Test data that will be fed to estimator.predict.

        y_true : array-like of shape (n_samples,)
            Gold standard target values for X.

        **kwargs : dict
            Other parameters passed to the scorer. Refer to
            :func:`set_score_request` for more details.

        Returns
        -------
        scores : ndarray of shape (thresholds,)
            The scores associated to each threshold.

        potential_thresholds : ndarray of shape (thresholds,)
            The potential thresholds used to compute the scores.
        """
        pos_label = self._get_pos_label()
        y_score = method_caller(
            estimator, self._response_method, X, pos_label=pos_label
        )

        scoring_kwargs = {**self._kwargs, **kwargs}
        if isinstance(self._thresholds, Integral):
            potential_thresholds = np.linspace(
                np.min(y_score), np.max(y_score), self._thresholds
            )
        else:
            potential_thresholds = np.asarray(self._thresholds)
        score_thresholds = [
            self._sign
            * self._score_func(
                y_true,
                _threshold_scores_to_class_labels(
                    y_score, th, estimator.classes_, pos_label
                ),
                **scoring_kwargs,
            )
            for th in potential_thresholds
        ]
        return np.array(score_thresholds), potential_thresholds
