Function _encode

sklearn/utils/_encode.py:197–237

Helper function to encode values into [0, n_uniques - 1]. Uses pure python method for object dtype, and numpy method for all other dtypes. The numpy method has the limitation that the `uniques` need to be sorted. Importantly, this is not checked but assumed to already be the cas

(values, *, uniques, check_unknown=True)

def _encode(values, *, uniques, check_unknown=True):
    """Helper function to encode values into [0, n_uniques - 1].

    Uses pure python method for object dtype, and numpy method for
    all other dtypes.
    The numpy method has the limitation that the `uniques` need to
    be sorted. Importantly, this is not checked but assumed to already be
    the case. The calling method needs to ensure this for all non-object
    values.

    Parameters
    ----------
    values : ndarray
        Values to encode.
    uniques : ndarray
        The unique values in `values`. If the dtype is not object, then
        `uniques` needs to be sorted.
    check_unknown : bool, default=True
        If True, check for values in `values` that are not in `unique`
        and raise an error. This is ignored for object dtype, and treated as
        True in this case. This parameter is useful for
        _BaseEncoder._transform() to avoid calling _check_unknown()
        twice.

    Returns
    -------
    encoded : ndarray
        Encoded values
    """
    xp, _ = get_namespace(values, uniques)
    if not xp.isdtype(values.dtype, "numeric"):
        try:
            return _map_to_integer(values, uniques)
        except KeyError as e:
            raise ValueError(f"y contains previously unseen labels: {e}")
    else:
        if check_unknown:
            diff = _check_unknown(values, uniques)
            if diff:
                raise ValueError(f"y contains previously unseen labels: {diff}")
        return xp.searchsorted(uniques, values)


def _check_unknown(values, known_values, return_mask=False):