8000 ENH Adds get_feature_names to manifold module (#22254) · scikit-learn/scikit-learn@3fd84f7 · GitHub
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ENH Adds get_feature_names to manifold module (#22254)
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‎doc/whats_new/v1.1.rst

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@@ -417,6 +417,9 @@ Changelog
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preserve this dtype.
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:pr:`21534` by :user:`Andrew Knyazev <lobpcg>`.
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- |Enhancement| Adds `get_feature_names_out` to :class:`manifold.Isomap`
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and :class:`manifold.LocallyLinearEmbedding`. :pr:`22254` by `Thomas Fan`_.
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- |Fix| :func:`manifold.spectral_embedding` now uses Gaussian instead of
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the previous uniform on [0, 1] random initial approximations to eigenvectors
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in eigen_solvers `lobpcg` and `amg` to improve their numerical stability.

‎sklearn/manifold/_isomap.py

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

‎sklearn/manifold/_locally_linear.py

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

‎sklearn/manifold/tests/test_isomap.py

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from itertools import product
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import numpy as np
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from numpy.testing import assert_almost_equal, assert_array_almost_equal
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from numpy.testing import (
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assert_almost_equal,
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assert_array_almost_equal,
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assert_array_equal,
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)
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import pytest
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from sklearn import datasets
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from sklearn import manifold
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from sklearn import neighbors
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from sklearn import pipeline
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from sklearn import preprocessing
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from sklearn.datasets import make_blobs
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from sklearn.metrics.pairwise import pairwise_distances
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from scipy.sparse import rand as sparse_rand
@@ -220,3 +225,14 @@ def test_multiple_connected_components_metric_precomputed():
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X_graph = neighbors.kneighbors_graph(X, n_neighbors=2, mode="distance")
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with pytest.raises(RuntimeError, match="number of connected components"):
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manifold.Isomap(n_neighbors=1, metric="precomputed").fit(X_graph)
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def test_get_feature_names_out():
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"""Check get_feature_names_out for Isomap."""
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X, y = make_blobs(random_state=0, n_features=4)
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n_components = 2
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iso = manifold.Isomap(n_components=n_components)
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iso.fit_transform(X)
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names = iso.get_feature_names_out()
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assert_array_equal([f"isomap{i}" for i in range(n_components)], names)

‎sklearn/manifold/tests/test_locally_linear.py

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@@ -1,11 +1,16 @@
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from itertools import product
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import numpy as np
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from numpy.testing import assert_almost_equal, assert_array_almost_equal
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from numpy.testing import (
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assert_almost_equal,
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assert_array_almost_equal,
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assert_array_equal,
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)
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from scipy import linalg
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import pytest
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from sklearn import neighbors, manifold
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from sklearn.datasets import make_blobs
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from sklearn.manifold._locally_linear import barycenter_kneighbors_graph
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from sklearn.utils._testing import ignore_warnings
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@@ -159,3 +164,16 @@ def test_integer_input():
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for method in ["standard", "hessian", "modified", "ltsa"]:
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clf = manifold.LocallyLinearEmbedding(method=method, n_neighbors=10)
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clf.fit(X) # this previously raised a TypeError
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def test_get_feature_names_out():
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"""Check get_feature_names_out for LocallyLinearEmbedding."""
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X, y = make_blobs(random_state=0, n_features=4)
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n_components = 2
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iso = manifold.LocallyLinearEmbedding(n_components=n_components)
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iso.fit(X)
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names = iso.get_feature_names_out()
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assert_array_equal(
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[f"locallylinearembedding{i}" for i in range(n_components)], names
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)

‎sklearn/tests/test_common.py

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@@ -384,7 +384,6 @@ def test_pandas_column_name_consistency(estimator):
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"isotonic",
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"kernel_approximation",
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"preprocessing",
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"manifold",
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"neural_network",
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]
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