API Change ColumnTransformer parameter name to verbose_feature_names_…

…out (#21080)

doc/modules/compose.rst

@@ -449,13 +449,13 @@ By default, the remaining rating columns are ignored (``remainder='drop'``)::
   >>> column_trans = ColumnTransformer(
   ...     [('categories', OneHotEncoder(dtype='int'), ['city']),
   ...      ('title_bow', CountVectorizer(), 'title')],
-  ...     remainder='drop', prefix_feature_names_out=False)
+  ...     remainder='drop', verbose_feature_names_out=False)
 
   >>> column_trans.fit(X)
-  ColumnTransformer(prefix_feature_names_out=False,
-                    transformers=[('categories', OneHotEncoder(dtype='int'),
+  ColumnTransformer(transformers=[('categories', OneHotEncoder(dtype='int'),
                                    ['city']),
-                                  ('title_bow', CountVectorizer(), 'title')])
+                                  ('title_bow', CountVectorizer(), 'title')],
+                    verbose_feature_names_out=False)
 
   >>> column_trans.get_feature_names_out()
   array(['city_London', 'city_Paris', 'city_Sallisaw', 'bow', 'feast',

doc/whats_new/v1.0.rst

@@ -251,9 +251,9 @@ Changelog
   an adequate transformer.
   :pr:`18898` by :user:`Oras Phongpanagnam <panangam>`.
 
-- |API| Adds `prefix_feature_names_out` to :class:`compose.ColumnTransformer`.
+- |API| Adds `verbose_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`_.
+  :term:`get_feature_names_out`. :pr:`18444` and :pr:`21080` by `Thomas Fan`_.
 
 :mod:`sklearn.covariance`
 .........................

examples/inspection/plot_linear_model_coefficient_interpretation.py

@@ -135,7 +135,7 @@
 preprocessor = make_column_transformer(
     (OneHotEncoder(drop="if_binary"), categorical_columns),
     remainder="passthrough",
-    prefix_feature_names_out=False,
+    verbose_feature_names_out=False,
 )
 
 # %%

sklearn/compose/_column_transformer.py

@@ -112,7 +112,7 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):
         If True, the time elapsed while fitting each transformer will be
         printed as it is completed.
 
-    prefix_feature_names_out : bool, default=True
+    verbose_feature_names_out : bool, default=True
         If True, :meth:`get_feature_names_out` will prefix all feature names
         with the name of the transformer that generated that feature.
         If False, :meth:`get_feature_names_out` will not prefix any feature
@@ -204,15 +204,15 @@ def __init__(
         n_jobs=None,
         transformer_weights=None,
         verbose=False,
-        prefix_feature_names_out=True,
+        verbose_feature_names_out=True,
     ):
         self.transformers = transformers
         self.remainder = remainder
         self.sparse_threshold = sparse_threshold
         self.n_jobs = n_jobs
         self.transformer_weights = transformer_weights
         self.verbose = verbose
-        self.prefix_feature_names_out = prefix_feature_names_out
+        self.verbose_feature_names_out = verbose_feature_names_out
 
     @property
     def _transformers(self):
@@ -489,7 +489,7 @@ def get_feature_names_out(self, input_features=None):
             # No feature names
             return np.array([], dtype=object)
 
-        if self.prefix_feature_names_out:
+        if self.verbose_feature_names_out:
             # Prefix the feature names out with the transformers name
             names = list(
                 chain.from_iterable(
@@ -499,7 +499,7 @@ def get_feature_names_out(self, input_features=None):
             )
             return np.asarray(names, dtype=object)
 
-        # prefix_feature_names_out is False
+        # verbose_feature_names_out is False
         # Check that names are all unique without a prefix
         feature_names_count = Counter(
             chain.from_iterable(s for _, s in transformer_with_feature_names_out)
@@ -517,7 +517,7 @@ def get_feature_names_out(self, input_features=None):
                 names_repr = str(top_6_overlap)
             raise ValueError(
                 f"Output feature names: {names_repr} are not unique. Please set "
-                "prefix_feature_names_out=True to add prefixes to feature names"
+                "verbose_feature_names_out=True to add prefixes to feature names"
             )
 
         return np.concatenate(
@@ -856,7 +856,7 @@ def make_column_transformer(
     sparse_threshold=0.3,
     n_jobs=None,
     verbose=False,
-    prefix_feature_names_out=True,
+    verbose_feature_names_out=True,
 ):
     """Construct a ColumnTransformer from the given transformers.
 
@@ -919,7 +919,7 @@ def make_column_transformer(
         If True, the time elapsed while fitting each transformer will be
         printed as it is completed.
 
-    prefix_feature_names_out : bool, default=True
+    verbose_feature_names_out : bool, default=True
         If True, :meth:`get_feature_names_out` will prefix all feature names
         with the name of the transformer that generated that feature.
         If False, :meth:`get_feature_names_out` will not prefix any feature
@@ -959,7 +959,7 @@ def make_column_transformer(
         remainder=remainder,
         sparse_threshold=sparse_threshold,
         verbose=verbose,
-        prefix_feature_names_out=prefix_feature_names_out,
+        verbose_feature_names_out=verbose_feature_names_out,
     )
 
 

sklearn/compose/tests/test_column_transformer.py

@@ -709,7 +709,7 @@ def test_column_transformer_get_set_params():
         "trans2__with_std": True,
         "transformers": ct.transformers,
         "transformer_weights": None,
-        "prefix_feature_names_out": True,
+        "verbose_feature_names_out": True,
         "verbose": False,
     }
 
@@ -730,7 +730,7 @@ def test_column_transformer_get_set_params():
         "trans2__with_std": True,
         "transformers": ct.transformers,
         "transformer_weights": None,
-        "prefix_feature_names_out": True,
+        "verbose_feature_names_out": True,
         "verbose": False,
     }
 
@@ -1149,7 +1149,7 @@ def test_column_transformer_get_set_params_with_remainder():
         "trans1__with_std": True,
         "transformers": ct.transformers,
         "transformer_weights": None,
-        "prefix_feature_names_out": True,
+        "verbose_feature_names_out": True,
         "verbose": False,
     }
 
@@ -1169,7 +1169,7 @@ def test_column_transformer_get_set_params_with_remainder():
         "trans1": "passthrough",
         "transformers": ct.transformers,
         "transformer_weights": None,
-        "prefix_feature_names_out": True,
+        "verbose_feature_names_out": True,
         "verbose": False,
     }
     assert ct.get_params() == exp
@@ -1760,8 +1760,8 @@ def get_feature_names_out(self, input_features=None):
         ),
     ],
 )
-def test_feature_names_out_prefix_true(transformers, remainder, expected_names):
-    """Check feature_names_out for prefix_feature_names_out==True (default)"""
+def test_verbose_feature_names_out_true(transformers, remainder, expected_names):
+    """Check feature_names_out for verbose_feature_names_out=True (default)"""
     pd = pytest.importorskip("pandas")
     df = pd.DataFrame([[1, 2, 3, 4]], columns=["a", "b", "c", "d"])
     ct = ColumnTransformer(
@@ -1835,14 +1835,14 @@ def test_feature_names_out_prefix_true(transformers, remainder, expected_names):
         ),
     ],
 )
-def test_feature_names_out_prefix_false(transformers, remainder, expected_names):
-    """Check feature_names_out for prefix_feature_names_out==True (default)"""
+def test_verbose_feature_names_out_false(transformers, remainder, expected_names):
+    """Check feature_names_out for verbose_feature_names_out=False"""
     pd = pytest.importorskip("pandas")
     df = pd.DataFrame([[1, 2, 3, 4]], columns=["a", "b", "c", "d"])
     ct = ColumnTransformer(
         transformers,
         remainder=remainder,
-        prefix_feature_names_out=False,
+        verbose_feature_names_out=False,
     )
     ct.fit(df)
 
@@ -1923,23 +1923,23 @@ def test_feature_names_out_prefix_false(transformers, remainder, expected_names)
         ),
     ],
 )
-def test_feature_names_out_prefix_false_errors(
+def test_verbose_feature_names_out_false_errors(
     transformers, remainder, colliding_columns
 ):
-    """Check feature_names_out for prefix_feature_names_out==False"""
+    """Check feature_names_out for verbose_feature_names_out=False"""
 
     pd = pytest.importorskip("pandas")
     df = pd.DataFrame([[1, 2, 3, 4]], columns=["a", "b", "c", "d"])
     ct = ColumnTransformer(
         transformers,
         remainder=remainder,
-        prefix_feature_names_out=False,
+        verbose_feature_names_out=False,
     )
     ct.fit(df)
 
     msg = re.escape(
         f"Output feature names: {colliding_columns} are not unique. Please set "
-        "prefix_feature_names_out=True to add prefixes to feature names"
+        "verbose_feature_names_out=True to add prefixes to feature names"
     )
     with pytest.raises(ValueError, match=msg):
         ct.get_feature_names_out()