def check_is_fitted(estimator, attributes, msg=None, all_or_any=all):
 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__}))

def check_is_fitted ( estimator , attributes , msg = None , all_or_any = all ) : 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__ } ) )
perform is_fitted validation for estimator .