Function validate_data

sklearn/utils/validation.py:2929–3061

Validate input data and set or check feature names and counts of the input. This helper function should be used in an estimator that requires input validation. This mutates the estimator and sets the `n_features_in_` and `feature_names_in_` attributes if `reset=True`. .. versionadd

(
    _estimator,
    /,
    X="no_validation",
    y="no_validation",
    reset=True,
    validate_separately=False,
    skip_check_array=False,
    **check_params,
)

def validate_data(
    _estimator,
    /,
    X="no_validation",
    y="no_validation",
    reset=True,
    validate_separately=False,
    skip_check_array=False,
    **check_params,
):
    """Validate input data and set or check feature names and counts of the input.

    This helper function should be used in an estimator that requires input
    validation. This mutates the estimator and sets the `n_features_in_` and
    `feature_names_in_` attributes if `reset=True`.

    .. versionadded:: 1.6

    Parameters
    ----------
    _estimator : estimator instance
        The estimator to validate the input for.

    X : {array-like, sparse matrix, dataframe} of shape \
            (n_samples, n_features), default='no validation'
        The input samples.
        If `'no_validation'`, no validation is performed on `X`. This is
        useful for meta-estimator which can delegate input validation to
        their underlying estimator(s). In that case `y` must be passed and
        the only accepted `check_params` are `multi_output` and
        `y_numeric`.

    y : array-like of shape (n_samples,), default='no_validation'
        The targets.

        - If `None`, :func:`~sklearn.utils.check_array` is called on `X`. If
          the estimator's `requires_y` tag is True, then an error will be raised.
        - If `'no_validation'`, :func:`~sklearn.utils.check_array` is called
          on `X` and the estimator's `requires_y` tag is ignored. This is a default
          placeholder and is never meant to be explicitly set. In that case `X` must be
          passed.
        - Otherwise, only `y` with `_check_y` or both `X` and `y` are checked with
          either :func:`~sklearn.utils.check_array` or
          :func:`~sklearn.utils.check_X_y` depending on `validate_separately`.

    reset : bool, default=True
        Whether to reset the `n_features_in_` attribute.
        If False, the input will be checked for consistency with data
        provided when reset was last True.

        .. note::

           It is recommended to call `reset=True` in `fit` and in the first
           call to `partial_fit`. All other methods that validate `X`
           should set `reset=False`.

    validate_separately : False or tuple of dicts, default=False
        Only used if `y` is not `None`.