mars.learn.utils.validation.check_is_fitted#

mars.learn.utils.validation.check_is_fitted(estimator, attributes=None, *, msg=None, all_or_any=<built-in function all>)[source]#

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 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.

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.

Return type

None

Raises

NotFittedError – If the attributes are not found.