# Source: https://github.com/scikit-learn/scikit-learn

"""Utilities for input validation"""

# Authors: Olivier Grisel
#          Gael Varoquaux
#          Andreas Mueller
#          Lars Buitinck
#          Alexandre Gramfort
#          Nicolas Tresegnie
# License: BSD 3 clause


class NotFittedError(ValueError, AttributeError):
    """Exception class to raise if estimator is used before fitting.
    This class inherits from both ValueError and AttributeError to help with
    exception handling and backward compatibility.
    Examples
    --------
    >>> from sklearn.svm import LinearSVC
    >>> from sklearn.exceptions import NotFittedError
    >>> try:
    ...     LinearSVC().predict([[1, 2], [2, 3], [3, 4]])
    ... except NotFittedError as e:
    ...     print(repr(e))
    ...                        # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    NotFittedError('This LinearSVC instance is not fitted yet',)
    .. versionchanged:: 0.18
       Moved from sklearn.utils.validation.
    """


def check_is_fitted(estimator, attributes, msg=None, all_or_any=all):
    """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.
    """
    if msg is None:
        msg = ("This %(name)s instance is not fitted yet. Call 'fit' with "
               "appropriate arguments before using this method.")

    if not hasattr(estimator, 'fit'):
        raise TypeError("%s is not an estimator instance." % (estimator))

    if not isinstance(attributes, (list, tuple)):
        attributes = [attributes]

    if not all_or_any([hasattr(estimator, attr) for attr in attributes]):
        raise NotFittedError(msg % {'name': type(estimator).__name__})