Slep007 - feature names, their generation and the API

slep007/proposal.rst

@@ -0,0 +1,272 @@
+ .. _slep_007:
+
+===========================================
+Feature names, their generation and the API
+===========================================
+
+:Author: Adrin Jalali
+:Status: Under Review
+:Type: Standards Track
+:Created: 2019-04
+
+Abstract
+########
+
+This SLEP proposes the introduction of the ``feature_names_in_`` attribute for
+all estimators, and the ``feature_names_out_`` attribute for all transformers.
+We here discuss the generation of such attributes and their propagation through
+pipelines.
+
+Motivation
+##########
+
+``scikit-learn`` has been making it easier to build complex workflows with the
+``ColumnTransformer`` and it has been seeing widespread adoption. However,
+using it results in pipelines where it's not clear what the input features to
+the final predictor are, even more so than before. For example, after fitting
+the following pipeline, users should ideally be able to inspect the features
+going into the final predictor::
+
+
+    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(
+        transformers=[
+            ('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)
+
+    clf.fit(X_train, y_train)
+
+
+However, it's impossible to interpret or even sanity-check the
+``LogisticRegression`` instance that's produced in the example, because the
+correspondence of the coefficients to the input features is basically
+impossible to figure out.
+
+This proposal suggests adding two attributes to fitted estimators:
+``feature_names_in_`` and ``feature_names_out_``, such that in the
+abovementioned example ``clf[-1].feature_names_in_`` and
+``clf[-2].feature_names_out_`` will be::
+
+    ['num__age',
+     'num__fare',
+     'cat__embarked_C',
+     'cat__embarked_Q',
+     'cat__embarked_S',
+     'cat__embarked_missing',
+     'cat__sex_female',
+     'cat__sex_male',
+     'cat__pclass_1',
+     'cat__pclass_2',
+     'cat__pclass_3']
+
+Ideally the generated feature names describe how a feature is generated at each
+stage of a pipeline. For instance, ``cat__sex_female`` shows that the feature
+has been through a categorical preprocessing pipeline, was originally the
+column ``sex``, and has been one hot encoded and is one if it was originally
+``female``. However, this is not always possible or desirable especially when a
+generated column is based on many columns, since the generated feature names
+will be too long, for example in ``PCA``. As a rule of thumb, the following
+types of transformers may generate feature names which corresponds to the
+original features:
+
+- Leave columns unchanged, *e.g.* ``StandardScaler``
+- Select a subset of columns, *e.g.* ``SelectKBest``
+- create new columns where each column depends on at most one input column,
+  *e.g* ``OneHotEncoder``
+- Algorithms that create combinations of a fixed number of features, *e.g.*
+  ``PolynomialFeatures``, as opposed to all of
+  them where there are many. Note that verbosity considerations and
+  ``verbose_feature_names`` as explained later can apply here.
+
+This proposal talks about how feature names are generated and not how they are
+propagated.
+
+Scope
+#####
+
+The API for input and output feature names includes a ``feature_names_in_``
+attribute for all estimators, and a ``feature_names_out_`` attribute for any
+estimator with a ``transform`` method, *i.e.* they expose the generated feature
+names via the ``feature_names_out_`` attribute.
+
+Note that this SLEP also applies to `resamplers
+<https://github.com/scikit-learn/enhancement_proposals/pull/15>`_ the same way
+as transformers.
+
+Input Feature Names
+###################
+
+The input feature names are stored in a fitted estimator in a
+``feature_names_in_`` attribute, and are taken from the given input data, for
+instance a ``pandas`` data frame. This attribute will be ``None`` if the input
+provides no feature names.
+
+Output Feature Names
+####################
+
+A fitted estimator exposes the output feature names through the
+``feature_names_out_`` attribute. Here we discuss more in detail how these
+feature names are generated. Since for most estimators there are multiple ways
+to generate feature names, this SLEP does not intend to define how exactly
+feature names are generated for all of them. It is instead a guideline on how
+they could generally be generated. Furthermore, that specific behavior of a
+given estimator may be tuned via the ``verbose_feature_names`` parameter, as
+detailed below.
+
+When generating the output feature names, if no input feature names are
+provided, ``x0`` to ``xn`` are assumed to be their names.
+
+Feature Selector Transformers
+*****************************
+
+This includes transformers which output a subset of the input features, w/o
+changing them. For example, if a ``SelectKBest`` transformer selects the first
+and the third features, and no names are provided, the ``feature_names_out_``
+will be ``[x0, x2]``.
+
+Feature Generating Transformers
+*******************************
+
+The simplest category of transformers in this section are the ones which
+generate a column based on a single given column. The generated output column
+in this case is a sensible transformation of the input feature name. For
+instance, a ``LogTransformer`` can do ``'age' -> 'log(age)'``, and a
+``OneHotEncoder`` could do ``'gender' -> 'gender_female', 'gender_fluid',
+...``. An alternative is to leave the feature names unchanged when each output
+feature corresponds to exactly one input feature. Whether or not to modify the
+feature name, *e.g.* ``log(x0)`` vs. ``x0`` may be controlled via the
+``verbose_feature_names`` to the constructor. The default value of
+``verbose_feature_names`` can be different depending on the transformer. For
+instance, ``StandardScaler`` can have it as ``False``, whereas
+``LogTransformer`` could have it as ``True`` by default.
+
+Transformers where each output feature depends on a fixed number of input
+features may generate descriptive names as well. For instance, a
+``PolynomialTransformer`` on a small subset of features can generate an output
+feature name such as ``x[0] * x[2] ** 3``.
+
+And finally, the transformers where each output feature depends on many or all
+input features, generate feature names which has the form of ``name0`` to
+``namen``, where ``name`` represents the transformer. For instance, a ``PCA``
+transformer will output ``[pca0, ..., pcan]``, ``n`` being the number of PCA
+components.
+
+Meta-Estimators
+***************
+
+Meta estimators can choose to prefix the output feature names given by the
+estimators they are wrapping or not.
+
+By default, ``Pipeline`` adds no prefix, *i.e* its ``feature_names_out_`` is
+the same as the ``feature_names_out_`` of the last step, and ``None`` if the
+last step is not a transformer.
+
+``ColumnTransformer`` by default adds a prefix to the output feature names,
+indicating the name of the transformer applied to them. If a column is in the output
+as a part of ``passthrough``, it won't be prefixed since no operation has been
+applied on it.
+
+This is the default behavior, and it can be tuned by constructor parameters if
+the meta estimator allows it. For instance, a ``verbose_feature_names=False``
+may indicate that a ``ColumnTransformer`` should not prefix the generated
+feature names with the name of the step.
+
+Examples
+########
+
+Here we include some examples to demonstrate the behavior of output feature
+names::
+
+    100 features (no names) -> PCA(n_components=3)
+    feature_names_out_: [pca0, pca1, pca2]
+
+
+    100 features (no names) -> SelectKBest(k=3)
+    feature_names_out_: [x2, x17, x42]
+
+
+    [f1, ..., f100] -> SelectKBest(k=3)
+    feature_names_out_: [f2, f17, f42]
+
+
+    [cat0] -> OneHotEncoder()
+    feature_names_out_: [cat0_cat, cat0_dog, ...]
+
+
+    [f1, ..., f100] -> Pipeline(
+                           [SelectKBest(k=30),
+                            PCA(n_components=3)]
+                       )
+    feature_names_out_: [pca0, pca1, pca2]
+
+
+    [model, make, numeric0, ..., numeric100] ->
+        ColumnTransformer(
+            [('cat', Pipeline(SimpleImputer(), OneHotEncoder()),
+              ['model', 'make']),
+             ('num', Pipeline(SimpleImputer(), PCA(n_components=3)),
+              ['numeric0', ..., 'numeric100'])]
+        )
+    feature_names_out_: ['cat_model_100', 'cat_model_200', ...,
+                         'cat_make_ABC', 'cat_make_XYZ', ...,
+                         'num_pca0', 'num_pca1', 'num_pca2']
+
+However, the following examples produce a somewhat redundant feature names,
+and hence the relevance of ``verbose_feature_names=False``::
+
+    [model, make, numeric0, ..., numeric100] ->
+        ColumnTransformer([
+            ('ohe', OneHotEncoder(), ['model', 'make']),
+            ('pca', PCA(n_components=3), ['numeric0', ..., 'numeric100'])
+        ])
+    feature_names_out_: ['ohe_model_100', 'ohe_model_200', ...,
+                         'ohe_make_ABC', 'ohe_make_XYZ', ...,
+                         'pca_pca0', 'pca_pca1', 'pca_pca2']
+
+If desired, the user can remove the prefixes::
+
+    [model, make, numeric0, ..., numeric100] ->
+        make_column_transformer(
+            (OneHotEncoder(), ['model', 'make']),
+            (PCA(n_components=3), ['numeric0', ..., 'numeric100']),
+            verbose_feature_names=False
+        )
+    feature_names_out_: ['model_100', 'model_200', ...,
+                         'make_ABC', 'make_XYZ', ...,
+                         'pca0', 'pca1', 'pca2']
+
+Backward Compatibility
+######################
+
+All estimators should implement the ``feature_names_in_`` and
+``feature_names_out_`` API. This is checked in ``check_estimator``, and the
+transition is done with a ``FutureWarning`` for at least two versions to give
+time to third party developers to implement the API.

under_review.rst

@@ -1,11 +1,11 @@
 SLEPs under review
 ==================
 
-No SLEP is currently under review.
+.. No SLEP is currently under review.
 
 .. Uncomment below when a SLEP is under review
 
-.. .. toctree::
-..     :maxdepth: 1
+.. toctree::
+    :maxdepth: 1
 
-..     slepXXX/proposal
+    slep007/proposal