Function check_is_fitted

sklearn/utils/validation.py:1634–1718

Perform is_fitted validation for estimator. Checks if the estimator is fitted by verifying the presence of fitted attributes (ending with a trailing underscore) and otherwise raises a :class:`~sklearn.exceptions.NotFittedError` with the given message. If an estimator does not set a

(estimator, attributes=None, *, msg=None, all_or_any=all)

def check_is_fitted(estimator, attributes=None, *, msg=None, all_or_any=all):
    """Perform is_fitted validation for estimator.

    Checks if the estimator is fitted by verifying the presence of
    fitted attributes (ending with a trailing underscore) and otherwise
    raises a :class:`~sklearn.exceptions.NotFittedError` with the given message.

    If an estimator does not set any attributes with a trailing underscore, it
    can define a ``__sklearn_is_fitted__`` method returning a boolean to
    specify if the estimator is fitted or not. See
    :ref:`sphx_glr_auto_examples_developing_estimators_sklearn_is_fitted.py`
    for an example on how to use the API.

    If no `attributes` are passed, this function will pass if an estimator is stateless.
    An estimator can indicate it's stateless by setting the `requires_fit` tag. See
    :ref:`estimator_tags` for more information. Note that the `requires_fit` tag
    is ignored if `attributes` are passed.

    Parameters
    ----------
    estimator : estimator instance
        Estimator instance for which the check is performed.

    attributes : str, list or tuple of str, default=None
        Attribute name(s) given as string or a list/tuple of strings
        Eg.: ``["coef_", "estimator_", ...], "coef_"``

        If `None`, `estimator` is considered fitted if there exist an
        attribute that ends with a underscore and does not start with double
        underscore.

    msg : str, default=None
        The default error message is, "This %(name)s instance is not fitted
        yet. Call 'fit' with appropriate arguments before using this
        estimator."

        For custom messages if "%(name)s" is present in the message string,
        it is substituted for the estimator name.

        Eg. : "Estimator, %(name)s, must be fitted before sparsifying".

    all_or_any : callable, {all, any}, default=all
        Specify whether all or any of the given attributes must exist.

    Raises
    ------
    TypeError
        If the estimator is a class or not an estimator instance

    NotFittedError
        If the attributes are not found.

    Examples
    --------
    >>> from sklearn.linear_model import LogisticRegression
    >>> from sklearn.utils.validation import check_is_fitted
    >>> from sklearn.exceptions import NotFittedError
    >>> lr = LogisticRegression()