"""
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: Andreas Mueller <amueller@ais.uni-bonn.de>
#          Lars Buitinck
#          Arnaud Joly <arnaud.v.joly@gmail.com>
# License: Simplified BSD

from collections.abc import Iterable
from functools import partial
from collections import Counter

import numpy as np

from . import (r2_score, median_absolute_error, max_error, mean_absolute_error,
               mean_squared_error, mean_squared_log_error,
               mean_poisson_deviance, mean_gamma_deviance, accuracy_score,
               top_k_accuracy_score, f1_score, roc_auc_score,
               average_precision_score, precision_score, recall_score,
               log_loss, balanced_accuracy_score, explained_variance_score,
               brier_score_loss, jaccard_score, mean_absolute_percentage_error)

from .cluster import adjusted_rand_score
from .cluster import rand_score
from .cluster import homogeneity_score
from .cluster import completeness_score
from .cluster import v_measure_score
from .cluster import mutual_info_score
from .cluster import adjusted_mutual_info_score
from .cluster import normalized_mutual_info_score
from .cluster import fowlkes_mallows_score

from ..utils.multiclass import type_of_target
from ..utils.validation import _deprecate_positional_args
from ..base import is_regressor


def _cached_call(cache, estimator, method, *args, **kwargs):
    """Call estimator with method and args and kwargs."""
    if cache is None:
        return getattr(estimator, method)(*args, **kwargs)

    try:
        return cache[method]
    except KeyError:
        result = getattr(estimator, method)(*args, **kwargs)
        cache[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.
    """
    def __init__(self, **scorers):
        self._scorers = scorers

    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)

        for name, scorer in self._scorers.items():
            if isinstance(scorer, _BaseScorer):
                score = scorer._score(cached_call, estimator,
                                      *args, **kwargs)
            else:
                score = scorer(estimator, *args, **kwargs)
            scores[name] = score
        return scores

    def _use_cache(self, estimator):
        """Return True if using a cache is beneficial.

        Caching may be beneficial when one of these conditions holds:
          - `_ProbaScorer` will be called twice.
          - `_PredictScorer` will be called twice.
          - `_ThresholdScorer` will be called twice.
          - `_ThresholdScorer` and `_PredictScorer` are called and
             estimator is a regressor.
          - `_ThresholdScorer` and `_ProbaScorer` are called and
             estimator does not have a `decision_function` attribute.

        """
        if len(self._scorers) == 1:  # Only one scorer
            return False

        counter = Counter([type(v) for v in self._scorers.values()])

        if any(counter[known_type] > 1 for known_type in
               [_PredictScorer, _ProbaScorer, _ThresholdScorer]):
            return True

        if counter[_ThresholdScorer]:
            if is_regressor(estimator) and counter[_PredictScorer]:
                return True
            elif (counter[_ProbaScorer] and
                  not hasattr(estimator, "decision_function")):
                return True
        return False


class _BaseScorer:
    def __init__(self, score_func, sign, kwargs):
        self._kwargs = kwargs
        self._score_func = score_func
        self._sign = sign

    @staticmethod
    def _check_pos_label(pos_label, classes):
        if pos_label not in list(classes):
            raise ValueError(
                f"pos_label={pos_label} is not a valid label: {classes}"
            )

    def _select_proba_binary(self, y_pred, classes):
        """Select the column of the positive label in `y_pred` when
        probabilities are provided.

        Parameters
        ----------
        y_pred : ndarray of shape (n_samples, n_classes)
            The prediction given by `predict_proba`.

        classes : ndarray of shape (n_classes,)
            The class labels for the estimator.

        Returns
        -------
        y_pred : ndarray of shape (n_samples,)
            Probability predictions of the positive class.
        """
        if y_pred.shape[1] == 2:
            pos_label = self._kwargs.get("pos_label", classes[1])
            self._check_pos_label(pos_label, classes)
            col_idx = np.flatnonzero(classes == pos_label)[0]
            return y_pred[:, col_idx]

        err_msg = (
            f"Got predict_proba of shape {y_pred.shape}, but need "
            f"classifier with two classes for {self._score_func.__name__} "
            f"scoring"
        )
        raise ValueError(err_msg)

    def __repr__(self):
        kwargs_string = "".join([", %s=%s" % (str(k), str(v))
                                 for k, v in self._kwargs.items()])
        return ("make_scorer(%s%s%s%s)"
                % (self._score_func.__name__,
                   "" if self._sign > 0 else ", greater_is_better=False",
                   self._factory_args(), kwargs_string))

    def __call__(self, estimator, X, y_true, sample_weight=None):
        """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.

        Returns
        -------
        score : float
            Score function applied to prediction of estimator on X.
        """
        return self._score(partial(_cached_call, None), estimator, X, y_true,
                           sample_weight=sample_weight)

    def _factory_args(self):
        """Return non-default make_scorer arguments for repr."""
        return ""


class _PredictScorer(_BaseScorer):
    def _score(self, method_caller, estimator, X, y_true, sample_weight=None):
        """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. Must have a `predict`
            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.

        Returns
        -------
        score : float
            Score function applied to prediction of estimator on X.
        """

        y_pred = method_caller(estimator, "predict", X)
        if sample_weight is not None:
            return self._sign * self._score_func(y_true, y_pred,
                                                 sample_weight=sample_weight,
                                                 **self._kwargs)
        else:
            return self._sign * self._score_func(y_true, y_pred,
                                                 **self._kwargs)


class _ProbaScorer(_BaseScorer):
    def _score(self, method_caller, clf, X, y, sample_weight=None):
        """Evaluate predicted probabilities for X relative to y_true.

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

        clf : object
            Trained classifier 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 clf.predict_proba.

        y : array-like
            Gold standard target values for X. These must be class labels,
            not probabilities.

        sample_weight : array-like, default=None
            Sample weights.

        Returns
        -------
        score : float
            Score function applied to prediction of estimator on X.
        """

        y_type = type_of_target(y)
        y_pred = method_caller(clf, "predict_proba", X)
        if y_type == "binary" and y_pred.shape[1] <= 2:
            # `y_type` could be equal to "binary" even in a multi-class
            # problem: (when only 2 class are given to `y_true` during scoring)
            # Thus, we need to check for the shape of `y_pred`.
            y_pred = self._select_proba_binary(y_pred, clf.classes_)
        if sample_weight is not None:
            return self._sign * self._score_func(y, y_pred,
                                                 sample_weight=sample_weight,
                                                 **self._kwargs)
        else:
            return self._sign * self._score_func(y, y_pred, **self._kwargs)

    def _factory_args(self):
        return ", needs_proba=True"


class _ThresholdScorer(_BaseScorer):
    def _score(self, method_caller, clf, X, y, sample_weight=None):
        """Evaluate decision function output for X relative to y_true.

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

        clf : object
            Trained classifier to use for scoring. Must have either a
            decision_function method or 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 clf.decision_function or
            clf.predict_proba.

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

        sample_weight : array-like, default=None
            Sample weights.

        Returns
        -------
        score : float
            Score function applied to prediction of estimator on X.
        """

        y_type = type_of_target(y)
        if y_type not in ("binary", "multilabel-indicator"):
            raise ValueError("{0} format is not supported".format(y_type))

        if is_regressor(clf):
            y_pred = method_caller(clf, "predict", X)
        else:
            try:
                y_pred = method_caller(clf, "decision_function", X)

                if isinstance(y_pred, list):
                    # For multi-output multi-class estimator
                    y_pred = np.vstack([p for p in y_pred]).T
                elif y_type == "binary" and "pos_label" in self._kwargs:
                    self._check_pos_label(
                        self._kwargs["pos_label"], clf.classes_
                    )
                    if self._kwargs["pos_label"] == clf.classes_[0]:
                        # The implicit positive class of the binary classifier
                        # does not match `pos_label`: we need to invert the
                        # predictions
                        y_pred *= -1

            except (NotImplementedError, AttributeError):
                y_pred = method_caller(clf, "predict_proba", X)

                if y_type == "binary":
                    y_pred = self._select_proba_binary(y_pred, clf.classes_)
                elif isinstance(y_pred, list):
                    y_pred = np.vstack([p[:, -1] for p in y_pred]).T

        if sample_weight is not None:
            return self._sign * self._score_func(y, y_pred,
                                                 sample_weight=sample_weight,
                                                 **self._kwargs)
        else:
            return self._sign * self._score_func(y, y_pred, **self._kwargs)

    def _factory_args(self):
        return ", needs_threshold=True"


def get_scorer(scoring):
    """Get a scorer from string.

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

    Parameters
    ----------
    scoring : str or callable
        Scoring method as string. If callable it is returned as is.

    Returns
    -------
    scorer : callable
        The scorer.
    """
    if isinstance(scoring, str):
        try:
            scorer = SCORERS[scoring]
        except KeyError:
            raise ValueError('%r is not a valid scoring value. '
                             'Use sorted(sklearn.metrics.SCORERS.keys()) '
                             'to get valid options.' % scoring)
    else:
        scorer = scoring
    return scorer


def _passthrough_scorer(estimator, *args, **kwargs):
    """Function that wraps estimator.score"""
    return estimator.score(*args, **kwargs)


@_deprecate_positional_args
def check_scoring(estimator, scoring=None, *, allow_none=False):
    """Determine scorer from user options.

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

    Parameters
    ----------
    estimator : estimator object implementing 'fit'
        The object to use to fit the data.

    scoring : str or callable, default=None
        A string (see model evaluation documentation) or
        a scorer callable object / function with signature
        ``scorer(estimator, X, y)``.

    allow_none : bool, default=False
        If no scoring is specified and the estimator has no score function, we
        can either return None or raise an exception.

    Returns
    -------
    scoring : callable
        A scorer callable object / function with signature
        ``scorer(estimator, X, y)``.
    """
    if not hasattr(estimator, 'fit'):
        raise TypeError("estimator should be an estimator implementing "
                        "'fit' method, %r was passed" % estimator)
    if isinstance(scoring, str):
        return get_scorer(scoring)
    elif 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)
    elif scoring is None:
        if hasattr(estimator, 'score'):
            return _passthrough_scorer
        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)
    elif isinstance(scoring, Iterable):
        raise ValueError("For evaluating multiple scores, use "
                         "sklearn.model_selection.cross_validate instead. "
                         "{0} was passed.".format(scoring))
    else:
        raise ValueError("scoring value should either be a callable, string or"
                         " None. %r was passed" % scoring)


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

    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. ")
        invalid = False
        try:
            keys = set(scoring)
        except TypeError:
            invalid = True
        if invalid:
            raise ValueError(err_msg)

        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


@_deprecate_positional_args
def make_scorer(score_func, *, greater_is_better=True, needs_proba=False,
                needs_threshold=False, **kwargs):
    """Make a scorer from a performance metric or loss function.

    This factory function wraps scoring functions for use in
    :class:`~sklearn.model_selection.GridSearchCV` and
    :func:`~sklearn.model_selection.cross_val_score`.
    It takes a score function, such as :func:`~sklearn.metrics.accuracy_score`,
    :func:`~sklearn.metrics.mean_squared_error`,
    :func:`~sklearn.metrics.adjusted_rand_index` or
    :func:`~sklearn.metrics.average_precision`
    and returns a callable that scores an estimator's output.
    The signature of the call is `(estimator, X, y)` where `estimator`
    is the model to be evaluated, `X` is the data and `y` is the
    ground truth labeling (or `None` in the case of unsupervised models).

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

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

    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.

    needs_proba : bool, default=False
        Whether score_func requires predict_proba to get probability estimates
        out of a classifier.

        If True, for binary `y_true`, the score function is supposed to accept
        a 1D `y_pred` (i.e., probability of the positive class, shape
        `(n_samples,)`).

    needs_threshold : bool, default=False
        Whether score_func takes a continuous decision certainty.
        This only works for binary classification using estimators that
        have either a decision_function or predict_proba method.

        If True, for binary `y_true`, the score function is supposed to accept
        a 1D `y_pred` (i.e., probability of the positive class or the decision
        function, shape `(n_samples,)`).

        For example ``average_precision`` or the area under the roc curve
        can not be computed using discrete predictions alone.

    **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, beta=2)
    >>> from sklearn.model_selection import GridSearchCV
    >>> from sklearn.svm import LinearSVC
    >>> grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]},
    ...                     scoring=ftwo_scorer)

    Notes
    -----
    If `needs_proba=False` and `needs_threshold=False`, the score
    function is supposed to accept the output of :term:`predict`. If
    `needs_proba=True`, the score function is supposed to accept the
    output of :term:`predict_proba` (For binary `y_true`, the score function is
    supposed to accept probability of the positive class). If
    `needs_threshold=True`, the score function is supposed to accept the
    output of :term:`decision_function`.
    """
    sign = 1 if greater_is_better else -1
    if needs_proba and needs_threshold:
        raise ValueError("Set either needs_proba or needs_threshold to True,"
                         " but not both.")
    if needs_proba:
        cls = _ProbaScorer
    elif needs_threshold:
        cls = _ThresholdScorer
    else:
        cls = _PredictScorer
    return cls(score_func, sign, kwargs)


# Standard regression scores
explained_variance_scorer = make_scorer(explained_variance_score)
r2_scorer = make_scorer(r2_score)
max_error_scorer = make_scorer(max_error,
                               greater_is_better=False)
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(mean_squared_error,
                                                 greater_is_better=False,
                                                 squared=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
)

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

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

# Score function for probabilistic classification
neg_log_loss_scorer = make_scorer(log_loss, greater_is_better=False,
                                  needs_proba=True)
neg_brier_score_scorer = make_scorer(brier_score_loss,
                                     greater_is_better=False,
                                     needs_proba=True)
brier_score_loss_scorer = make_scorer(brier_score_loss,
                                      greater_is_better=False,
                                      needs_proba=True)


# 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,
               max_error=max_error_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,  # noqa
               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_mean_poisson_deviance=neg_mean_poisson_deviance_scorer,
               neg_mean_gamma_deviance=neg_mean_gamma_deviance_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,
               # 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)


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)
