CODEWITHSUNDEEP
pythonpipelineone-hot-encoding
Toby Chitty
Toby Chitty

Reputation: 13

Loss of feature names when onehotencoding

Building pipelines with onehotencoding and when fitting and transforming to training/test set and converting into data frame it results in the features not having names. Is there any way to get names for each encoded feature?

# Numerical column transformer
num_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', StandardScaler())
])

# Categorical column transformer
cat_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))
])

# Preprocessing pipeline
preprocessor = ColumnTransformer(
    transformers=[
        ('num', num_transformer, numerical_cols),
        ('cat', cat_transformer, categorical_cols)
    ])


# Fitting the data and transforming the training & test set
X_train_preprocessed = preprocessor.fit_transform(X_train)
test_preprocessed = preprocessor.fit_transform(test)

Upvotes: 1

Views: 612

Answers (1)

Vadim Shkaberda
Vadim Shkaberda

Reputation: 2936

You can access transformers using attribute named_transformers_ of ColumnTransformer. You have 2 transformers named 'num' and 'cat', so preprocessor.named_transformers_['cat'] gives you access to your cat_transformer. Then using named_steps attribute of Pipeline you can access your OneHotEncoder named 'onehot' and its categories_ attribute:

X = [['Male', 1], ['Female', 3], ['Female', 2]]

preprocessor.fit_transform(X)
Out[6]: 
array([[-1.22474487,  0.        ,  1.        ],
       [ 1.22474487,  1.        ,  0.        ],
       [ 0.        ,  1.        ,  0.        ]])

preprocessor.named_transformers_['cat'].named_steps['onehot'].categories_
Out[7]: [array(['Female', 'Male'], dtype=object)]

Upvotes: 1

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