ai-content-maker/.venv/Lib/site-packages/sklearn/metrics/_ranking.py

"""Metrics to assess performance on classification task given scores.

Functions named as ``*_score`` return a scalar value to maximize: the higher
the better.

Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better.
"""

# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#          Mathieu Blondel <mathieu@mblondel.org>
#          Olivier Grisel <olivier.grisel@ensta.org>
#          Arnaud Joly <a.joly@ulg.ac.be>
#          Jochen Wersdorfer <jochen@wersdoerfer.de>
#          Lars Buitinck
#          Joel Nothman <joel.nothman@gmail.com>
#          Noel Dawe <noel@dawe.me>
#          Michal Karbownik <michakarbownik@gmail.com>
# License: BSD 3 clause


import warnings
from functools import partial
from numbers import Integral, Real

import numpy as np
from scipy.sparse import csr_matrix, issparse
from scipy.stats import rankdata

from ..exceptions import UndefinedMetricWarning
from ..preprocessing import label_binarize
from ..utils import (
    assert_all_finite,
    check_array,
    check_consistent_length,
    column_or_1d,
)
from ..utils._encode import _encode, _unique
from ..utils._param_validation import Interval, StrOptions, validate_params
from ..utils.extmath import stable_cumsum
from ..utils.fixes import trapezoid
from ..utils.multiclass import type_of_target
from ..utils.sparsefuncs import count_nonzero
from ..utils.validation import _check_pos_label_consistency, _check_sample_weight
from ._base import _average_binary_score, _average_multiclass_ovo_score


@validate_params(
    {"x": ["array-like"], "y": ["array-like"]},
    prefer_skip_nested_validation=True,
)
def auc(x, y):
    """Compute Area Under the Curve (AUC) using the trapezoidal rule.

    This is a general function, given points on a curve.  For computing the
    area under the ROC-curve, see :func:`roc_auc_score`.  For an alternative
    way to summarize a precision-recall curve, see
    :func:`average_precision_score`.

    Parameters
    ----------
    x : array-like of shape (n,)
        X coordinates. These must be either monotonic increasing or monotonic
        decreasing.
    y : array-like of shape (n,)
        Y coordinates.

    Returns
    -------
    auc : float
        Area Under the Curve.

    See Also
    --------
    roc_auc_score : Compute the area under the ROC curve.
    average_precision_score : Compute average precision from prediction scores.
    precision_recall_curve : Compute precision-recall pairs for different
        probability thresholds.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn import metrics
    >>> y = np.array([1, 1, 2, 2])
    >>> pred = np.array([0.1, 0.4, 0.35, 0.8])
    >>> fpr, tpr, thresholds = metrics.roc_curve(y, pred, pos_label=2)
    >>> metrics.auc(fpr, tpr)
    0.75
    """
    check_consistent_length(x, y)
    x = column_or_1d(x)
    y = column_or_1d(y)

    if x.shape[0] < 2:
        raise ValueError(
            "At least 2 points are needed to compute area under curve, but x.shape = %s"
            % x.shape
        )

    direction = 1
    dx = np.diff(x)
    if np.any(dx < 0):
        if np.all(dx <= 0):
            direction = -1
        else:
            raise ValueError("x is neither increasing nor decreasing : {}.".format(x))

    area = direction * trapezoid(y, x)
    if isinstance(area, np.memmap):
        # Reductions such as .sum used internally in trapezoid do not return a
        # scalar by default for numpy.memmap instances contrary to
        # regular numpy.ndarray instances.
        area = area.dtype.type(area)
    return area


@validate_params(
    {
        "y_true": ["array-like"],
        "y_score": ["array-like"],
        "average": [StrOptions({"micro", "samples", "weighted", "macro"}), None],
        "pos_label": [Real, str, "boolean"],
        "sample_weight": ["array-like", None],
    },
    prefer_skip_nested_validation=True,
)
def average_precision_score(
    y_true, y_score, *, average="macro", pos_label=1, sample_weight=None
):
    """Compute average precision (AP) from prediction scores.

    AP summarizes a precision-recall curve as the weighted mean of precisions
    achieved at each threshold, with the increase in recall from the previous
    threshold used as the weight:

    .. math::
        \\text{AP} = \\sum_n (R_n - R_{n-1}) P_n

    where :math:`P_n` and :math:`R_n` are the precision and recall at the nth
    threshold [1]_. This implementation is not interpolated and is different
    from computing the area under the precision-recall curve with the
    trapezoidal rule, which uses linear interpolation and can be too
    optimistic.

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

    Parameters
    ----------
    y_true : array-like of shape (n_samples,) or (n_samples, n_classes)
        True binary labels or binary label indicators.

    y_score : array-like of shape (n_samples,) or (n_samples, n_classes)
        Target scores, can either be probability estimates of the positive
        class, confidence values, or non-thresholded measure of decisions
        (as returned by :term:`decision_function` on some classifiers).

    average : {'micro', 'samples', 'weighted', 'macro'} or None, \
            default='macro'
        If ``None``, the scores for each class are returned. Otherwise,
        this determines the type of averaging performed on the data:

        ``'micro'``:
            Calculate metrics globally by considering each element of the label
            indicator matrix as a label.
        ``'macro'``:
            Calculate metrics for each label, and find their unweighted
            mean.  This does not take label imbalance into account.
        ``'weighted'``:
            Calculate metrics for each label, and find their average, weighted
            by support (the number of true instances for each label).
        ``'samples'``:
            Calculate metrics for each instance, and find their average.

        Will be ignored when ``y_true`` is binary.

    pos_label : int, float, bool or str, default=1
        The label of the positive class. Only applied to binary ``y_true``.
        For multilabel-indicator ``y_true``, ``pos_label`` is fixed to 1.

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

    Returns
    -------
    average_precision : float
        Average precision score.

    See Also
    --------
    roc_auc_score : Compute the area under the ROC curve.
    precision_recall_curve : Compute precision-recall pairs for different
        probability thresholds.

    Notes
    -----
    .. versionchanged:: 0.19
      Instead of linearly interpolating between operating points, precisions
      are weighted by the change in recall since the last operating point.

    References
    ----------
    .. [1] `Wikipedia entry for the Average precision
           <https://en.wikipedia.org/w/index.php?title=Information_retrieval&
           oldid=793358396#Average_precision>`_

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.metrics import average_precision_score
    >>> y_true = np.array([0, 0, 1, 1])
    >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
    >>> average_precision_score(y_true, y_scores)
    0.83...
    >>> y_true = np.array([0, 0, 1, 1, 2, 2])
    >>> y_scores = np.array([
    ...     [0.7, 0.2, 0.1],
    ...     [0.4, 0.3, 0.3],
    ...     [0.1, 0.8, 0.1],
    ...     [0.2, 0.3, 0.5],
    ...     [0.4, 0.4, 0.2],
    ...     [0.1, 0.2, 0.7],
    ... ])
    >>> average_precision_score(y_true, y_scores)
    0.77...
    """

    def _binary_uninterpolated_average_precision(
        y_true, y_score, pos_label=1, sample_weight=None
    ):
        precision, recall, _ = precision_recall_curve(
            y_true, y_score, pos_label=pos_label, sample_weight=sample_weight
        )
        # Return the step function integral
        # The following works because the last entry of precision is
        # guaranteed to be 1, as returned by precision_recall_curve
        return -np.sum(np.diff(recall) * np.array(precision)[:-1])

    y_type = type_of_target(y_true, input_name="y_true")

    # Convert to Python primitive type to avoid NumPy type / Python str
    # comparison. See https://github.com/numpy/numpy/issues/6784
    present_labels = np.unique(y_true).tolist()

    if y_type == "binary":
        if len(present_labels) == 2 and pos_label not in present_labels:
            raise ValueError(
                f"pos_label={pos_label} is not a valid label. It should be "
                f"one of {present_labels}"
            )

    elif y_type == "multilabel-indicator" and pos_label != 1:
        raise ValueError(
            "Parameter pos_label is fixed to 1 for multilabel-indicator y_true. "
            "Do not set pos_label or set pos_label to 1."
        )

    elif y_type == "multiclass":
        if pos_label != 1:
            raise ValueError(
                "Parameter pos_label is fixed to 1 for multiclass y_true. "
                "Do not set pos_label or set pos_label to 1."
            )
        y_true = label_binarize(y_true, classes=present_labels)

    average_precision = partial(
        _binary_uninterpolated_average_precision, pos_label=pos_label
    )
    return _average_binary_score(
        average_precision, y_true, y_score, average, sample_weight=sample_weight
    )


@validate_params(
    {
        "y_true": ["array-like"],
        "y_score": ["array-like"],
        "pos_label": [Real, str, "boolean", None],
        "sample_weight": ["array-like", None],
    },
    prefer_skip_nested_validation=True,
)
def det_curve(y_true, y_score, pos_label=None, sample_weight=None):
    """Compute error rates for different probability thresholds.

    .. note::
       This metric is used for evaluation of ranking and error tradeoffs of
       a binary classification task.

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

    .. versionadded:: 0.24

    Parameters
    ----------
    y_true : ndarray of shape (n_samples,)
        True binary labels. If labels are not either {-1, 1} or {0, 1}, then
        pos_label should be explicitly given.

    y_score : ndarray of shape of (n_samples,)
        Target scores, can either be probability estimates of the positive
        class, confidence values, or non-thresholded measure of decisions
        (as returned by "decision_function" on some classifiers).

    pos_label : int, float, bool or str, default=None
        The label of the positive class.
        When ``pos_label=None``, if `y_true` is in {-1, 1} or {0, 1},
        ``pos_label`` is set to 1, otherwise an error will be raised.

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

    Returns
    -------
    fpr : ndarray of shape (n_thresholds,)
        False positive rate (FPR) such that element i is the false positive
        rate of predictions with score >= thresholds[i]. This is occasionally
        referred to as false acceptance probability or fall-out.

    fnr : ndarray of shape (n_thresholds,)
        False negative rate (FNR) such that element i is the false negative
        rate of predictions with score >= thresholds[i]. This is occasionally
        referred to as false rejection or miss rate.

    thresholds : ndarray of shape (n_thresholds,)
        Decreasing score values.

    See Also
    --------
    DetCurveDisplay.from_estimator : Plot DET curve given an estimator and
        some data.
    DetCurveDisplay.from_predictions : Plot DET curve given the true and
        predicted labels.
    DetCurveDisplay : DET curve visualization.
    roc_curve : Compute Receiver operating characteristic (ROC) curve.
    precision_recall_curve : Compute precision-recall curve.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.metrics import det_curve
    >>> y_true = np.array([0, 0, 1, 1])
    >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
    >>> fpr, fnr, thresholds = det_curve(y_true, y_scores)
    >>> fpr
    array([0.5, 0.5, 0. ])
    >>> fnr
    array([0. , 0.5, 0.5])
    >>> thresholds
    array([0.35, 0.4 , 0.8 ])
    """
    fps, tps, thresholds = _binary_clf_curve(
        y_true, y_score, pos_label=pos_label, sample_weight=sample_weight
    )

    if len(np.unique(y_true)) != 2:
        raise ValueError(
            "Only one class present in y_true. Detection error "
            "tradeoff curve is not defined in that case."
        )

    fns = tps[-1] - tps
    p_count = tps[-1]
    n_count = fps[-1]

    # start with false positives zero
    first_ind = (
        fps.searchsorted(fps[0], side="right") - 1
        if fps.searchsorted(fps[0], side="right") > 0
        else None
    )
    # stop with false negatives zero
    last_ind = tps.searchsorted(tps[-1]) + 1
    sl = slice(first_ind, last_ind)

    # reverse the output such that list of false positives is decreasing
    return (fps[sl][::-1] / n_count, fns[sl][::-1] / p_count, thresholds[sl][::-1])


def _binary_roc_auc_score(y_true, y_score, sample_weight=None, max_fpr=None):
    """Binary roc auc score."""
    if len(np.unique(y_true)) != 2:
        raise ValueError(
            "Only one class present in y_true. ROC AUC score "
            "is not defined in that case."
        )

    fpr, tpr, _ = roc_curve(y_true, y_score, sample_weight=sample_weight)
    if max_fpr is None or max_fpr == 1:
        return auc(fpr, tpr)
    if max_fpr <= 0 or max_fpr > 1:
        raise ValueError("Expected max_fpr in range (0, 1], got: %r" % max_fpr)

    # Add a single point at max_fpr by linear interpolation
    stop = np.searchsorted(fpr, max_fpr, "right")
    x_interp = [fpr[stop - 1], fpr[stop]]
    y_interp = [tpr[stop - 1], tpr[stop]]
    tpr = np.append(tpr[:stop], np.interp(max_fpr, x_interp, y_interp))
    fpr = np.append(fpr[:stop], max_fpr)
    partial_auc = auc(fpr, tpr)

    # McClish correction: standardize result to be 0.5 if non-discriminant
    # and 1 if maximal
    min_area = 0.5 * max_fpr**2
    max_area = max_fpr
    return 0.5 * (1 + (partial_auc - min_area) / (max_area - min_area))


@validate_params(
    {
        "y_true": ["array-like"],
        "y_score": ["array-like"],
        "average": [StrOptions({"micro", "macro", "samples", "weighted"}), None],
        "sample_weight": ["array-like", None],
        "max_fpr": [Interval(Real, 0.0, 1, closed="right"), None],
        "multi_class": [StrOptions({"raise", "ovr", "ovo"})],
        "labels": ["array-like", None],
    },
    prefer_skip_nested_validation=True,
)
def roc_auc_score(
    y_true,
    y_score,
    *,
    average="macro",
    sample_weight=None,
    max_fpr=None,
    multi_class="raise",
    labels=None,
):
    """Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) \
    from prediction scores.

    Note: this implementation can be used with binary, multiclass and
    multilabel classification, but some restrictions apply (see Parameters).

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

    Parameters
    ----------
    y_true : array-like of shape (n_samples,) or (n_samples, n_classes)
        True labels or binary label indicators. The binary and multiclass cases
        expect labels with shape (n_samples,) while the multilabel case expects
        binary label indicators with shape (n_samples, n_classes).

    y_score : array-like of shape (n_samples,) or (n_samples, n_classes)
        Target scores.

        * In the binary case, it corresponds to an array of shape
          `(n_samples,)`. Both probability estimates and non-thresholded
          decision values can be provided. The probability estimates correspond
          to the **probability of the class with the greater label**,
          i.e. `estimator.classes_[1]` and thus
          `estimator.predict_proba(X, y)[:, 1]`. The decision values
          corresponds to the output of `estimator.decision_function(X, y)`.
          See more information in the :ref:`User guide <roc_auc_binary>`;
        * In the multiclass case, it corresponds to an array of shape
          `(n_samples, n_classes)` of probability estimates provided by the
          `predict_proba` method. The probability estimates **must**
          sum to 1 across the possible classes. In addition, the order of the
          class scores must correspond to the order of ``labels``,
          if provided, or else to the numerical or lexicographical order of
          the labels in ``y_true``. See more information in the
          :ref:`User guide <roc_auc_multiclass>`;
        * In the multilabel case, it corresponds to an array of shape
          `(n_samples, n_classes)`. Probability estimates are provided by the
          `predict_proba` method and the non-thresholded decision values by
          the `decision_function` method. The probability estimates correspond
          to the **probability of the class with the greater label for each
          output** of the classifier. See more information in the
          :ref:`User guide <roc_auc_multilabel>`.

    average : {'micro', 'macro', 'samples', 'weighted'} or None, \
            default='macro'
        If ``None``, the scores for each class are returned.
        Otherwise, this determines the type of averaging performed on the data.
        Note: multiclass ROC AUC currently only handles the 'macro' and
        'weighted' averages. For multiclass targets, `average=None` is only
        implemented for `multi_class='ovr'` and `average='micro'` is only
        implemented for `multi_class='ovr'`.

        ``'micro'``:
            Calculate metrics globally by considering each element of the label
            indicator matrix as a label.
        ``'macro'``:
            Calculate metrics for each label, and find their unweighted
            mean.  This does not take label imbalance into account.
        ``'weighted'``:
            Calculate metrics for each label, and find their average, weighted
            by support (the number of true instances for each label).
        ``'samples'``:
            Calculate metrics for each instance, and find their average.

        Will be ignored when ``y_true`` is binary.

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

    max_fpr : float > 0 and <= 1, default=None
        If not ``None``, the standardized partial AUC [2]_ over the range
        [0, max_fpr] is returned. For the multiclass case, ``max_fpr``,
        should be either equal to ``None`` or ``1.0`` as AUC ROC partial
        computation currently is not supported for multiclass.

    multi_class : {'raise', 'ovr', 'ovo'}, default='raise'
        Only used for multiclass targets. Determines the type of configuration
        to use. The default value raises an error, so either
        ``'ovr'`` or ``'ovo'`` must be passed explicitly.

        ``'ovr'``:
            Stands for One-vs-rest. Computes the AUC of each class
            against the rest [3]_ [4]_. This
            treats the multiclass case in the same way as the multilabel case.
            Sensitive to class imbalance even when ``average == 'macro'``,
            because class imbalance affects the composition of each of the
            'rest' groupings.
        ``'ovo'``:
            Stands for One-vs-one. Computes the average AUC of all
            possible pairwise combinations of classes [5]_.
            Insensitive to class imbalance when
            ``average == 'macro'``.

    labels : array-like of shape (n_classes,), default=None
        Only used for multiclass targets. List of labels that index the
        classes in ``y_score``. If ``None``, the numerical or lexicographical
        order of the labels in ``y_true`` is used.

    Returns
    -------
    auc : float
        Area Under the Curve score.

    See Also
    --------
    average_precision_score : Area under the precision-recall curve.
    roc_curve : Compute Receiver operating characteristic (ROC) curve.
    RocCurveDisplay.from_estimator : Plot Receiver Operating Characteristic
        (ROC) curve given an estimator and some data.
    RocCurveDisplay.from_predictions : Plot Receiver Operating Characteristic
        (ROC) curve given the true and predicted values.

    Notes
    -----
    The Gini Coefficient is a summary measure of the ranking ability of binary
    classifiers. It is expressed using the area under of the ROC as follows:

    G = 2 * AUC - 1

    Where G is the Gini coefficient and AUC is the ROC-AUC score. This normalisation
    will ensure that random guessing will yield a score of 0 in expectation, and it is
    upper bounded by 1.

    References
    ----------
    .. [1] `Wikipedia entry for the Receiver operating characteristic
            <https://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_

    .. [2] `Analyzing a portion of the ROC curve. McClish, 1989
            <https://www.ncbi.nlm.nih.gov/pubmed/2668680>`_

    .. [3] Provost, F., Domingos, P. (2000). Well-trained PETs: Improving
           probability estimation trees (Section 6.2), CeDER Working Paper
           #IS-00-04, Stern School of Business, New York University.

    .. [4] `Fawcett, T. (2006). An introduction to ROC analysis. Pattern
            Recognition Letters, 27(8), 861-874.
            <https://www.sciencedirect.com/science/article/pii/S016786550500303X>`_

    .. [5] `Hand, D.J., Till, R.J. (2001). A Simple Generalisation of the Area
            Under the ROC Curve for Multiple Class Classification Problems.
            Machine Learning, 45(2), 171-186.
            <http://link.springer.com/article/10.1023/A:1010920819831>`_
    .. [6] `Wikipedia entry for the Gini coefficient
            <https://en.wikipedia.org/wiki/Gini_coefficient>`_

    Examples
    --------
    Binary case:

    >>> from sklearn.datasets import load_breast_cancer
    >>> from sklearn.linear_model import LogisticRegression
    >>> from sklearn.metrics import roc_auc_score
    >>> X, y = load_breast_cancer(return_X_y=True)
    >>> clf = LogisticRegression(solver="liblinear", random_state=0).fit(X, y)
    >>> roc_auc_score(y, clf.predict_proba(X)[:, 1])
    0.99...
    >>> roc_auc_score(y, clf.decision_function(X))
    0.99...

    Multiclass case:

    >>> from sklearn.datasets import load_iris
    >>> X, y = load_iris(return_X_y=True)
    >>> clf = LogisticRegression(solver="liblinear").fit(X, y)
    >>> roc_auc_score(y, clf.predict_proba(X), multi_class='ovr')
    0.99...

    Multilabel case:

    >>> import numpy as np
    >>> from sklearn.datasets import make_multilabel_classification
    >>> from sklearn.multioutput import MultiOutputClassifier
    >>> X, y = make_multilabel_classification(random_state=0)
    >>> clf = MultiOutputClassifier(clf).fit(X, y)
    >>> # get a list of n_output containing probability arrays of shape
    >>> # (n_samples, n_classes)
    >>> y_pred = clf.predict_proba(X)
    >>> # extract the positive columns for each output
    >>> y_pred = np.transpose([pred[:, 1] for pred in y_pred])
    >>> roc_auc_score(y, y_pred, average=None)
    array([0.82..., 0.86..., 0.94..., 0.85... , 0.94...])
    >>> from sklearn.linear_model import RidgeClassifierCV
    >>> clf = RidgeClassifierCV().fit(X, y)
    >>> roc_auc_score(y, clf.decision_function(X), average=None)
    array([0.81..., 0.84... , 0.93..., 0.87..., 0.94...])
    """

    y_type = type_of_target(y_true, input_name="y_true")
    y_true = check_array(y_true, ensure_2d=False, dtype=None)
    y_score = check_array(y_score, ensure_2d=False)

    if y_type == "multiclass" or (
        y_type == "binary" and y_score.ndim == 2 and y_score.shape[1] > 2
    ):
        # do not support partial ROC computation for multiclass
        if max_fpr is not None and max_fpr != 1.0:
            raise ValueError(
                "Partial AUC computation not available in "
                "multiclass setting, 'max_fpr' must be"
                " set to `None`, received `max_fpr={0}` "
                "instead".format(max_fpr)
            )
        if multi_class == "raise":
            raise ValueError("multi_class must be in ('ovo', 'ovr')")
        return _multiclass_roc_auc_score(
            y_true, y_score, labels, multi_class, average, sample_weight
        )
    elif y_type == "binary":
        labels = np.unique(y_true)
        y_true = label_binarize(y_true, classes=labels)[:, 0]
        return _average_binary_score(
            partial(_binary_roc_auc_score, max_fpr=max_fpr),
            y_true,
            y_score,
            average,
            sample_weight=sample_weight,
        )
    else:  # multilabel-indicator
        return _average_binary_score(
            partial(_binary_roc_auc_score, max_fpr=max_fpr),
            y_true,
            y_score,
            average,
            sample_weight=sample_weight,
        )


def _multiclass_roc_auc_score(
    y_true, y_score, labels, multi_class, average, sample_weight
):
    """Multiclass roc auc score.

    Parameters
    ----------
    y_true : array-like of shape (n_samples,)
        True multiclass labels.

    y_score : array-like of shape (n_samples, n_classes)
        Target scores corresponding to probability estimates of a sample
        belonging to a particular class

    labels : array-like of shape (n_classes,) or None
        List of labels to index ``y_score`` used for multiclass. If ``None``,
        the lexical order of ``y_true`` is used to index ``y_score``.

    multi_class : {'ovr', 'ovo'}
        Determines the type of multiclass configuration to use.
        ``'ovr'``:
            Calculate metrics for the multiclass case using the one-vs-rest
            approach.
        ``'ovo'``:
            Calculate metrics for the multiclass case using the one-vs-one
            approach.

    average : {'micro', 'macro', 'weighted'}
        Determines the type of averaging performed on the pairwise binary
        metric scores
        ``'micro'``:
            Calculate metrics for the binarized-raveled classes. Only supported
            for `multi_class='ovr'`.

        .. versionadded:: 1.2

        ``'macro'``:
            Calculate metrics for each label, and find their unweighted
            mean. This does not take label imbalance into account. Classes
            are assumed to be uniformly distributed.
        ``'weighted'``:
            Calculate metrics for each label, taking into account the
            prevalence of the classes.

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

    """
    # validation of the input y_score
    if not np.allclose(1, y_score.sum(axis=1)):
        raise ValueError(
            "Target scores need to be probabilities for multiclass "
            "roc_auc, i.e. they should sum up to 1.0 over classes"
        )

    # validation for multiclass parameter specifications
    average_options = ("macro", "weighted", None)
    if multi_class == "ovr":
        average_options = ("micro",) + average_options
    if average not in average_options:
        raise ValueError(
            "average must be one of {0} for multiclass problems".format(average_options)
        )

    multiclass_options = ("ovo", "ovr")
    if multi_class not in multiclass_options:
        raise ValueError(
            "multi_class='{0}' is not supported "
            "for multiclass ROC AUC, multi_class must be "
            "in {1}".format(multi_class, multiclass_options)
        )

    if average is None and multi_class == "ovo":
        raise NotImplementedError(
            "average=None is not implemented for multi_class='ovo'."
        )

    if labels is not None:
        labels = column_or_1d(labels)
        classes = _unique(labels)
        if len(classes) != len(labels):
            raise ValueError("Parameter 'labels' must be unique")
        if not np.array_equal(classes, labels):
            raise ValueError("Parameter 'labels' must be ordered")
        if len(classes) != y_score.shape[1]:
            raise ValueError(
                "Number of given labels, {0}, not equal to the number "
                "of columns in 'y_score', {1}".format(len(classes), y_score.shape[1])
            )
        if len(np.setdiff1d(y_true, classes)):
            raise ValueError("'y_true' contains labels not in parameter 'labels'")
    else:
        classes = _unique(y_true)
        if len(classes) != y_score.shape[1]:
            raise ValueError(
                "Number of classes in y_true not equal to the number of "
                "columns in 'y_score'"
            )

    if multi_class == "ovo":
        if sample_weight is not None:
            raise ValueError(
                "sample_weight is not supported "
                "for multiclass one-vs-one ROC AUC, "
                "'sample_weight' must be None in this case."
            )
        y_true_encoded = _encode(y_true, uniques=classes)
        # Hand & Till (2001) implementation (ovo)
        return _average_multiclass_ovo_score(
            _binary_roc_auc_score, y_true_encoded, y_score, average=average
        )
    else:
        # ovr is same as multi-label
        y_true_multilabel = label_binarize(y_true, classes=classes)
        return _average_binary_score(
            _binary_roc_auc_score,
            y_true_multilabel,
            y_score,
            average,
            sample_weight=sample_weight,
        )


def _binary_clf_curve(y_true, y_score, pos_label=None, sample_weight=None):
    """Calculate true and false positives per binary classification threshold.

    Parameters
    ----------
    y_true : ndarray of shape (n_samples,)
        True targets of binary classification.

    y_score : ndarray of shape (n_samples,)
        Estimated probabilities or output of a decision function.

    pos_label : int, float, bool or str, default=None
        The label of the positive class.

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

    Returns
    -------
    fps : ndarray of shape (n_thresholds,)
        A count of false positives, at index i being the number of negative
        samples assigned a score >= thresholds[i]. The total number of
        negative samples is equal to fps[-1] (thus true negatives are given by
        fps[-1] - fps).

    tps : ndarray of shape (n_thresholds,)
        An increasing count of true positives, at index i being the number
        of positive samples assigned a score >= thresholds[i]. The total
        number of positive samples is equal to tps[-1] (thus false negatives
        are given by tps[-1] - tps).

    thresholds : ndarray of shape (n_thresholds,)
        Decreasing score values.
    """
    # Check to make sure y_true is valid
    y_type = type_of_target(y_true, input_name="y_true")
    if not (y_type == "binary" or (y_type == "multiclass" and pos_label is not None)):
        raise ValueError("{0} format is not supported".format(y_type))

    check_consistent_length(y_true, y_score, sample_weight)
    y_true = column_or_1d(y_true)
    y_score = column_or_1d(y_score)
    assert_all_finite(y_true)
    assert_all_finite(y_score)

    # Filter out zero-weighted samples, as they should not impact the result
    if sample_weight is not None:
        sample_weight = column_or_1d(sample_weight)
        sample_weight = _check_sample_weight(sample_weight, y_true)
        nonzero_weight_mask = sample_weight != 0
        y_true = y_true[nonzero_weight_mask]
        y_score = y_score[nonzero_weight_mask]
        sample_weight = sample_weight[nonzero_weight_mask]

    pos_label = _check_pos_label_consistency(pos_label, y_true)

    # make y_true a boolean vector
    y_true = y_true == pos_label

    # sort scores and corresponding truth values
    desc_score_indices = np.argsort(y_score, kind="mergesort")[::-1]
    y_score = y_score[desc_score_indices]
    y_true = y_true[desc_score_indices]
    if sample_weight is not None:
        weight = sample_weight[desc_score_indices]
    else:
        weight = 1.0

    # y_score typically has many tied values. Here we extract
    # the indices associated with the distinct values. We also
    # concatenate a value for the end of the curve.
    distinct_value_indices = np.where(np.diff(y_score))[0]
    threshold_idxs = np.r_[distinct_value_indices, y_true.size - 1]

    # accumulate the true positives with decreasing threshold
    tps = stable_cumsum(y_true * weight)[threshold_idxs]
    if sample_weight is not None:
        # express fps as a cumsum to ensure fps is increasing even in
        # the presence of floating point errors
        fps = stable_cumsum((1 - y_true) * weight)[threshold_idxs]
    else:
        fps = 1 + threshold_idxs - tps
    return fps, tps, y_score[threshold_idxs]


@validate_params(
    {
        "y_true": ["array-like"],
        "probas_pred": ["array-like"],
        "pos_label": [Real, str, "boolean", None],
        "sample_weight": ["array-like", None],
        "drop_intermediate": ["boolean"],
    },
    prefer_skip_nested_validation=True,
)
def precision_recall_curve(
    y_true, probas_pred, *, pos_label=None, sample_weight=None, drop_intermediate=False
):
    """Compute precision-recall pairs for different probability thresholds.

    Note: this implementation is restricted to the binary classification task.

    The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of
    true positives and ``fp`` the number of false positives. The precision is
    intuitively the ability of the classifier not to label as positive a sample
    that is negative.

    The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of
    true positives and ``fn`` the number of false negatives. The recall is
    intuitively the ability of the classifier to find all the positive samples.

    The last precision and recall values are 1. and 0. respectively and do not
    have a corresponding threshold. This ensures that the graph starts on the
    y axis.

    The first precision and recall values are precision=class balance and recall=1.0
    which corresponds to a classifier that always predicts the positive class.

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

    Parameters
    ----------
    y_true : array-like of shape (n_samples,)
        True binary labels. If labels are not either {-1, 1} or {0, 1}, then
        pos_label should be explicitly given.

    probas_pred : array-like of shape (n_samples,)
        Target scores, can either be probability estimates of the positive
        class, or non-thresholded measure of decisions (as returned by
        `decision_function` on some classifiers).

    pos_label : int, float, bool or str, default=None
        The label of the positive class.
        When ``pos_label=None``, if y_true is in {-1, 1} or {0, 1},
        ``pos_label`` is set to 1, otherwise an error will be raised.

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

    drop_intermediate : bool, default=False
        Whether to drop some suboptimal thresholds which would not appear
        on a plotted precision-recall curve. This is useful in order to create
        lighter precision-recall curves.

        .. versionadded:: 1.3

    Returns
    -------
    precision : ndarray of shape (n_thresholds + 1,)
        Precision values such that element i is the precision of
        predictions with score >= thresholds[i] and the last element is 1.

    recall : ndarray of shape (n_thresholds + 1,)
        Decreasing recall values such that element i is the recall of
        predictions with score >= thresholds[i] and the last element is 0.

    thresholds : ndarray of shape (n_thresholds,)
        Increasing thresholds on the decision function used to compute
        precision and recall where `n_thresholds = len(np.unique(probas_pred))`.

    See Also
    --------
    PrecisionRecallDisplay.from_estimator : Plot Precision Recall Curve given
        a binary classifier.
    PrecisionRecallDisplay.from_predictions : Plot Precision Recall Curve
        using predictions from a binary classifier.
    average_precision_score : Compute average precision from prediction scores.
    det_curve: Compute error rates for different probability thresholds.
    roc_curve : Compute Receiver operating characteristic (ROC) curve.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.metrics import precision_recall_curve
    >>> y_true = np.array([0, 0, 1, 1])
    >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
    >>> precision, recall, thresholds = precision_recall_curve(
    ...     y_true, y_scores)
    >>> precision
    array([0.5       , 0.66666667, 0.5       , 1.        , 1.        ])
    >>> recall
    array([1. , 1. , 0.5, 0.5, 0. ])
    >>> thresholds
    array([0.1 , 0.35, 0.4 , 0.8 ])
    """
    fps, tps, thresholds = _binary_clf_curve(
        y_true, probas_pred, pos_label=pos_label, sample_weight=sample_weight
    )

    if drop_intermediate and len(fps) > 2:
        # Drop thresholds corresponding to points where true positives (tps)
        # do not change from the previous or subsequent point. This will keep
        # only the first and last point for each tps value. All points
        # with the same tps value have the same recall and thus x coordinate.
        # They appear as a vertical line on the plot.
        optimal_idxs = np.where(
            np.concatenate(
                [[True], np.logical_or(np.diff(tps[:-1]), np.diff(tps[1:])), [True]]
            )
        )[0]
        fps = fps[optimal_idxs]
        tps = tps[optimal_idxs]
        thresholds = thresholds[optimal_idxs]

    ps = tps + fps
    # Initialize the result array with zeros to make sure that precision[ps == 0]
    # does not contain uninitialized values.
    precision = np.zeros_like(tps)
    np.divide(tps, ps, out=precision, where=(ps != 0))

    # When no positive label in y_true, recall is set to 1 for all thresholds
    # tps[-1] == 0 <=> y_true == all negative labels
    if tps[-1] == 0:
        warnings.warn(
            "No positive class found in y_true, "
            "recall is set to one for all thresholds."
        )
        recall = np.ones_like(tps)
    else:
        recall = tps / tps[-1]

    # reverse the outputs so recall is decreasing
    sl = slice(None, None, -1)
    return np.hstack((precision[sl], 1)), np.hstack((recall[sl], 0)), thresholds[sl]


@validate_params(
    {
        "y_true": ["array-like"],
        "y_score": ["array-like"],
        "pos_label": [Real, str, "boolean", None],
        "sample_weight": ["array-like", None],
        "drop_intermediate": ["boolean"],
    },
    prefer_skip_nested_validation=True,
)
def roc_curve(
    y_true, y_score, *, pos_label=None, sample_weight=None, drop_intermediate=True
):
    """Compute Receiver operating characteristic (ROC).

    Note: this implementation is restricted to the binary classification task.

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

    Parameters
    ----------
    y_true : array-like of shape (n_samples,)
        True binary labels. If labels are not either {-1, 1} or {0, 1}, then
        pos_label should be explicitly given.

    y_score : array-like of shape (n_samples,)
        Target scores, can either be probability estimates of the positive
        class, confidence values, or non-thresholded measure of decisions
        (as returned by "decision_function" on some classifiers).

    pos_label : int, float, bool or str, default=None
        The label of the positive class.
        When ``pos_label=None``, if `y_true` is in {-1, 1} or {0, 1},
        ``pos_label`` is set to 1, otherwise an error will be raised.

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

    drop_intermediate : bool, default=True
        Whether to drop some suboptimal thresholds which would not appear
        on a plotted ROC curve. This is useful in order to create lighter
        ROC curves.

        .. versionadded:: 0.17
           parameter *drop_intermediate*.

    Returns
    -------
    fpr : ndarray of shape (>2,)
        Increasing false positive rates such that element i is the false
        positive rate of predictions with score >= `thresholds[i]`.

    tpr : ndarray of shape (>2,)
        Increasing true positive rates such that element `i` is the true
        positive rate of predictions with score >= `thresholds[i]`.

    thresholds : ndarray of shape (n_thresholds,)
        Decreasing thresholds on the decision function used to compute
        fpr and tpr. `thresholds[0]` represents no instances being predicted
        and is arbitrarily set to `np.inf`.

    See Also
    --------
    RocCurveDisplay.from_estimator : Plot Receiver Operating Characteristic
        (ROC) curve given an estimator and some data.
    RocCurveDisplay.from_predictions : Plot Receiver Operating Characteristic
        (ROC) curve given the true and predicted values.
    det_curve: Compute error rates for different probability thresholds.
    roc_auc_score : Compute the area under the ROC curve.

    Notes
    -----
    Since the thresholds are sorted from low to high values, they
    are reversed upon returning them to ensure they correspond to both ``fpr``
    and ``tpr``, which are sorted in reversed order during their calculation.

    An arbitrary threshold is added for the case `tpr=0` and `fpr=0` to
    ensure that the curve starts at `(0, 0)`. This threshold corresponds to the
    `np.inf`.

    References
    ----------
    .. [1] `Wikipedia entry for the Receiver operating characteristic
            <https://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_

    .. [2] Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition
           Letters, 2006, 27(8):861-874.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn import metrics
    >>> y = np.array([1, 1, 2, 2])
    >>> scores = np.array([0.1, 0.4, 0.35, 0.8])
    >>> fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=2)
    >>> fpr
    array([0. , 0. , 0.5, 0.5, 1. ])
    >>> tpr
    array([0. , 0.5, 0.5, 1. , 1. ])
    >>> thresholds
    array([ inf, 0.8 , 0.4 , 0.35, 0.1 ])
    """
    fps, tps, thresholds = _binary_clf_curve(
        y_true, y_score, pos_label=pos_label, sample_weight=sample_weight
    )

    # Attempt to drop thresholds corresponding to points in between and
    # collinear with other points. These are always suboptimal and do not
    # appear on a plotted ROC curve (and thus do not affect the AUC).
    # Here np.diff(_, 2) is used as a "second derivative" to tell if there
    # is a corner at the point. Both fps and tps must be tested to handle
    # thresholds with multiple data points (which are combined in
    # _binary_clf_curve). This keeps all cases where the point should be kept,
    # but does not drop more complicated cases like fps = [1, 3, 7],
    # tps = [1, 2, 4]; there is no harm in keeping too many thresholds.
    if drop_intermediate and len(fps) > 2:
        optimal_idxs = np.where(
            np.r_[True, np.logical_or(np.diff(fps, 2), np.diff(tps, 2)), True]
        )[0]
        fps = fps[optimal_idxs]
        tps = tps[optimal_idxs]
        thresholds = thresholds[optimal_idxs]

    # Add an extra threshold position
    # to make sure that the curve starts at (0, 0)
    tps = np.r_[0, tps]
    fps = np.r_[0, fps]
    # get dtype of `y_score` even if it is an array-like
    thresholds = np.r_[np.inf, thresholds]

    if fps[-1] <= 0:
        warnings.warn(
            "No negative samples in y_true, false positive value should be meaningless",
            UndefinedMetricWarning,
        )
        fpr = np.repeat(np.nan, fps.shape)
    else:
        fpr = fps / fps[-1]

    if tps[-1] <= 0:
        warnings.warn(
            "No positive samples in y_true, true positive value should be meaningless",
            UndefinedMetricWarning,
        )
        tpr = np.repeat(np.nan, tps.shape)
    else:
        tpr = tps / tps[-1]

    return fpr, tpr, thresholds


@validate_params(
    {
        "y_true": ["array-like", "sparse matrix"],
        "y_score": ["array-like"],
        "sample_weight": ["array-like", None],
    },
    prefer_skip_nested_validation=True,
)
def label_ranking_average_precision_score(y_true, y_score, *, sample_weight=None):
    """Compute ranking-based average precision.

    Label ranking average precision (LRAP) is the average over each ground
    truth label assigned to each sample, of the ratio of true vs. total
    labels with lower score.

    This metric is used in multilabel ranking problem, where the goal
    is to give better rank to the labels associated to each sample.

    The obtained score is always strictly greater than 0 and
    the best value is 1.

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

    Parameters
    ----------
    y_true : {array-like, sparse matrix} of shape (n_samples, n_labels)
        True binary labels in binary indicator format.

    y_score : array-like of shape (n_samples, n_labels)
        Target scores, can either be probability estimates of the positive
        class, confidence values, or non-thresholded measure of decisions
        (as returned by "decision_function" on some classifiers).

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

        .. versionadded:: 0.20

    Returns
    -------
    score : float
        Ranking-based average precision score.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.metrics import label_ranking_average_precision_score
    >>> y_true = np.array([[1, 0, 0], [0, 0, 1]])
    >>> y_score = np.array([[0.75, 0.5, 1], [1, 0.2, 0.1]])
    >>> label_ranking_average_precision_score(y_true, y_score)
    0.416...
    """
    check_consistent_length(y_true, y_score, sample_weight)
    y_true = check_array(y_true, ensure_2d=False, accept_sparse="csr")
    y_score = check_array(y_score, ensure_2d=False)

    if y_true.shape != y_score.shape:
        raise ValueError("y_true and y_score have different shape")

    # Handle badly formatted array and the degenerate case with one label
    y_type = type_of_target(y_true, input_name="y_true")
    if y_type != "multilabel-indicator" and not (
        y_type == "binary" and y_true.ndim == 2
    ):
        raise ValueError("{0} format is not supported".format(y_type))

    if not issparse(y_true):
        y_true = csr_matrix(y_true)

    y_score = -y_score

    n_samples, n_labels = y_true.shape

    out = 0.0
    for i, (start, stop) in enumerate(zip(y_true.indptr, y_true.indptr[1:])):
        relevant = y_true.indices[start:stop]

        if relevant.size == 0 or relevant.size == n_labels:
            # If all labels are relevant or unrelevant, the score is also
            # equal to 1. The label ranking has no meaning.
            aux = 1.0
        else:
            scores_i = y_score[i]
            rank = rankdata(scores_i, "max")[relevant]
            L = rankdata(scores_i[relevant], "max")
            aux = (L / rank).mean()

        if sample_weight is not None:
            aux = aux * sample_weight[i]
        out += aux

    if sample_weight is None:
        out /= n_samples
    else:
        out /= np.sum(sample_weight)

    return out


@validate_params(
    {
        "y_true": ["array-like"],
        "y_score": ["array-like"],
        "sample_weight": ["array-like", None],
    },
    prefer_skip_nested_validation=True,
)
def coverage_error(y_true, y_score, *, sample_weight=None):
    """Coverage error measure.

    Compute how far we need to go through the ranked scores to cover all
    true labels. The best value is equal to the average number
    of labels in ``y_true`` per sample.

    Ties in ``y_scores`` are broken by giving maximal rank that would have
    been assigned to all tied values.

    Note: Our implementation's score is 1 greater than the one given in
    Tsoumakas et al., 2010. This extends it to handle the degenerate case
    in which an instance has 0 true labels.

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

    Parameters
    ----------
    y_true : array-like of shape (n_samples, n_labels)
        True binary labels in binary indicator format.

    y_score : array-like of shape (n_samples, n_labels)
        Target scores, can either be probability estimates of the positive
        class, confidence values, or non-thresholded measure of decisions
        (as returned by "decision_function" on some classifiers).

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

    Returns
    -------
    coverage_error : float
        The coverage error.

    References
    ----------
    .. [1] Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010).
           Mining multi-label data. In Data mining and knowledge discovery
           handbook (pp. 667-685). Springer US.

    Examples
    --------
    >>> from sklearn.metrics import coverage_error
    >>> y_true = [[1, 0, 0], [0, 1, 1]]
    >>> y_score = [[1, 0, 0], [0, 1, 1]]
    >>> coverage_error(y_true, y_score)
    1.5
    """
    y_true = check_array(y_true, ensure_2d=True)
    y_score = check_array(y_score, ensure_2d=True)
    check_consistent_length(y_true, y_score, sample_weight)

    y_type = type_of_target(y_true, input_name="y_true")
    if y_type != "multilabel-indicator":
        raise ValueError("{0} format is not supported".format(y_type))

    if y_true.shape != y_score.shape:
        raise ValueError("y_true and y_score have different shape")

    y_score_mask = np.ma.masked_array(y_score, mask=np.logical_not(y_true))
    y_min_relevant = y_score_mask.min(axis=1).reshape((-1, 1))
    coverage = (y_score >= y_min_relevant).sum(axis=1)
    coverage = coverage.filled(0)

    return np.average(coverage, weights=sample_weight)


@validate_params(
    {
        "y_true": ["array-like", "sparse matrix"],
        "y_score": ["array-like"],
        "sample_weight": ["array-like", None],
    },
    prefer_skip_nested_validation=True,
)
def label_ranking_loss(y_true, y_score, *, sample_weight=None):
    """Compute Ranking loss measure.

    Compute the average number of label pairs that are incorrectly ordered
    given y_score weighted by the size of the label set and the number of
    labels not in the label set.

    This is similar to the error set size, but weighted by the number of
    relevant and irrelevant labels. The best performance is achieved with
    a ranking loss of zero.

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

    .. versionadded:: 0.17
       A function *label_ranking_loss*

    Parameters
    ----------
    y_true : {array-like, sparse matrix} of shape (n_samples, n_labels)
        True binary labels in binary indicator format.

    y_score : array-like of shape (n_samples, n_labels)
        Target scores, can either be probability estimates of the positive
        class, confidence values, or non-thresholded measure of decisions
        (as returned by "decision_function" on some classifiers).

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

    Returns
    -------
    loss : float
        Average number of label pairs that are incorrectly ordered given
        y_score weighted by the size of the label set and the number of labels not
        in the label set.

    References
    ----------
    .. [1] Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010).
           Mining multi-label data. In Data mining and knowledge discovery
           handbook (pp. 667-685). Springer US.

    Examples
    --------
    >>> from sklearn.metrics import label_ranking_loss
    >>> y_true = [[1, 0, 0], [0, 0, 1]]
    >>> y_score = [[0.75, 0.5, 1], [1, 0.2, 0.1]]
    >>> label_ranking_loss(y_true, y_score)
    0.75...
    """
    y_true = check_array(y_true, ensure_2d=False, accept_sparse="csr")
    y_score = check_array(y_score, ensure_2d=False)
    check_consistent_length(y_true, y_score, sample_weight)

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

    if y_true.shape != y_score.shape:
        raise ValueError("y_true and y_score have different shape")

    n_samples, n_labels = y_true.shape

    y_true = csr_matrix(y_true)

    loss = np.zeros(n_samples)
    for i, (start, stop) in enumerate(zip(y_true.indptr, y_true.indptr[1:])):
        # Sort and bin the label scores
        unique_scores, unique_inverse = np.unique(y_score[i], return_inverse=True)
        true_at_reversed_rank = np.bincount(
            unique_inverse[y_true.indices[start:stop]], minlength=len(unique_scores)
        )
        all_at_reversed_rank = np.bincount(unique_inverse, minlength=len(unique_scores))
        false_at_reversed_rank = all_at_reversed_rank - true_at_reversed_rank

        # if the scores are ordered, it's possible to count the number of
        # incorrectly ordered paires in linear time by cumulatively counting
        # how many false labels of a given score have a score higher than the
        # accumulated true labels with lower score.
        loss[i] = np.dot(true_at_reversed_rank.cumsum(), false_at_reversed_rank)

    n_positives = count_nonzero(y_true, axis=1)
    with np.errstate(divide="ignore", invalid="ignore"):
        loss /= (n_labels - n_positives) * n_positives

    # When there is no positive or no negative labels, those values should
    # be consider as correct, i.e. the ranking doesn't matter.
    loss[np.logical_or(n_positives == 0, n_positives == n_labels)] = 0.0

    return np.average(loss, weights=sample_weight)


def _dcg_sample_scores(y_true, y_score, k=None, log_base=2, ignore_ties=False):
    """Compute Discounted Cumulative Gain.

    Sum the true scores ranked in the order induced by the predicted scores,
    after applying a logarithmic discount.

    This ranking metric yields a high value if true labels are ranked high by
    ``y_score``.

    Parameters
    ----------
    y_true : ndarray of shape (n_samples, n_labels)
        True targets of multilabel classification, or true scores of entities
        to be ranked.

    y_score : ndarray of shape (n_samples, n_labels)
        Target scores, can either be probability estimates, confidence values,
        or non-thresholded measure of decisions (as returned by
        "decision_function" on some classifiers).

    k : int, default=None
        Only consider the highest k scores in the ranking. If `None`, use all
        outputs.

    log_base : float, default=2
        Base of the logarithm used for the discount. A low value means a
        sharper discount (top results are more important).

    ignore_ties : bool, default=False
        Assume that there are no ties in y_score (which is likely to be the
        case if y_score is continuous) for efficiency gains.

    Returns
    -------
    discounted_cumulative_gain : ndarray of shape (n_samples,)
        The DCG score for each sample.

    See Also
    --------
    ndcg_score : The Discounted Cumulative Gain divided by the Ideal Discounted
        Cumulative Gain (the DCG obtained for a perfect ranking), in order to
        have a score between 0 and 1.
    """
    discount = 1 / (np.log(np.arange(y_true.shape[1]) + 2) / np.log(log_base))
    if k is not None:
        discount[k:] = 0
    if ignore_ties:
        ranking = np.argsort(y_score)[:, ::-1]
        ranked = y_true[np.arange(ranking.shape[0])[:, np.newaxis], ranking]
        cumulative_gains = discount.dot(ranked.T)
    else:
        discount_cumsum = np.cumsum(discount)
        cumulative_gains = [
            _tie_averaged_dcg(y_t, y_s, discount_cumsum)
            for y_t, y_s in zip(y_true, y_score)
        ]
        cumulative_gains = np.asarray(cumulative_gains)
    return cumulative_gains


def _tie_averaged_dcg(y_true, y_score, discount_cumsum):
    """
    Compute DCG by averaging over possible permutations of ties.

    The gain (`y_true`) of an index falling inside a tied group (in the order
    induced by `y_score`) is replaced by the average gain within this group.
    The discounted gain for a tied group is then the average `y_true` within
    this group times the sum of discounts of the corresponding ranks.

    This amounts to averaging scores for all possible orderings of the tied
    groups.

    (note in the case of dcg@k the discount is 0 after index k)

    Parameters
    ----------
    y_true : ndarray
        The true relevance scores.

    y_score : ndarray
        Predicted scores.

    discount_cumsum : ndarray
        Precomputed cumulative sum of the discounts.

    Returns
    -------
    discounted_cumulative_gain : float
        The discounted cumulative gain.

    References
    ----------
    McSherry, F., & Najork, M. (2008, March). Computing information retrieval
    performance measures efficiently in the presence of tied scores. In
    European conference on information retrieval (pp. 414-421). Springer,
    Berlin, Heidelberg.
    """
    _, inv, counts = np.unique(-y_score, return_inverse=True, return_counts=True)
    ranked = np.zeros(len(counts))
    np.add.at(ranked, inv, y_true)
    ranked /= counts
    groups = np.cumsum(counts) - 1
    discount_sums = np.empty(len(counts))
    discount_sums[0] = discount_cumsum[groups[0]]
    discount_sums[1:] = np.diff(discount_cumsum[groups])
    return (ranked * discount_sums).sum()


def _check_dcg_target_type(y_true):
    y_type = type_of_target(y_true, input_name="y_true")
    supported_fmt = (
        "multilabel-indicator",
        "continuous-multioutput",
        "multiclass-multioutput",
    )
    if y_type not in supported_fmt:
        raise ValueError(
            "Only {} formats are supported. Got {} instead".format(
                supported_fmt, y_type
            )
        )


@validate_params(
    {
        "y_true": ["array-like"],
        "y_score": ["array-like"],
        "k": [Interval(Integral, 1, None, closed="left"), None],
        "log_base": [Interval(Real, 0.0, None, closed="neither")],
        "sample_weight": ["array-like", None],
        "ignore_ties": ["boolean"],
    },
    prefer_skip_nested_validation=True,
)
def dcg_score(
    y_true, y_score, *, k=None, log_base=2, sample_weight=None, ignore_ties=False
):
    """Compute Discounted Cumulative Gain.

    Sum the true scores ranked in the order induced by the predicted scores,
    after applying a logarithmic discount.

    This ranking metric yields a high value if true labels are ranked high by
    ``y_score``.

    Usually the Normalized Discounted Cumulative Gain (NDCG, computed by
    ndcg_score) is preferred.

    Parameters
    ----------
    y_true : array-like of shape (n_samples, n_labels)
        True targets of multilabel classification, or true scores of entities
        to be ranked.

    y_score : array-like of shape (n_samples, n_labels)
        Target scores, can either be probability estimates, confidence values,
        or non-thresholded measure of decisions (as returned by
        "decision_function" on some classifiers).

    k : int, default=None
        Only consider the highest k scores in the ranking. If None, use all
        outputs.

    log_base : float, default=2
        Base of the logarithm used for the discount. A low value means a
        sharper discount (top results are more important).

    sample_weight : array-like of shape (n_samples,), default=None
        Sample weights. If `None`, all samples are given the same weight.

    ignore_ties : bool, default=False
        Assume that there are no ties in y_score (which is likely to be the
        case if y_score is continuous) for efficiency gains.

    Returns
    -------
    discounted_cumulative_gain : float
        The averaged sample DCG scores.

    See Also
    --------
    ndcg_score : The Discounted Cumulative Gain divided by the Ideal Discounted
        Cumulative Gain (the DCG obtained for a perfect ranking), in order to
        have a score between 0 and 1.

    References
    ----------
    `Wikipedia entry for Discounted Cumulative Gain
    <https://en.wikipedia.org/wiki/Discounted_cumulative_gain>`_.

    Jarvelin, K., & Kekalainen, J. (2002).
    Cumulated gain-based evaluation of IR techniques. ACM Transactions on
    Information Systems (TOIS), 20(4), 422-446.

    Wang, Y., Wang, L., Li, Y., He, D., Chen, W., & Liu, T. Y. (2013, May).
    A theoretical analysis of NDCG ranking measures. In Proceedings of the 26th
    Annual Conference on Learning Theory (COLT 2013).

    McSherry, F., & Najork, M. (2008, March). Computing information retrieval
    performance measures efficiently in the presence of tied scores. In
    European conference on information retrieval (pp. 414-421). Springer,
    Berlin, Heidelberg.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.metrics import dcg_score
    >>> # we have groud-truth relevance of some answers to a query:
    >>> true_relevance = np.asarray([[10, 0, 0, 1, 5]])
    >>> # we predict scores for the answers
    >>> scores = np.asarray([[.1, .2, .3, 4, 70]])
    >>> dcg_score(true_relevance, scores)
    9.49...
    >>> # we can set k to truncate the sum; only top k answers contribute
    >>> dcg_score(true_relevance, scores, k=2)
    5.63...
    >>> # now we have some ties in our prediction
    >>> scores = np.asarray([[1, 0, 0, 0, 1]])
    >>> # by default ties are averaged, so here we get the average true
    >>> # relevance of our top predictions: (10 + 5) / 2 = 7.5
    >>> dcg_score(true_relevance, scores, k=1)
    7.5
    >>> # we can choose to ignore ties for faster results, but only
    >>> # if we know there aren't ties in our scores, otherwise we get
    >>> # wrong results:
    >>> dcg_score(true_relevance,
    ...           scores, k=1, ignore_ties=True)
    5.0
    """
    y_true = check_array(y_true, ensure_2d=False)
    y_score = check_array(y_score, ensure_2d=False)
    check_consistent_length(y_true, y_score, sample_weight)
    _check_dcg_target_type(y_true)
    return np.average(
        _dcg_sample_scores(
            y_true, y_score, k=k, log_base=log_base, ignore_ties=ignore_ties
        ),
        weights=sample_weight,
    )


def _ndcg_sample_scores(y_true, y_score, k=None, ignore_ties=False):
    """Compute Normalized Discounted Cumulative Gain.

    Sum the true scores ranked in the order induced by the predicted scores,
    after applying a logarithmic discount. Then divide by the best possible
    score (Ideal DCG, obtained for a perfect ranking) to obtain a score between
    0 and 1.

    This ranking metric yields a high value if true labels are ranked high by
    ``y_score``.

    Parameters
    ----------
    y_true : ndarray of shape (n_samples, n_labels)
        True targets of multilabel classification, or true scores of entities
        to be ranked.

    y_score : ndarray of shape (n_samples, n_labels)
        Target scores, can either be probability estimates, confidence values,
        or non-thresholded measure of decisions (as returned by
        "decision_function" on some classifiers).

    k : int, default=None
        Only consider the highest k scores in the ranking. If None, use all
        outputs.

    ignore_ties : bool, default=False
        Assume that there are no ties in y_score (which is likely to be the
        case if y_score is continuous) for efficiency gains.

    Returns
    -------
    normalized_discounted_cumulative_gain : ndarray of shape (n_samples,)
        The NDCG score for each sample (float in [0., 1.]).

    See Also
    --------
    dcg_score : Discounted Cumulative Gain (not normalized).

    """
    gain = _dcg_sample_scores(y_true, y_score, k, ignore_ties=ignore_ties)
    # Here we use the order induced by y_true so we can ignore ties since
    # the gain associated to tied indices is the same (permuting ties doesn't
    # change the value of the re-ordered y_true)
    normalizing_gain = _dcg_sample_scores(y_true, y_true, k, ignore_ties=True)
    all_irrelevant = normalizing_gain == 0
    gain[all_irrelevant] = 0
    gain[~all_irrelevant] /= normalizing_gain[~all_irrelevant]
    return gain


@validate_params(
    {
        "y_true": ["array-like"],
        "y_score": ["array-like"],
        "k": [Interval(Integral, 1, None, closed="left"), None],
        "sample_weight": ["array-like", None],
        "ignore_ties": ["boolean"],
    },
    prefer_skip_nested_validation=True,
)
def ndcg_score(y_true, y_score, *, k=None, sample_weight=None, ignore_ties=False):
    """Compute Normalized Discounted Cumulative Gain.

    Sum the true scores ranked in the order induced by the predicted scores,
    after applying a logarithmic discount. Then divide by the best possible
    score (Ideal DCG, obtained for a perfect ranking) to obtain a score between
    0 and 1.

    This ranking metric returns a high value if true labels are ranked high by
    ``y_score``.

    Parameters
    ----------
    y_true : array-like of shape (n_samples, n_labels)
        True targets of multilabel classification, or true scores of entities
        to be ranked. Negative values in `y_true` may result in an output
        that is not between 0 and 1.

    y_score : array-like of shape (n_samples, n_labels)
        Target scores, can either be probability estimates, confidence values,
        or non-thresholded measure of decisions (as returned by
        "decision_function" on some classifiers).

    k : int, default=None
        Only consider the highest k scores in the ranking. If `None`, use all
        outputs.

    sample_weight : array-like of shape (n_samples,), default=None
        Sample weights. If `None`, all samples are given the same weight.

    ignore_ties : bool, default=False
        Assume that there are no ties in y_score (which is likely to be the
        case if y_score is continuous) for efficiency gains.

    Returns
    -------
    normalized_discounted_cumulative_gain : float in [0., 1.]
        The averaged NDCG scores for all samples.

    See Also
    --------
    dcg_score : Discounted Cumulative Gain (not normalized).

    References
    ----------
    `Wikipedia entry for Discounted Cumulative Gain
    <https://en.wikipedia.org/wiki/Discounted_cumulative_gain>`_

    Jarvelin, K., & Kekalainen, J. (2002).
    Cumulated gain-based evaluation of IR techniques. ACM Transactions on
    Information Systems (TOIS), 20(4), 422-446.

    Wang, Y., Wang, L., Li, Y., He, D., Chen, W., & Liu, T. Y. (2013, May).
    A theoretical analysis of NDCG ranking measures. In Proceedings of the 26th
    Annual Conference on Learning Theory (COLT 2013)

    McSherry, F., & Najork, M. (2008, March). Computing information retrieval
    performance measures efficiently in the presence of tied scores. In
    European conference on information retrieval (pp. 414-421). Springer,
    Berlin, Heidelberg.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.metrics import ndcg_score
    >>> # we have groud-truth relevance of some answers to a query:
    >>> true_relevance = np.asarray([[10, 0, 0, 1, 5]])
    >>> # we predict some scores (relevance) for the answers
    >>> scores = np.asarray([[.1, .2, .3, 4, 70]])
    >>> ndcg_score(true_relevance, scores)
    0.69...
    >>> scores = np.asarray([[.05, 1.1, 1., .5, .0]])
    >>> ndcg_score(true_relevance, scores)
    0.49...
    >>> # we can set k to truncate the sum; only top k answers contribute.
    >>> ndcg_score(true_relevance, scores, k=4)
    0.35...
    >>> # the normalization takes k into account so a perfect answer
    >>> # would still get 1.0
    >>> ndcg_score(true_relevance, true_relevance, k=4)
    1.0...
    >>> # now we have some ties in our prediction
    >>> scores = np.asarray([[1, 0, 0, 0, 1]])
    >>> # by default ties are averaged, so here we get the average (normalized)
    >>> # true relevance of our top predictions: (10 / 10 + 5 / 10) / 2 = .75
    >>> ndcg_score(true_relevance, scores, k=1)
    0.75...
    >>> # we can choose to ignore ties for faster results, but only
    >>> # if we know there aren't ties in our scores, otherwise we get
    >>> # wrong results:
    >>> ndcg_score(true_relevance,
    ...           scores, k=1, ignore_ties=True)
    0.5...
    """
    y_true = check_array(y_true, ensure_2d=False)
    y_score = check_array(y_score, ensure_2d=False)
    check_consistent_length(y_true, y_score, sample_weight)

    if y_true.min() < 0:
        raise ValueError("ndcg_score should not be used on negative y_true values.")
    if y_true.ndim > 1 and y_true.shape[1] <= 1:
        raise ValueError(
            "Computing NDCG is only meaningful when there is more than 1 document. "
            f"Got {y_true.shape[1]} instead."
        )
    _check_dcg_target_type(y_true)
    gain = _ndcg_sample_scores(y_true, y_score, k=k, ignore_ties=ignore_ties)
    return np.average(gain, weights=sample_weight)


@validate_params(
    {
        "y_true": ["array-like"],
        "y_score": ["array-like"],
        "k": [Interval(Integral, 1, None, closed="left")],
        "normalize": ["boolean"],
        "sample_weight": ["array-like", None],
        "labels": ["array-like", None],
    },
    prefer_skip_nested_validation=True,
)
def top_k_accuracy_score(
    y_true, y_score, *, k=2, normalize=True, sample_weight=None, labels=None
):
    """Top-k Accuracy classification score.

    This metric computes the number of times where the correct label is among
    the top `k` labels predicted (ranked by predicted scores). Note that the
    multilabel case isn't covered here.

    Read more in the :ref:`User Guide <top_k_accuracy_score>`

    Parameters
    ----------
    y_true : array-like of shape (n_samples,)
        True labels.

    y_score : array-like of shape (n_samples,) or (n_samples, n_classes)
        Target scores. These can be either probability estimates or
        non-thresholded decision values (as returned by
        :term:`decision_function` on some classifiers).
        The binary case expects scores with shape (n_samples,) while the
        multiclass case expects scores with shape (n_samples, n_classes).
        In the multiclass case, the order of the class scores must
        correspond to the order of ``labels``, if provided, or else to
        the numerical or lexicographical order of the labels in ``y_true``.
        If ``y_true`` does not contain all the labels, ``labels`` must be
        provided.

    k : int, default=2
        Number of most likely outcomes considered to find the correct label.

    normalize : bool, default=True
        If `True`, return the fraction of correctly classified samples.
        Otherwise, return the number of correctly classified samples.

    sample_weight : array-like of shape (n_samples,), default=None
        Sample weights. If `None`, all samples are given the same weight.

    labels : array-like of shape (n_classes,), default=None
        Multiclass only. List of labels that index the classes in ``y_score``.
        If ``None``, the numerical or lexicographical order of the labels in
        ``y_true`` is used. If ``y_true`` does not contain all the labels,
        ``labels`` must be provided.

    Returns
    -------
    score : float
        The top-k accuracy score. The best performance is 1 with
        `normalize == True` and the number of samples with
        `normalize == False`.

    See Also
    --------
    accuracy_score : Compute the accuracy score. By default, the function will
        return the fraction of correct predictions divided by the total number
        of predictions.

    Notes
    -----
    In cases where two or more labels are assigned equal predicted scores,
    the labels with the highest indices will be chosen first. This might
    impact the result if the correct label falls after the threshold because
    of that.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.metrics import top_k_accuracy_score
    >>> y_true = np.array([0, 1, 2, 2])
    >>> y_score = np.array([[0.5, 0.2, 0.2],  # 0 is in top 2
    ...                     [0.3, 0.4, 0.2],  # 1 is in top 2
    ...                     [0.2, 0.4, 0.3],  # 2 is in top 2
    ...                     [0.7, 0.2, 0.1]]) # 2 isn't in top 2
    >>> top_k_accuracy_score(y_true, y_score, k=2)
    0.75
    >>> # Not normalizing gives the number of "correctly" classified samples
    >>> top_k_accuracy_score(y_true, y_score, k=2, normalize=False)
    3
    """
    y_true = check_array(y_true, ensure_2d=False, dtype=None)
    y_true = column_or_1d(y_true)
    y_type = type_of_target(y_true, input_name="y_true")
    if y_type == "binary" and labels is not None and len(labels) > 2:
        y_type = "multiclass"
    if y_type not in {"binary", "multiclass"}:
        raise ValueError(
            f"y type must be 'binary' or 'multiclass', got '{y_type}' instead."
        )
    y_score = check_array(y_score, ensure_2d=False)
    if y_type == "binary":
        if y_score.ndim == 2 and y_score.shape[1] != 1:
            raise ValueError(
                "`y_true` is binary while y_score is 2d with"
                f" {y_score.shape[1]} classes. If `y_true` does not contain all the"
                " labels, `labels` must be provided."
            )
        y_score = column_or_1d(y_score)

    check_consistent_length(y_true, y_score, sample_weight)
    y_score_n_classes = y_score.shape[1] if y_score.ndim == 2 else 2

    if labels is None:
        classes = _unique(y_true)
        n_classes = len(classes)

        if n_classes != y_score_n_classes:
            raise ValueError(
                f"Number of classes in 'y_true' ({n_classes}) not equal "
                f"to the number of classes in 'y_score' ({y_score_n_classes})."
                "You can provide a list of all known classes by assigning it "
                "to the `labels` parameter."
            )
    else:
        labels = column_or_1d(labels)
        classes = _unique(labels)
        n_labels = len(labels)
        n_classes = len(classes)

        if n_classes != n_labels:
            raise ValueError("Parameter 'labels' must be unique.")

        if not np.array_equal(classes, labels):
            raise ValueError("Parameter 'labels' must be ordered.")

        if n_classes != y_score_n_classes:
            raise ValueError(
                f"Number of given labels ({n_classes}) not equal to the "
                f"number of classes in 'y_score' ({y_score_n_classes})."
            )

        if len(np.setdiff1d(y_true, classes)):
            raise ValueError("'y_true' contains labels not in parameter 'labels'.")

    if k >= n_classes:
        warnings.warn(
            (
                f"'k' ({k}) greater than or equal to 'n_classes' ({n_classes}) "
                "will result in a perfect score and is therefore meaningless."
            ),
            UndefinedMetricWarning,
        )

    y_true_encoded = _encode(y_true, uniques=classes)

    if y_type == "binary":
        if k == 1:
            threshold = 0.5 if y_score.min() >= 0 and y_score.max() <= 1 else 0
            y_pred = (y_score > threshold).astype(np.int64)
            hits = y_pred == y_true_encoded
        else:
            hits = np.ones_like(y_score, dtype=np.bool_)
    elif y_type == "multiclass":
        sorted_pred = np.argsort(y_score, axis=1, kind="mergesort")[:, ::-1]
        hits = (y_true_encoded == sorted_pred[:, :k].T).any(axis=0)

    if normalize:
        return np.average(hits, weights=sample_weight)
    elif sample_weight is None:
        return np.sum(hits)
    else:
        return np.dot(hits, sample_weight)