No need to check if # of dimensions of testing set align with training set in target_encoder

category_encoders/category_encoders/utils.py

Lines 322 to 323 in 6a13c14

    
           if X.shape[1] != self._dim: 
        
               raise ValueError(f'Unexpected input dimension {X.shape[1]}, expected {self._dim}')

For the function _check_transform_inputs(), I do not want it to report error when # of dimensions for testing set don't align with training set. However, the default is it has to align. Considering that the purpose of target encoder is to transform designated columns using target encoders, nothing else, logically we don't have to validate the dimension alignment.

plase have a look at the dicussion in #355
is this a duplicate of that issue?

	if X.shape[1] != self._dim:
	raise ValueError(f'Unexpected input dimension {X.shape[1]}, expected {self._dim}')