mlearner version: 0.2.0

check_is_fitted

check_is_fitted(estimator, attributes, msg=None, all_or_any=)

Perform is_fitted validation for estimator. Checks if the estimator is fitted by verifying the presence of "all_or_any" of the passed attributes and raises a NotFittedError with the given message. Parameters

• estimator : estimator instance.

  estimator instance for which the check is performed.

• attributes : attribute name(s) given as string or a list/tuple of strings

  Eg.: ["coef_", "estimator_", ...], "coef_"

• msg : string

  The default error message is, "This %(name)s instance is not fitted yet. Call 'fit' with appropriate arguments before using this method." 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. Returns

None Raises

NotFittedError If the attributes are not found.