Feature extraction methods in sklearn pipeline

[MSasta]

Hey, I'm facing an issue with implementing BayesianTargetEncoder and CycleTransformer in a pipeline that I've made. This might be because of my lack of experience but any help would still be appreciated.

The pipeline I currently have is

model = RandomForestRegressor(n_estimators = 1000, min_samples_split = 2, max_features = 'sqrt',
max_depth = 20, random_state = 42, bootstrap = True, oob_score = True, n_jobs = 4)

ss = StandardScaler()
bte = BayesianTargetEncoder()
ct = CycleTransformer()

rf = Pipeline(
    [
    ("transformer", ColumnTransformer
        (
            [
                ("scaler", ss, num_cols),
                ("bayesian_target_encoder", bte, cat_cols),
                ("cycle_transformer"), ct, times
            ]
        )
    ),
    ("reg", model) 
    ]
)  

and the issue that I am facing is that it says TypeError: 'CycleTransformer' object is not iterable

If I remove CycleTransformer and proceed without it, then I get this issue:

C:\py\lib\site-packages\xam\feature_extraction\encoding\bayesian_target.py in transform(self, X, y)
     73             raise ValueError('X has to be a pandas.DataFrame')
     74 
---> 75         for cols in self.columns:
     76 
     77             if isinstance(cols, list):

TypeError: 'NoneType' object is not iterable

I understand it says that X has to be a pandas dataframe but i'm not sure what the work around to this would be.
Thank you