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

Code before:
    ----------
    .. [1] :doi:`J. Cohen (1960). "A coefficient of agreement for nominal scales".
           Educational and Psychological Measurement 20(1):37-46.
           <10.1177/001316446002000104>`
    .. [2] `R. Artstein and M. Poesio (2008). "Inter-coder agreement for
           computational linguistics". Computational Linguistics 34(4):555-596
           <https://www.mitpressjournals.org/doi/pdf/10.1162/coli.07-034-R2>`_.
    .. [3] `Wikipedia entry for the Cohen's kappa
            <https://en.wikipedia.org/wiki/Cohen%27s_kappa>`_.

    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
    """

    def _check_zero_division(denominator, replace_undefined_by, msg):
        if np.isclose(denominator, 0):
            if replace_undefined_by == "deprecated":
                replace_undefined_by = np.nan
                warnings.warn(mgs_changing_default, FutureWarning)
            warnings.warn(msg, UndefinedMetricWarning, stacklevel=2)
            return True

    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 = (
        "`np.nan` as 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)

Review comment (suggestion):
```suggestion
            warnings.warn(msg, UndefinedMetricWarning, stacklevel=2)
            return True
        return False
```

Code after:

    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
        Sets the return value when the metric is undefined. This can happen when no
        label of interest (as defined in the `labels` param) is assigned by the second
        annotator, or when both `y1` and `y2`only have one label in common that is also
        in `labels`. In these cases, an
        :class:`~sklearn.exceptions.UndefinedMetricWarning` is raised. Can take the
        following values:

        - `np.nan` to return `np.nan`
        - a floating point value in the range of [-1.0, 1.0] to return a specific value

        .. versionadded:: 1.9

    Returns
    -------
    kappa : float
        The kappa statistic, which is a number between -1.0 and 1.0. The maximum value
        means complete agreement; the minimum value means complete disagreement; 0.0
        indicates no agreement beyond what would be expected by chance.

    References
    ----------
    .. [1] :doi:`J. Cohen (1960). "A coefficient of agreement for nominal scales".
           Educational and Psychological Measurement 20(1):37-46.
           <10.1177/001316446002000104>`
    .. [2] `R. Artstein and M. Poesio (2008). "Inter-coder agreement for
           computational linguistics". Computational Linguistics 34(4):555-596
           <https://www.mitpressjournals.org/doi/pdf/10.1162/coli.07-034-R2>`_.
    .. [3] `Wikipedia entry for the Cohen's kappa
            <https://en.wikipedia.org/wiki/Cohen%27s_kappa>`_.

    Examples