sklearn 流水线 pipeline TypeError: fit_transform() takes 2 positional arguments but 3 were given解决

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import FeatureUnion
import pandas as pd
import numpy as np
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import LabelBinarizer


housing=pd.read_csv(r'datafilepath'
    )#缺失值默认记为Non
housing_num = housing.drop('ocean_proximity', axis=1)

from sklearn.base import BaseEstimator, TransformerMixin
class DataFrameSelector(BaseEstimator, TransformerMixin):
    def __init__(self, attribute_names):
        self.attribute_names = attribute_names
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X[self.attribute_names].values

from sklearn.base import BaseEstimator, TransformerMixin
rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6

class CombinedAttributesAdder(BaseEstimator, TransformerMixin):
    def __init__(self, add_bedrooms_per_room = True): # no *args or **kargs
        self.add_bedrooms_per_room = add_bedrooms_per_room
    def fit(self, X, y=None):
        return self  # nothing else to do
    def transform(self, X, y=None):
        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]
        population_per_household = X[:, population_ix] / X[:, household_ix]
        if self.add_bedrooms_per_room:
            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]
            return np.c_[X, rooms_per_household, population_per_household,
                         bedrooms_per_room]
        else:
            return np.c_[X, rooms_per_household, population_per_household]

attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)
housing_extra_attribs = attr_adder.transform(housing.values)

num_attribs = list(housing_num)
cat_attribs = ["ocean_proximity"]
num_pipeline = Pipeline([
('selector', DataFrameSelector(num_attribs)),
('imputer', Imputer(strategy="median")),
('attribs_adder', CombinedAttributesAdder()),
('std_scaler', StandardScaler()),
])
num_pipeline_ = num_pipeline.fit_transform(housing)

cat_pipeline =  LabelBinarizer() 
cat_pipeline_ = cat_pipeline.fit_transform(housing[cat_attribs])

housing_prepared = np.concatenate((num_pipeline_, cat_pipeline_), axis=1)
print(len(housing_prepared))

参考:
https://blog.csdn.net/dss_dssssd/article/details/82846998