Sklearn Pipeline: Get feature names after OneHotEncode In ColumnTransformer

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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']

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