SLEP013: n_features_out_ attribute


Adrin Jalali




Standards Track




This SLEP proposes the introduction of a public n_features_out_ attribute for most transformers (where relevant).


Knowing the number of features that a transformer outputs is useful for inspection purposes. This is in conjunction with *SLEP010: ``n_features_in_``*.

Take the following piece as an example:

X, y = fetch_openml("titanic", version=1, as_frame=True, return_X_y=True)

# We will train our classifier with the following features:
# Numeric Features:
# - age: float.
# - fare: float.
# Categorical Features:
# - embarked: categories encoded as strings {'C', 'S', 'Q'}.
# - sex: categories encoded as strings {'female', 'male'}.
# - pclass: ordinal integers {1, 2, 3}.

# We create the preprocessing pipelines for both numeric and categorical data.
numeric_features = ['age', 'fare']
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())])

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

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

# Append classifier to preprocessing pipeline.
# Now we have a full prediction pipeline.
clf = Pipeline(steps=[('preprocessor', preprocessor),
                      ('classifier', LogisticRegression())])

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2), y_train)

The user could then inspect the number of features going out from each step:

# Total number of output features from the `ColumnTransformer`

# Number of features as a result of the numerical pipeline:

# Number of features as a result of the categorical pipeline:


The proposed solution is for the n_features_out_ attribute to be set once a call to fit is done. In many cases the value of n_features_out_ is the same as some other attribute stored in the transformer, e.g. n_components_, and in these cases a Mixin such as a ComponentsMixin can delegate n_features_out_ to those attributes.


A test to the common tests is added to ensure the presence of the attribute or property after calling fit.


The main consideration is that the addition of the common test means that existing estimators in downstream libraries will not pass our test suite, unless the estimators also have the n_features_out_ attribute.

The newly introduced checks will only raise a warning instead of an exception for the next 2 releases, so this will give more time for downstream packages to adjust.

There are other minor considerations:

  • In some meta-estimators, this is delegated to the sub-estimator(s). The n_features_out_ attribute of the meta-estimator is thus explicitly set to that of the sub-estimator, either via a @property, or directly in fit().

  • Some transformers such as FunctionTransformer may not know the number of output features since arbitrary arrays can be passed to transform. In such cases n_features_out_ is set to None.

References and Footnotes