Function _check_sample_weight

sklearn/utils/validation.py:2092–2206

Validate sample weights. Note that passing sample_weight=None will output an array of ones. Therefore, in some cases, you may want to protect the call with: if sample_weight is not None: sample_weight = _check_sample_weight(...) Parameters ---------- sample_weight :

(
    sample_weight,
    X,
    *,
    dtype=None,
    force_float_dtype=True,
    ensure_non_negative=False,
    ensure_same_device=True,
    copy=False,
    allow_all_zero_weights=False,
)

def _check_sample_weight(
    sample_weight,
    X,
    *,
    dtype=None,
    force_float_dtype=True,
    ensure_non_negative=False,
    ensure_same_device=True,
    copy=False,
    allow_all_zero_weights=False,
):
    """Validate sample weights.

    Note that passing sample_weight=None will output an array of ones.
    Therefore, in some cases, you may want to protect the call with:
    if sample_weight is not None:
        sample_weight = _check_sample_weight(...)

    Parameters
    ----------
    sample_weight : {ndarray, Number or None}, shape (n_samples,)
        Input sample weights.

    X : {ndarray, list, sparse matrix}
        Input data.

    dtype : dtype, default=None
        dtype of the validated `sample_weight`.
        If None, and `sample_weight` is an array:

            - If `sample_weight.dtype` is one of `{np.float64, np.float32}`,
              then the dtype is preserved.
            - Else the output has NumPy's default dtype: `np.float64`.

        If `dtype` is not `{np.float32, np.float64, None}`, then output will
        be `np.float64`.

    force_float_dtype : bool, default=True
        Whether `X` should be forced to be float dtype, when `dtype` is a non-float
        dtype or None.

    ensure_non_negative : bool, default=False,
        Whether or not the weights are expected to be non-negative.

        .. versionadded:: 1.0

    ensure_same_device : bool, default=True
        Whether `sample_weight` should be forced to be on the same device as `X`.

    copy : bool, default=False
        If True, a copy of sample_weight will be created.

    allow_all_zero_weights : bool, default=False,
        Whether or not to raise an error when sample weights are all zero.

    Returns
    -------
    sample_weight : ndarray of shape (n_samples,)