Get feature names after sklearn pipeline

Question

I want to match the output np array with the features to make a new pandas dataframe

Here is my pipeline:

from sklearn.pipeline import Pipeline
# Categorical pipeline
categorical_preprocessing = Pipeline(
[
    ('Imputation', SimpleImputer(missing_values=np.nan, strategy='most_frequent')),
    ('Ordinal encoding', OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)),
]
)
# Continuous pipeline
continuous_preprocessing = Pipeline(
[
     ('Imputation', SimpleImputer(missing_values=np.nan, strategy='mean')),
     ('Scaling', StandardScaler())
]
)
# Creating preprocessing pipeline
preprocessing = make_column_transformer(
     (continuous_preprocessing, continuous_cols),
     (categorical_preprocessing, categorical_cols),
)
# Final pipeline
pipeline = Pipeline(
[('Preprocessing', preprocessing)]
)

Here is how I call it:

X_train = pipeline.fit_transform(X_train)
X_val = pipeline.transform(X_val)
X_test = pipeline.transform(X_test)

Here is what I get when trying to get the feature names:

pipeline['Preprocessing'].transformers_[1][1]['Ordinal encoding'].get_feature_names()

OUT:

AttributeError: 'OrdinalEncoder' object has no attribute 'get_feature_names'

Here is a SO question that was similar: Sklearn Pipeline: Get feature names after OneHotEncode In ColumnTransformer