Sklearn Pipeline: Get feature names after OneHotEncode In ColumnTransformer

If you are looking for how to access column names after successive pipelines with the last one being ColumnTransformer, you can access them by following this example:

In the full_pipeline there are two pipelines gender and relevent_experience

full_pipeline = ColumnTransformer([
    ("gender", gender_encoder, ["gender"]),
    ("relevent_experience", relevent_experience_encoder, ["relevent_experience"]),
])

The gender pipeline looks like this:

gender_encoder = Pipeline([
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ("cat", OneHotEncoder())
])

After fitting the full_pipeline, you can access the column names using the following snippet

full_pipeline.transformers_[0][1][1].get_feature_names()

In my case the output was: array(['x0_Female', 'x0_Male', 'x0_Other'], dtype=object)

Scikit-Learn 1.0 now has new features to keep track of feature names.

from sklearn.compose import make_column_transformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

# SimpleImputer does not have get_feature_names_out, so we need to add it
# manually. This should be fixed in Scikit-Learn 1.0.1: all transformers will
# have this method.
# g
SimpleImputer.get_feature_names_out = (lambda self, names=None:
                                       self.feature_names_in_)

num_pipeline = make_pipeline(SimpleImputer(), StandardScaler())
transformer = make_column_transformer(
    (num_pipeline, ["age", "height"]),
    (OneHotEncoder(), ["city"]))
pipeline = make_pipeline(transformer, LinearRegression())



df = pd.DataFrame({"city": ["Rabat", "Tokyo", "Paris", "Auckland"],
                   "age": [32, 65, 18, 24],
                   "height": [172, 163, 169, 190],
                   "weight": [65, 62, 54, 95]},
                  index=["Alice", "Bunji", "Cécile", "Dave"])



pipeline.fit(df, df["weight"])


## get pipeline feature names
pipeline[:-1].get_feature_names_out()


## specify feature names as your columns
pd.DataFrame(pipeline[:-1].transform(df),
             columns=pipeline[:-1].get_feature_names_out(),
             index=df.index)

EDIT: actually Peter's comment answer is in the ColumnTransformer doc:

The order of the columns in the transformed feature matrix follows the order of how the columns are specified in the transformers list. Columns of the original feature matrix that are not specified are dropped from the resulting transformed feature matrix, unless specified in the passthrough keyword. Those columns specified with passthrough are added at the right to the output of the transformers.

---

To complete Venkatachalam's answer with what Paul asked in his comment, the order of feature names as it appears in the ColumnTransformer .get_feature_names() method depends on the order of declaration of the steps variable at the ColumnTransformer instanciation.

I could not find any doc so I just played with the toy example below and that let me understand the logic.

from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import RobustScaler

class testEstimator(BaseEstimator,TransformerMixin):
    def __init__(self,string):
        self.string = string

    def fit(self,X):
        return self

    def transform(self,X):
        return np.full(X.shape, self.string).reshape(-1,1)

    def get_feature_names(self):
        return self.string

transformers = [('first_transformer',testEstimator('A'),1), ('second_transformer',testEstimator('B'),0)]
column_transformer = ColumnTransformer(transformers)
steps = [('scaler',RobustScaler()), ('transformer', column_transformer)]
pipeline = Pipeline(steps)

dt_test = np.zeros((1000,2))
pipeline.fit_transform(dt_test)

for name,step in pipeline.named_steps.items():
    if hasattr(step, 'get_feature_names'):
        print(step.get_feature_names())

For the sake of having a more representative example I added a RobustScaler and nested the ColumnTransformer on a Pipeline. By the way, you will find my version of Venkatachalam's way to get the feature name looping of the steps. You can turn it into a slightly more usable variable by unpacking the names with a list comprehension:

[i for i in v.get_feature_names() for k, v in pipeline.named_steps.items() if hasattr(v,'get_feature_names')]

So play around with the dt_test and the estimators to soo how the feature name is built, and how it is concatenated in the get_feature_names(). Here is another example with a transformer which output 2 columns, using the input column:

class testEstimator3(BaseEstimator,TransformerMixin):
    def __init__(self,string):
        self.string = string

    def fit(self,X):
        self.unique = np.unique(X)[0]
        return self

    def transform(self,X):
        return np.concatenate((X.reshape(-1,1), np.full(X.shape,self.string).reshape(-1,1)), axis = 1)

    def get_feature_names(self):
        return list((self.unique,self.string))

dt_test2 = np.concatenate((np.full((1000,1),'A'),np.full((1000,1),'B')), axis = 1)

transformers = [('first_transformer',testEstimator3('A'),1), ('second_transformer',testEstimator3('B'),0)]
column_transformer = ColumnTransformer(transformers)
steps = [('transformer', column_transformer)]
pipeline = Pipeline(steps)

pipeline.fit_transform(dt_test2)
for step in pipeline.steps:
    if hasattr(step[1], 'get_feature_names'):
        print(step[1].get_feature_names())

You can access the feature_names using the following snippet:

clf.named_steps['preprocessor'].transformers_[1][1]\
   .named_steps['onehot'].get_feature_names(categorical_features)

Using sklearn >= 0.21 version, we can make it even simpler:

clf['preprocessor'].transformers_[1][1]\
    ['onehot'].get_feature_names(categorical_features)

Reproducible example:

import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LinearRegression

df = pd.DataFrame({'brand': ['aaaa', 'asdfasdf', 'sadfds', 'NaN'],
                   'category': ['asdf', 'asfa', 'asdfas', 'as'],
                   'num1': [1, 1, 0, 0],
                   'target': [0.2, 0.11, 1.34, 1.123]})

numeric_features = ['num1']
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())])

categorical_features = ['brand', 'category']
categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features),
        ('cat', categorical_transformer, categorical_features)])

clf = Pipeline(steps=[('preprocessor', preprocessor),
                      ('regressor',  LinearRegression())])
clf.fit(df.drop('target', 1), df['target'])

clf.named_steps['preprocessor'].transformers_[1][1]\
   .named_steps['onehot'].get_feature_names(categorical_features)

# ['brand_NaN' 'brand_aaaa' 'brand_asdfasdf' 'brand_sadfds' 'category_as'
#  'category_asdf' 'category_asdfas' 'category_asfa']