scikit-learn · jeremiedbb · Jan 28, 2022 · Jan 20, 2022 · Jan 20, 2022 · Jan 20, 2022
diff --git a/doc/whats_new/v1.1.rst b/doc/whats_new/v1.1.rst
@@ -413,6 +413,9 @@ Changelog
   preserve this dtype.
   :pr:`21534` by :user:`Andrew Knyazev <lobpcg>`.
 
+- |Enhancement| Adds `get_feature_names_out` to :class:`manifold.Isomap`
+  and :class:`manifold.LocallyLinearEmbedding`. :pr:`22254` by `Thomas Fan`_.
+
 - |Fix| :func:`manifold.spectral_embedding` now uses Gaussian instead of
   the previous uniform on [0, 1] random initial approximations to eigenvectors
   in eigen_solvers `lobpcg` and `amg` to improve their numerical stability.

diff --git a/sklearn/manifold/_isomap.py b/sklearn/manifold/_isomap.py
@@ -10,7 +10,7 @@
 from scipy.sparse.csgraph import shortest_path
 from scipy.sparse.csgraph import connected_components
 
-from ..base import BaseEstimator, TransformerMixin
+from ..base import BaseEstimator, TransformerMixin, _ClassNamePrefixFeaturesOutMixin
 from ..neighbors import NearestNeighbors, kneighbors_graph
 from ..utils.validation import check_is_fitted
 from ..decomposition import KernelPCA
@@ -19,7 +19,7 @@
 from ..externals._packaging.version import parse as parse_version
 
 
-class Isomap(TransformerMixin, BaseEstimator):
+class Isomap(_ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
     """Isomap Embedding.
 
     Non-linear dimensionality reduction through Isometric Mapping
@@ -257,6 +257,7 @@ def _fit_transform(self, X):
         G *= -0.5
 
         self.embedding_ = self.kernel_pca_.fit_transform(G)
+        self._n_features_out = self.embedding_.shape[1]
 
     def reconstruction_error(self):
         """Compute the reconstruction error for the embedding.

diff --git a/sklearn/manifold/_locally_linear.py b/sklearn/manifold/_locally_linear.py
@@ -9,7 +9,12 @@
 from scipy.sparse import eye, csr_matrix
 from scipy.sparse.linalg import eigsh
 
-from ..base import BaseEstimator, TransformerMixin, _UnstableArchMixin
+from ..base import (
+    BaseEstimator,
+    TransformerMixin,
+    _UnstableArchMixin,
+    _ClassNamePrefixFeaturesOutMixin,
+)
 from ..utils import check_random_state, check_array
 from ..utils._arpack import _init_arpack_v0
 from ..utils.extmath import stable_cumsum
@@ -542,7 +547,12 @@ def locally_linear_embedding(
     )
 
 
-class LocallyLinearEmbedding(TransformerMixin, _UnstableArchMixin, BaseEstimator):
+class LocallyLinearEmbedding(
+    _ClassNamePrefixFeaturesOutMixin,
+    TransformerMixin,
+    _UnstableArchMixin,
+    BaseEstimator,
+):
     """Locally Linear Embedding.
 
     Read more in the :ref:`User Guide <locally_linear_embedding>`.
@@ -728,6 +738,7 @@ def _fit_transform(self, X):
             reg=self.reg,
             n_jobs=self.n_jobs,
         )
+        self._n_features_out = self.embedding_.shape[1]
 
     def fit(self, X, y=None):
         """Compute the embedding vectors for data X.

diff --git a/sklearn/manifold/tests/test_isomap.py b/sklearn/manifold/tests/test_isomap.py
@@ -1,13 +1,18 @@
 from itertools import product
 import numpy as np
-from numpy.testing import assert_almost_equal, assert_array_almost_equal
+from numpy.testing import (
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_equal,
+)
 import pytest
 
 from sklearn import datasets
 from sklearn import manifold
 from sklearn import neighbors
 from sklearn import pipeline
 from sklearn import preprocessing
+from sklearn.datasets import make_blobs
 from sklearn.metrics.pairwise import pairwise_distances
 
 from scipy.sparse import rand as sparse_rand
@@ -220,3 +225,14 @@ def test_multiple_connected_components_metric_precomputed():
     X_graph = neighbors.kneighbors_graph(X, n_neighbors=2, mode="distance")
     with pytest.raises(RuntimeError, match="number of connected components"):
         manifold.Isomap(n_neighbors=1, metric="precomputed").fit(X_graph)
+
+
+def test_get_feature_names_out():
+    """Check get_feature_names_out for Isomap."""
+    X, y = make_blobs(random_state=0, n_features=4)
+    n_components = 2
+
+    iso = manifold.Isomap(n_components=n_components)
+    iso.fit_transform(X)
+    names = iso.get_feature_names_out()
+    assert_array_equal([f"isomap{i}" for i in range(n_components)], names)
diff --git a/sklearn/manifold/tests/test_locally_linear.py b/sklearn/manifold/tests/test_locally_linear.py
@@ -1,11 +1,16 @@
 from itertools import product
 
 import numpy as np
-from numpy.testing import assert_almost_equal, assert_array_almost_equal
+from numpy.testing import (
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_equal,
+)
 from scipy import linalg
 import pytest
 
 from sklearn import neighbors, manifold
+from sklearn.datasets import make_blobs
 from sklearn.manifold._locally_linear import barycenter_kneighbors_graph
 from sklearn.utils._testing import ignore_warnings
 
@@ -159,3 +164,16 @@ def test_integer_input():
     for method in ["standard", "hessian", "modified", "ltsa"]:
         clf = manifold.LocallyLinearEmbedding(method=method, n_neighbors=10)
         clf.fit(X)  # this previously raised a TypeError
+
+
+def test_get_feature_names_out():
+    """Check get_feature_names_out for LocallyLinearEmbedding."""
+    X, y = make_blobs(random_state=0, n_features=4)
+    n_components = 2
+
+    iso = manifold.LocallyLinearEmbedding(n_components=n_components)
+    iso.fit(X)
+    names = iso.get_feature_names_out()
+    assert_array_equal(
+        [f"locallylinearembedding{i}" for i in range(n_components)], names
+    )
diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
@@ -385,7 +385,6 @@ def test_pandas_column_name_consistency(estimator):
     "isotonic",
     "kernel_approximation",
     "preprocessing",
-    "manifold",
     "neural_network",
 ]