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

Code before:
            "`labels`. cohen_kappa_score is undefined and set to the value defined in "
            "the `replace_undefined_by` param, which defaults to 0.0."
        )
        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)
        w_mat += np.arange(n_classes)
        if weights == "linear":
            w_mat = np.abs(w_mat - w_mat.T)
        else:
            w_mat = (w_mat - w_mat.T) ** 2

    numerator = np.sum(w_mat * confusion)
    denominator = np.sum(w_mat * expected)
    if np.isclose(denominator, 0):
        if replace_undefined_by == "deprecated":
            replace_undefined_by = np.nan
            warnings.warn(mgs_changing_default, FutureWarning)
        msg = (
            "`y1` and `y2` only have one label in common that is also in `labels`. "
            "cohen_kappa_score is undefined and set to the value defined in the "
            "`replace_undefined_by` param, which defaults to 0.0."
        )
        warnings.warn(msg, UndefinedMetricWarning, stacklevel=2)
        return replace_undefined_by
    k = numerator / denominator

    return float(1 - k)


@validate_params(
    {
        "y_true": ["array-like", "sparse matrix"],
        "y_pred": ["array-like", "sparse matrix"],
        "labels": ["array-like", None],
        "pos_label": [Real, str, "boolean", None],
        "average": [
            StrOptions({"micro", "macro", "samples", "weighted", "binary"}),
            None,
        ],
        "sample_weight": ["array-like", None],
        "zero_division": [
            Options(Real, {0, 1}),
            StrOptions({"warn"}),
        ],

Review comment (suggestion):
```suggestion
            "`y1`, `y2` and `labels` have only one label in common. "
```

Code after:
        ``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
    --------
    >>> 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: