scikit-learn · ogrisel · Sep 7, 2021 · Nov 20, 2018 · Nov 20, 2018 · Nov 20, 2018
diff --git a/doc/glossary.rst b/doc/glossary.rst
@@ -894,6 +894,7 @@ Class APIs and Estimator Types
         * :term:`fit`
         * :term:`transform`
         * :term:`get_feature_names`
+        * :term:`get_feature_names_out`
 
     meta-estimator
     meta-estimators
@@ -1262,6 +1263,17 @@ Methods
         to the names of input columns from which output column names can
         be generated.  By default input features are named x0, x1, ....
 
+    ``get_feature_names_out``
+        Primarily for :term:`feature extractors`, but also used for other
+        transformers to provide string names for each column in the output of
+        the estimator's :term:`transform` method.  It outputs an array of
+        strings and may take an array-like of strings as input, corresponding
+        to the names of input columns from which output column names can
+        be generated.  If `input_features` is not passed in, then the
+        `feature_names_in_` attribute will be used. If the
+        `feature_names_in_` attribute is not defined, then the
+        input names are named `[x0, x1, ..., x(n_features_in_)]`.
+
     ``get_n_splits``
         On a :term:`CV splitter` (not an estimator), returns the number of
         elements one would get if iterating through the return value of

diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst
@@ -139,6 +139,27 @@ or by name::
     >>> pipe['reduce_dim']
     PCA()
 
+To enable model inspection, :class:`~sklearn.pipeline.Pipeline` has a
+``get_feature_names_out()`` method, just like all transformers. You can use
+pipeline slicing to get the feature names going into each step::
+
+    >>> from sklearn.datasets import load_iris
+    >>> from sklearn.feature_selection import SelectKBest
+    >>> iris = load_iris()
+    >>> pipe = Pipeline(steps=[
+    ...    ('select', SelectKBest(k=2)),
+    ...    ('clf', LogisticRegression())])
+    >>> pipe.fit(iris.data, iris.target)
+    Pipeline(steps=[('select', SelectKBest(...)), ('clf', LogisticRegression(...))])
+    >>> pipe[:-1].get_feature_names_out()
+    array(['x2', 'x3'], ...)
+
+You can also provide custom feature names for the input data using
+``get_feature_names_out``::
+
+    >>> pipe[:-1].get_feature_names_out(iris.feature_names)
+    array(['petal length (cm)', 'petal width (cm)'], ...)
+
 .. topic:: Examples:
 
  * :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py`
@@ -426,21 +447,20 @@ By default, the remaining rating columns are ignored (``remainder='drop'``)::
   >>> from sklearn.feature_extraction.text import CountVectorizer
   >>> from sklearn.preprocessing import OneHotEncoder
   >>> column_trans = ColumnTransformer(
-  ...     [('city_category', OneHotEncoder(dtype='int'),['city']),
+  ...     [('categories', OneHotEncoder(dtype='int'), ['city']),
   ...      ('title_bow', CountVectorizer(), 'title')],
-  ...     remainder='drop')
+  ...     remainder='drop', prefix_feature_names_out=False)
 
   >>> column_trans.fit(X)
-  ColumnTransformer(transformers=[('city_category', OneHotEncoder(dtype='int'),
+  ColumnTransformer(prefix_feature_names_out=False,
+                    transformers=[('categories', OneHotEncoder(dtype='int'),
                                    ['city']),
                                   ('title_bow', CountVectorizer(), 'title')])
 
-  >>> column_trans.get_feature_names()
-  ['city_category__x0_London', 'city_category__x0_Paris', 'city_category__x0_Sallisaw',
-  'title_bow__bow', 'title_bow__feast', 'title_bow__grapes', 'title_bow__his',
-  'title_bow__how', 'title_bow__last', 'title_bow__learned', 'title_bow__moveable',
-  'title_bow__of', 'title_bow__the', 'title_bow__trick', 'title_bow__watson',
-  'title_bow__wrath']
+  >>> column_trans.get_feature_names_out()
+  array(['city_London', 'city_Paris', 'city_Sallisaw', 'bow', 'feast',
+  'grapes', 'his', 'how', 'last', 'learned', 'moveable', 'of', 'the',
+   'trick', 'watson', 'wrath'], ...)
 
   >>> column_trans.transform(X).toarray()
   array([[1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],

diff --git a/doc/modules/feature_extraction.rst b/doc/modules/feature_extraction.rst
@@ -1,4 +1,4 @@
-.. _feature_extraction:
+.. _feature_extraction:
 
 ==================
 Feature extraction
@@ -53,8 +53,8 @@ is a traditional numerical feature::
          [ 0.,  1.,  0., 12.],
          [ 0.,  0.,  1., 18.]])
 
-  >>> vec.get_feature_names()
-  ['city=Dubai', 'city=London', 'city=San Francisco', 'temperature']
+  >>> vec.get_feature_names_out()
+  array(['city=Dubai', 'city=London', 'city=San Francisco', 'temperature'], ...)
 
 :class:`DictVectorizer` accepts multiple string values for one
 feature, like, e.g., multiple categories for a movie.
@@ -69,10 +69,9 @@ and its year of release.
     array([[0.000e+00, 1.000e+00, 0.000e+00, 1.000e+00, 2.003e+03],
            [1.000e+00, 0.000e+00, 1.000e+00, 0.000e+00, 2.011e+03],
            [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.974e+03]])
-    >>> vec.get_feature_names() == ['category=animation', 'category=drama',
-    ...                             'category=family', 'category=thriller',
-    ...                             'year']
-    True
+    >>> vec.get_feature_
10000
names_out()
+    array(['category=animation', 'category=drama', 'category=family',
+           'category=thriller', 'year'], ...)
     >>> vec.transform({'category': ['thriller'],
     ...                'unseen_feature': '3'}).toarray()
     array([[0., 0., 0., 1., 0.]])
@@ -111,8 +110,9 @@ suitable for feeding into a classifier (maybe after being piped into a
       with 6 stored elements in Compressed Sparse ... format>
   >>> pos_vectorized.toarray()
   array([[1., 1., 1., 1., 1., 1.]])
-  >>> vec.get_feature_names()
-  ['pos+1=PP', 'pos-1=NN', 'pos-2=DT', 'word+1=on', 'word-1=cat', 'word-2=the']
+  >>> vec.get_feature_names_out()
+  array(['pos+1=PP', 'pos-1=NN', 'pos-2=DT', 'word+1=on', 'word-1=cat',
+         'word-2=the'], ...)
 
 As you can imagine, if one extracts such a context around each individual
 word of a corpus of documents the resulting matrix will be very wide
@@ -340,10 +340,9 @@ Each term found by the analyzer during the fit is assigned a unique
 integer index corresponding to a column in the resulting matrix. This
 interpretation of the columns can be retrieved as follows::
 
-  >>> vectorizer.get_feature_names() == (
-  ...     ['and', 'document', 'first', 'is', 'one',
-  ...      'second', 'the', 'third', 'this'])
-  True
+  >>> vectorizer.get_feature_names_out()
+  array(['and', 'document', 'first', 'is', 'one', 'second', 'the',
+         'third', 'this'], ...)
 
   >>> X.toarray()
   array([[0, 1, 1, 1, 0, 0, 1, 0, 1],
@@ -406,8 +405,8 @@ however, similar words are useful for prediction, such as in classifying
 writing style or personality.
 
 There are several known issues in our provided 'english' stop word list. It
-does not aim to be a general, 'one-size-fits-all' solution as some tasks 
-may require a more custom solution. See [NQY18]_ for more details. 
+does not aim to be a general, 'one-size-fits-all' solution as some tasks
+may require a more custom solution. See [NQY18]_ for more details.
 
 Please take care in choosing a stop word list.
 Popular stop word lists may include words that are highly informative to
@@ -742,9 +741,8 @@ decide better::
 
   >>> ngram_vectorizer = CountVectorizer(analyzer='char_wb', ngram_range=(2, 2))
   >>> counts = ngram_vectorizer.fit_transform(['words', 'wprds'])
-  >>> ngram_vectorizer.get_feature_names() == (
-  ...     [' w', 'ds', 'or', 'pr', 'rd', 's ', 'wo', 'wp'])
-  True
+  >>> ngram_vectorizer.get_feature_names_out()
+  array([' w', 'ds', 'or', 'pr', 'rd', 's ', 'wo', 'wp'], ...)
   >>> counts.toarray().astype(int)
   array([[1, 1, 1, 0, 1, 1, 1, 0],
          [1, 1, 0, 1, 1, 1, 0, 1]])
@@ -758,17 +756,15 @@ span across words::
   >>> ngram_vectorizer.fit_transform(['jumpy fox'])
   <1x4 sparse matrix of type '<... 'numpy.int64'>'
      with 4 stored elements in Compressed Sparse ... format>
-  >>> ngram_vectorizer.get_feature_names() == (
-  ...     [' fox ', ' jump', 'jumpy', 'umpy '])
-  True
+  >>> ngram_vectorizer.get_feature_names_out()
+  array([' fox ', ' jump', 'jumpy', 'umpy '], ...)
 
   >>> ngram_vectorizer = CountVectorizer(analyzer='char', ngram_range=(5, 5))
   >>> ngram_vectorizer.fit_transform(['jumpy fox'])
   <1x5 sparse matrix of type '<... 'numpy.int64'>'
       with 5 stored elements in Compressed Sparse ... format>
-  >>> ngram_vectorizer.get_feature_names() == (
-  ...     ['jumpy', 'mpy f', 'py fo', 'umpy ', 'y fox'])
-  True
+  >>> ngram_vectorizer.get_feature_names_out()
+  array(['jumpy', 'mpy f', 'py fo', 'umpy ', 'y fox'], ...)
 
 The word boundaries-aware variant ``char_wb`` is especially interesting
 for languages that use white-spaces for word separation as it generates

diff --git a/doc/whats_new/v1.0.rst b/doc/whats_new/v1.0.rst
@@ -134,6 +134,9 @@ Changelog
 - |API| `np.matrix` usage is deprecated in 1.0 and will raise a `TypeError` in
   1.2. :pr:`20165` by `Thomas Fan`_.
 
+- |API| :term:`get_feature_names_out` has been added to the transformer API
+  to get the names of the output features. :pr:`18444` by `Thomas Fan`_.
+
 - |API| All estimators store `feature_names_in_` when fitted on pandas Dataframes.
   These feature names are compared to names seen in `non-fit` methods,
   `i.e.` `transform` and will raise a `FutureWarning` if they are not consistent.
@@ -221,6 +224,10 @@ Changelog
 :mod:`sklearn.compose`
 ......................
 
+- |API| Adds `prefix_feature_names_out` to :class:`compose.ColumnTransformer`.
+  This flag controls the prefixing of feature names out in
+  :term:`get_feature_names_out`. :pr:`18444` by `Thomas Fan`_.
+
 - |Enhancement| :class:`compose.ColumnTransformer` now records the output
   of each transformer in `output_indices_`. :pr:`18393` by
   :user:`Luca Bittarello <lbittarello>`.

diff --git a/examples/applications/plot_topics_extraction_with_nmf_lda.py b/examples/applications/plot_topics_extraction_with_nmf_lda.py
@@ -103,7 +103,7 @@ def plot_top_words(model, feature_names, n_top_words, title):
 print("done in %0.3fs." % (time() - t0))
 
 
-tfidf_feature_names = tfidf_vectorizer.get_feature_names()
+tfidf_feature_names = tfidf_vectorizer.get_feature_names_out()
 plot_top_words(nmf, tfidf_feature_names, n_top_words,
                'Topics in NMF model (Frobenius norm)')
 
@@ -117,7 +117,7 @@ def plot_top_words(model, feature_names, n_top_words, title):
           l1_ratio=.5).fit(tfidf)
 print("done in %0.3fs." % (time() - t0))
 
-tfidf_feature_names = tfidf_vectorizer.get_feature_names()
+tfidf_feature_names = tfidf_vectorizer.get_feature_names_out()
 plot_top_words(nmf, tfidf_feature_names, n_top_words,
                'Topics in NMF model (generalized Kullback-Leibler divergence)')
 
@@ -132,5 +132,5 @@ def plot_top_words(model, feature_names, n_top_words, title):
 lda.fit(tf)
 print("done in %0.3fs." % (time() - t0))
 
-tf_feature_names = tf_vectorizer.get_feature_names()
+tf_feature_names = tf_vectorizer.get_feature_names_out()
 plot_top_words(lda, tf_feature_names, n_top_words, 'Topics in LDA model')
diff --git a/examples/bicluster/plot_bicluster_newsgroups.py b/examples/bicluster/plot_bicluster_newsgroups.py
@@ -89,7 +89,7 @@ def build_tokenizer(self):
     time() - start_time,
     v_measure_score(y_kmeans, y_true)))
 
-feature_names = vectorizer.get_feature_names()
+feature_names = vectorizer.get_feature_names_out()
 document_names = list(newsgroups.target_names[i] for i in newsgroups.target)
 
 

diff --git a/examples/inspection/plot_linear_model_coefficient_interpretation.py b/examples/inspection/plot_linear_model_coefficient_interpretation.py
@@ -133,7 +133,9 @@
 numerical_columns = ["EDUCATION", "EXPERIENCE", "AGE"]
 
 preprocessor = make_column_transformer(
-    (OneHotEncoder(drop="if_binary"), categorical_columns), remainder="passthrough"
+    (OneHotEncoder(drop="if_binary"), categorical_columns),
+    remainder="passthrough",
+    prefix_feature_names_out=False,
 )
 
 # %%
@@ -199,13 +201,7 @@
 #
 # First of all, we can take a look to the values of the coefficients of the
 # regressor we have fitted.
-
-feature_names = (
-    model.named_steps["columntransformer"]
-    .named_transformers_["onehotencoder"]
-    .get_feature_names(input_features=categorical_columns)
-)
-feature_names = np.concatenate([feature_names, numerical_columns])
+feature_names = model[:-1].get_feature_names_out()
 
 coefs = pd.DataFrame(
     model.named_steps["transformedtargetregressor"].regressor_.coef_,

diff --git a/examples/inspection/plot_permutation_importance.py b/examples/inspection/plot_permutation_importance.py
@@ -120,7 +120,7 @@
 #   capacity).
 ohe = (rf.named_steps['preprocess']
          .named_transformers_['cat'])
-feature_names = ohe.get_feature_names(input_features=categorical_columns)
+feature_names = ohe.get_feature_names_out(categorical_columns)
 feature_names = np.r_[feature_names, numerical_columns]
 
 tree_feature_importances = (

diff --git a/examples/text/plot_document_classification_20newsgroups.py b/examples/text/plot_document_classification_20newsgroups.py
@@ -174,7 +174,7 @@ def size_mb(docs):
 if opts.use_hashing:
     feature_names = None
 else:
-    feature_names = vectorizer.get_feature_names()
+    feature_names = vectorizer.get_feature_names_out()
 
 if opts.select_chi2:
     print("Extracting %d best features by a chi-squared test" %
@@ -183,16 +183,12 @@ def size_mb(docs):
     ch2 = SelectKBest(chi2, k=opts.select_chi2)
     X_train = ch2.fit_transform(X_train, y_train)
     X_test = ch2.transform(X_test)
-    if feature_names:
+    if feature_names is not None:
         # keep selected feature names
-        feature_names = [feature_names[i] for i
-                         in ch2.get_support(indices=True)]
+        feature_names = feature_names[ch2.get_support()]
     print("done in %fs" % (time() - t0))
     print()
 
-if feature_names:
-    feature_names = np.asarray(feature_names)
-
 
 def trim(s):
     """Trim string to fit on terminal (assuming 80-column display)"""

diff --git a/examples/text/plot_document_clustering.py b/examples/text/plot_document_clustering.py
@@ -217,7 +217,7 @@ def is_interactive():
     else:
         order_centroids = km.cluster_centers_.argsort()[:, ::-1]
 
-    terms = vectorizer.get_feature_names()
+    terms = vectorizer.get_feature_names_out()
     for i in range(true_k):
         print("Cluster %d:" % i, end='')
         for ind in order_centroids[i, :10]:

diff --git a/examples/text/plot_hashing_vs_dict_vectorizer.py b/examples/text/plot_hashing_vs_dict_vectorizer.py
@@ -89,7 +89,7 @@ def token_freqs(doc):
 vectorizer.fit_transform(token_freqs(d) for d in raw_data)
 duration = time() - t0
 print("done in %fs at %0.3fMB/s" % (duration, data_size_mb / duration))
-print("Found %d unique terms" % len(vectorizer.get_feature_names()))
+print("Found %d unique terms" % len(vectorizer.get_feature_names_out()))
 print()
 
 print("FeatureHasher on frequency dicts")

diff --git a/sklearn/base.py b/sklearn/base.py
@@ -23,6 +23,7 @@
 from .utils.validation import check_array
 from .utils.validation import _check_y
 from .utils.validation import _num_features
+from .utils.validation import _check_feature_names_in
 from .utils._estimator_html_repr import estimator_html_repr
 from .utils.validation import _get_feature_names
 
@@ -846,6 +847,35 @@ def fit_transform(self, X, y=None, **fit_params):
             return self.fit(X, y, **fit_params).transform(X)
 
 
+class _OneToOneFeatureMixin:
+    """Provides `get_feature_names_out` for simple transformers.
+
+    Assumes there's a 1-to-1 correspondence between input features
+    and output features.
+    """
+
+    def get_feature_names_out(self, input_features=None):
+        """Get output feature names for transformation.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Input features.
+
+            - If `input_features` is `None`, then `feature_names_in_` is
+              used as feature names in. If `feature_names_in_` is not defined,
+              then names are generated: `[x0, x1, ..., x(n_features_in_)]`.
+            - If `input_features` is an array-like, then `input_features` must
+              match `feature_names_in_` if `feature_names_in_` is defined.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            Same as input features.
+        """
+        return _check_feature_names_in(self, input_features)
+
+
 class DensityMixin:
     """Mixin class for all density estimators in scikit-learn."""