Function validate_data

mne/decoding/_fixes.py:12–133

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`.

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

    # Use a limited version pulled from sklearn 1.7
    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`.