Repository: scikit-learn/scikit-learn
File: sklearn/metrics/_classification.py (Python)

Code before:
    Examples
    --------
    >>> from sklearn.metrics import cohen_kappa_score
    >>> y1 = ["negative", "positive", "negative", "neutral", "positive"]
    >>> y2 = ["negative", "positive", "negative", "neutral", "negative"]
    >>> cohen_kappa_score(y1, y2)
    0.6875
    """
    try:
        confusion = confusion_matrix(y1, y2, labels=labels, sample_weight=sample_weight)
    except ValueError as e:
        if "At least one label specified must be in y_true" in str(e):
            msg = (
                "At least one label in `labels` must be present in `y1` (even though "
                "`cohen_kappa_score` is otherwise agnostic to the order of `y1` and "
                "`y2`)."
            )
            raise ValueError(msg) from e
        raise

    n_classes = confusion.shape[0]
    sum0 = np.sum(confusion, axis=0)
    sum1 = np.sum(confusion, axis=1)

    mgs_changing_default = (
        "The default return value of `cohen_kappa_score` in case of a division "
        "by zero has been deprecated in 1.7 and will be changed to 0.0 in version "
        "1.9. Set `replace_undefined_by=0.0` to use the new default and to silence "
        "this Warning."
    )

    numerator = np.outer(sum0, sum1)
    denominator = np.sum(sum0)
    if np.isclose(denominator, 0):
        if replace_undefined_by == "deprecated":
            replace_undefined_by = np.nan
            warnings.warn(mgs_changing_default, FutureWarning)
        msg = (
            "`y2` contains no labels that are presented in both `y1` and `labels`."
            "cohen_kappa_score is undefined and set to the value defined in "
            "the `replace_undefined_by` param, which defaults to `np.nan`."
        )
        warnings.warn(msg, UndefinedMetricWarning, stacklevel=2)
        return replace_undefined_by
    expected = numerator / denominator

    if weights is None:
        w_mat = np.ones([n_classes, n_classes], dtype=int)
        w_mat.flat[:: n_classes + 1] = 0
    else:  # "linear" or "quadratic"
        w_mat = np.zeros([n_classes, n_classes], dtype=int)

Review comment (suggestion):
```suggestion
        "`np.nan` as the default return value of `cohen_kappa_score` in case of a division "
```

This makes it a bit more clear

Code after:
)
def cohen_kappa_score(
    y1,
    y2,
    *,
    labels=None,
    weights=None,
    sample_weight=None,
    replace_undefined_by=np.nan,
):
    r"""Compute Cohen's kappa: a statistic that measures inter-annotator agreement.

    This function computes Cohen's kappa [1]_, a score that expresses the level
    of agreement between two annotators on a classification problem. It is
    defined as

    .. math::
        \kappa = (p_o - p_e) / (1 - p_e)

    where :math:`p_o` is the empirical probability of agreement on the label
    assigned to any sample (the observed agreement ratio), and :math:`p_e` is
    the expected agreement when both annotators assign labels randomly.
    :math:`p_e` is estimated using a per-annotator empirical prior over the
    class labels [2]_.

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

    Parameters
    ----------
    y1 : array-like of shape (n_samples,)
        Labels assigned by the first annotator.

    y2 : array-like of shape (n_samples,)
        Labels assigned by the second annotator. The kappa statistic is
        symmetric, so swapping ``y1`` and ``y2`` doesn't change the value.

    labels : array-like of shape (n_classes,), default=None
        List of labels to index the matrix. This may be used to select a
        subset of labels. If `None`, all labels that appear at least once in
        ``y1`` or ``y2`` are used. Note that at least one label in `labels` must be
        present in `y1`, even though this function is otherwise agnostic to the order
        of `y1` and `y2`.

    weights : {'linear', 'quadratic'}, default=None
        Weighting type to calculate the score. `None` means not weighted;
        "linear" means linear weighting; "quadratic" means quadratic weighting.

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

    replace_undefined_by : np.nan, float in [-1.0, 1.0], default=np.nan