Scikit-learn Column Transformer does not return back feature names

Question

I'm trying to use Column Transformer with OneHotEncoder to transform my categorical data :

A quick look at my data :

I want to do one-hot-encoding for 3 features : 'sex' , 'smoker' , 'region', so I use Column Transformer by scikit-learn. ( I don't want to want to seperate numerical one and categorical one than transform them seperately, I just want to perform them on a single dataset)

My code :

cat_feature = X.select_dtypes(include = 'object') #select only categorical columns 

enc = ColumnTransformer([ ('one_hot_encoder' , OneHotEncoder() , cat_feature ) ] , 
                     remainder = 'passthrough')

X_transformed  =  enc.fit_transform(X)   # transformed version of original data

My problem is that, X_transformed is then removed all the feature names which is little bit confusing for me to debug :

So is there anyway to retain my columns' names after doing this transformation? I want to incorporate this transformer into a pipeline so I can't use pd.get_dummies. Thank you!!

Answer 1

User will have to write custom Transformer which does passthrough and supports get_feature_names

Steps:

1. Custom Transformer which will return pass through columns names via get_feature_names
2. Dont use remainder = 'passthrough' but rather use our custom Transformer

Use enc.get_feature_names() to get the feature list.

Sample:

from sklearn.base import BaseEstimator

df = pd.DataFrame({
    'age': [1,2,3,4],
    'sex': ['male', 'female']*2,
    'bmi': [1.1,2.2,3.3,4.4],
    'children': [1]*4,
    'smoker': ['yes', 'no']*2
})
cat_features = df.select_dtypes(include = 'object').columns
passthrough_features = [c for c in df.columns if c not in cat_features]

class PassthroughTransformer(BaseEstimator):
  def fit(self, X, y = None):
    self.cols = X.columns
    return self

  def transform(self, X, y = None):
    self.cols = X.columns
    return X.values

  def get_feature_names(self):
    return self.cols

enc = ColumnTransformer([ ('1hot' , OneHotEncoder() , cat_features ),
                         ('pass' , PassthroughTransformer(), passthrough_features)])
X_transformed   = enc.fit_transform(df)
pd.DataFrame(X_transformed, columns=enc.get_feature_names())

Output:

    1hot__x0_female 1hot__x0_male   1hot__x1_no 1hot__x1_yes    pass__age   pass__bmi   pass__children
0   0.0             1.0             0.0         1.0             1.0         1.1         1.0
1   1.0             0.0             1.0         0.0             2.0         2.2         1.0
2   0.0             1.0             0.0         1.0             3.0         3.3         1.0
3    1.0            0.0             1.0         0.0             4.0         4.4         1.0