Как реализовать RandomUnderSampler в конвейере обучения scikit?

У меня есть конвейер обучения scikit для масштабирования числовых функций и кодирования категориальных функций. Он работал нормально, пока я не попытался реализовать RandomUnderSampler из имблэрна. Моя цель — реализовать шаг подвыборки, так как мой набор данных очень несбалансирован 1: 1000.

Я обязательно использовал метод Pipeline из imblearn вместо sklearn. И ниже код, который я пробовал.

Данные кода работают (с использованием конвейера sklearn) без метода подвыборки.

from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from imblearn.pipeline import make_pipeline as make_pipeline_imb
from imblearn.pipeline import Pipeline as Pipeline_imb

from sklearn.base import BaseEstimator, TransformerMixin
class TypeSelector(BaseEstimator, TransformerMixin):
    def __init__(self, dtype):
        self.dtype = dtype
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        assert isinstance(X, pd.DataFrame)
        return X.select_dtypes(include=[self.dtype])

transformer = Pipeline([
    # Union numeric, categoricals and boolean
    ('features', FeatureUnion(n_jobs=1, transformer_list=[
         # Select bolean features                                                  
        ('boolean', Pipeline([
            ('selector', TypeSelector('bool')),
        ])),
         # Select and scale numericals
        ('numericals', Pipeline([
            ('selector', TypeSelector(np.number)),
            ('scaler', StandardScaler()),
        ])),
         # Select and encode categoricals
        ('categoricals', Pipeline([
            ('selector', TypeSelector('category')),
            ('encoder', OneHotEncoder(handle_unknown='ignore')),
        ])) 
    ])),
])
pipe = Pipeline([('prep', transformer), 
                 ('clf', RandomForestClassifier(n_estimators=500, class_weight='balanced'))
                 ])

Код, который не работает (используя конвейер imblearn) с методом подвыборки.

from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from imblearn.pipeline import make_pipeline as make_pipeline_imb
from imblearn.pipeline import Pipeline as Pipeline_imb

from sklearn.base import BaseEstimator, TransformerMixin
class TypeSelector(BaseEstimator, TransformerMixin):
    def __init__(self, dtype):
        self.dtype = dtype
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        assert isinstance(X, pd.DataFrame)
        return X.select_dtypes(include=[self.dtype])

transformer = Pipeline_imb([
    # Union numeric, categoricals and boolean
    ('features', FeatureUnion(n_jobs=1, transformer_list=[
         # Select bolean features                                                  
        ('boolean', Pipeline_imb([
            ('selector', TypeSelector('bool')),
        ])),
         # Select and scale numericals
        ('numericals', Pipeline_imb([
            ('selector', TypeSelector(np.number)),
            ('scaler', StandardScaler()),
        ])),
         # Select and encode categoricals
        ('categoricals', Pipeline_imb([
            ('selector', TypeSelector('category')),
            ('encoder', OneHotEncoder(handle_unknown='ignore')),
        ])) 
    ])),  
])
pipe = Pipeline_imb([
                 ('sampler', RandomUnderSampler(0.1)),
                 ('prep', transformer), 
                 ('clf', RandomForestClassifier(n_estimators=500, class_weight='balanced'))
                 ])

Вот ошибка, которую я получаю:

/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py in __init__(self, steps, memory, verbose)
    133     def __init__(self, steps, memory=None, verbose=False):
    134         self.steps = steps
--> 135         self._validate_steps()
    136         self.memory = memory
    137         self.verbose = verbose

/usr/local/lib/python3.6/dist-packages/imblearn/pipeline.py in _validate_steps(self)
    144             if isinstance(t, pipeline.Pipeline):
    145                 raise TypeError(
--> 146                     "All intermediate steps of the chain should not be"
    147                     " Pipelines")
    148 

TypeError: All intermediate steps of the chain should not be Pipelines