scikit-learn · thomasjpfan · Jan 20, 2022 · Jan 14, 2022 · Jan 14, 2022 · Jan 14, 2022
diff --git a/doc/whats_new/v1.1.rst b/doc/whats_new/v1.1.rst
@@ -423,6 +423,11 @@ Changelog
   ndarray with `np.nan` when passed a `Float32` or `Float64` pandas extension
   array with `pd.NA`. :pr:`21278` by `Thomas Fan`_.
 
+- |Enhancement| Adds :term:`get_feature_names_out` to
+  :class:`neighbors.RadiusNeighborsTransformer`, :class:`neighbors.KNeighborsTransformer`
+  and :class:`neighbors.NeighborhoodComponentsAnalysis`. :pr:`22212` by
+  :user : `Meekail Zain <micky774>`.
+
 - |Fix| :class:`neighbors.KernelDensity` now validates input parameters in `fit`
   instead of `__init__`. :pr:`21430` by :user:`Desislava Vasileva <DessyVV>` and
   :user:`Lucy Jimenez <LucyJimenez>`.

diff --git a/sklearn/neighbors/_graph.py b/sklearn/neighbors/_graph.py
@@ -7,7 +7,7 @@
 from ._base import KNeighborsMixin, RadiusNeighborsMixin
 from ._base import NeighborsBase
 from ._unsupervised import NearestNeighbors
-from ..base import TransformerMixin
+from ..base import TransformerMixin, _ClassNamePrefixFeaturesOutMixin
 from ..utils.validation import check_is_fitted
 
 
@@ -223,7 +223,9 @@ def radius_neighbors_graph(
     return X.radius_neighbors_graph(query, radius, mode)
 
 
-class KNeighborsTransformer(KNeighborsMixin, TransformerMixin, NeighborsBase):
+class KNeighborsTransformer(
+    _ClassNamePrefixFeaturesOutMixin, KNeighborsMixin, TransformerMixin, NeighborsBase
+):
     """Transform X into a (weighted) graph of k nearest neighbors.
 
     The transformed data is a sparse graph as returned by kneighbors_graph.
@@ -389,7 +391,9 @@ def fit(self, X, y=None):
         self : KNeighborsTransformer
             The fitted k-nearest neighbors transformer.
         """
-        return self._fit(X)
+        self._fit(X)
+        self._n_features_out = self.n_samples_fit_
+        return self
 
     def transform(self, X):
         """Compute the (weighted) graph of Neighbors for points in X.
@@ -445,7 +449,12 @@ def _more_tags(self):
         }
 
 
-class RadiusNeighborsTransformer(RadiusNeighborsMixin, TransformerMixin, NeighborsBase):
+class RadiusNeighborsTransformer(
+    _ClassNamePrefixFeaturesOutMixin,
+    RadiusNeighborsMixin,
+    TransformerMixin,
+    NeighborsBase,
+):
     """Transform X into a (weighted) graph of neighbors nearer than a radius.
 
     The transformed data is a sparse graph as returned by
@@ -614,7 +623,9 @@ def fit(self, X, y=None):
         self : RadiusNeighborsTransformer
             The fitted radius neighbors transformer.
         """
-        return self._fit(X)
+        self._fit(X)
+        self._n_features_out = self.n_samples_fit_
+        return self
 
     def transform(self, X):
         """Compute the (weighted) graph of Neighbors for points in X.

diff --git a/sklearn/neighbors/_nca.py b/sklearn/neighbors/_nca.py
@@ -15,7 +15,7 @@
 from scipy.optimize import minimize
 from ..utils.extmath import softmax
 from ..metrics import pairwise_distances
-from ..base import BaseEstimator, TransformerMixin
+from ..base import BaseEstimator, TransformerMixin, _ClassNamePrefixFeaturesOutMixin
 from ..preprocessing import LabelEncoder
 from ..decomposition import PCA
 from ..utils.multiclass import check_classification_targets
@@ -24,7 +24,9 @@
 from ..exceptions import ConvergenceWarning
 
 
-class NeighborhoodComponentsAnalysis(TransformerMixin, BaseEstimator):
+class NeighborhoodComponentsAnalysis(
+    _ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator
+):
     """Neighborhood Components Analysis.
 
     Neighborhood Component Analysis (NCA) is a machine learning algorithm for
@@ -249,6 +251,7 @@ def fit(self, X, y):
 
         # Reshape the solution found by the optimizer
         self.components_ = opt_result.x.reshape(-1, X.shape[1])
+        self._n_features_out = self.components_.shape[1]
 
         # Stop timer
         t_train = time.time() - t_train

diff --git a/sklearn/neighbors/tests/test_graph.py b/sklearn/neighbors/tests/test_graph.py
@@ -1,8 +1,10 @@
 import numpy as np
+import pytest
 
 from sklearn.metrics import euclidean_distances
 from sklearn.neighbors import KNeighborsTransformer, RadiusNeighborsTransformer
 from sklearn.neighbors._base import _is_sorted_by_data
+from sklearn.utils._testing import assert_array_equal
 
 
 def test_transformer_result():
@@ -77,3 +79,23 @@ def test_explicit_diagonal():
     # Using transform on new data should not always have zero diagonal
     X2t = nnt.transform(X2)
     assert not _has_explicit_diagonal(X2t)
+
+
+@pytest.mark.parametrize("Klass", [KNeighborsTransformer, RadiusNeighborsTransformer])
+def test_graph_feature_names_out(Klass):
+    """Check `get_feature_names_out` for transformers defined in `_graph.py`."""
+
+    n_samples_fit = 20
+    n_features = 10
+    rng = np.random.RandomState(42)
+    X = rng.randn(n_samples_fit, n_features)
+
+    est = Klass().fit(X)
+    names_out = est.get_feature_names_out()
+
+    class_name_lower = Klass.__name__.lower()
+    expected_names_out = np.array(
+        [f"{class_name_lower}{i}" for i in range(est.n_samples_fit_)],
+        dtype=object,
+    )
+    assert_array_equal(names_out, expected_names_out)
diff --git a/sklearn/neighbors/tests/test_nca.py b/sklearn/neighbors/tests/test_nca.py
@@ -554,3 +554,20 @@ def test_parameters_valid_types(param, value):
     y = iris_target
 
     nca.fit(X, y)
+
+
+def test_nca_feature_names_out():
+    """Check `get_feature_names_out` for `NeighborhoodComponentsAnalysis`."""
+
+    X = iris_data
+    y = iris_target
+
+    est = NeighborhoodComponentsAnalysis().fit(X, y)
+    names_out = est.get_feature_names_out()
+
+    class_name_lower = est.__class__.__name__.lower()
+    expected_names_out = np.array(
+        [f"{class_name_lower}{i}" for i in range(est.components_.shape[1])],
+        dtype=object,
+    )
+    assert_array_equal(names_out, expected_names_out)
diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
@@ -386,7 +386,6 @@ def test_pandas_column_name_consistency(estimator):
     "kernel_approximation",
     "preprocessing",
     "manifold",
-    "neighbors",
     "neural_network",
 ]