8000 ENH Adds get_feature_names_out to neighbors module (#22212) · scikit-learn/scikit-learn@330881a · GitHub
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Micky774ogrisel
Micky774
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ogrisel
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ENH Adds get_feature_names_out to neighbors module (#22212)
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
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‎doc/whats_new/v1.1.rst

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@@ -439,6 +439,11 @@ Changelog
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ndarray with `np.nan` when passed a `Float32` or `Float64` pandas extension
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array with `pd.NA`. :pr:`21278` by `Thomas Fan`_.
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- |Enhancement| Adds :term:`get_feature_names_out` to
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:class:`neighbors.RadiusNeighborsTransformer`, :class:`neighbors.KNeighborsTransformer`
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and :class:`neighbors.NeighborhoodComponentsAnalysis`. :pr:`22212` by
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:user : `Meekail Zain <micky774>`.
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- |Fix| :class:`neighbors.KernelDensity` now validates input parameters in `fit`
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instead of `__init__`. :pr:`21430` by :user:`Desislava Vasileva <DessyVV>` and
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:user:`Lucy Jimenez <LucyJimenez>`.

‎sklearn/neighbors/_graph.py

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@@ -7,7 +7,7 @@
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from ._base import KNeighborsMixin, RadiusNeighborsMixin
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from ._base import NeighborsBase
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from ._unsupervised import NearestNeighbors
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from ..base import TransformerMixin
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from ..base import TransformerMixin, _ClassNamePrefixFeaturesOutMixin
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from ..utils.validation import check_is_fitted
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@@ -223,7 +223,9 @@ def radius_neighbors_graph(
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return X.radius_neighbors_graph(query, radius, mode)
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class KNeighborsTransformer(KNeighborsMixin, TransformerMixin, NeighborsBase):
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class KNeighborsTransformer(
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_ClassNamePrefixFeaturesOutMixin, KNeighborsMixin, TransformerMixin, NeighborsBase
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):
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"""Transform X into a (weighted) graph of k nearest neighbors.
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The transformed data is a sparse graph as returned by kneighbors_graph.
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self : KNeighborsTransformer
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The fitted k-nearest neighbors transformer.
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"""
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return self._fit(X)
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self._fit(X)
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self._n_features_out = self.n_samples_fit_
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return self
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def transform(self, X):
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"""Compute the (weighted) graph of Neighbors for points in X.
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}
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class RadiusNeighborsTransformer(RadiusNeighborsMixin, TransformerMixin, NeighborsBase):
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class RadiusNeighborsTransformer(
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_ClassNamePrefixFeaturesOutMixin,
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RadiusNeighborsMixin,
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TransformerMixin,
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NeighborsBase,
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):
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"""Transform X into a (weighted) graph of neighbors nearer than a radius.
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The transformed data is a sparse graph as returned by
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self : RadiusNeighborsTransformer
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The fitted radius neighbors transformer.
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"""
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return self._fit(X)
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self._fit(X)
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self._n_features_out = self.n_samples_fit_
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return self
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def transform(self, X):
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"""Compute the (weighted) graph of Neighbors for points in X.

‎sklearn/neighbors/_nca.py

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

‎sklearn/neighbors/tests/test_graph.py

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import numpy as np
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import pytest
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from sklearn.metrics import euclidean_distances
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from sklearn.neighbors import KNeighborsTransformer, RadiusNeighborsTransformer
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from sklearn.neighbors._base import _is_sorted_by_data
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from sklearn.utils._testing import assert_array_equal
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def test_transformer_result():
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# Using transform on new data should not always have zero diagonal
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X2t = nnt.transform(X2)
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assert not _has_explicit_diagonal(X2t)
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@pytest.mark.parametrize("Klass", [KNeighborsTransformer, RadiusNeighborsTransformer])
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def test_graph_feature_names_out(Klass):
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"""Check `get_feature_names_out` for transformers defined in `_graph.py`."""
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n_samples_fit = 20
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n_features = 10
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rng = np.random.RandomState(42)
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X = rng.randn(n_samples_fit, n_features)
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est = Klass().fit(X)
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names_out = est.get_feature_names_out()
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class_name_lower = Klass.__name__.lower()
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expected_names_out = np.array(
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[f"{class_name_lower}{i}" for i in range(est.n_samples_fit_)],
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dtype=object,
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)
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assert_array_equal(names_out, expected_names_out)

‎sklearn/neighbors/tests/test_nca.py

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@@ -554,3 +554,20 @@ def test_parameters_valid_types(param, value):
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y = iris_target
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nca.fit(X, y)
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def test_nca_feature_names_out():
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"""Check `get_feature_names_out` for `NeighborhoodComponentsAnalysis`."""
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X = iris_data
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y = iris_target
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est = NeighborhoodComponentsAnalysis().fit(X, y)
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names_out = est.get_feature_names_out()
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class_name_lower = est.__class__.__name__.lower()
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expected_names_out = np.array(
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[f"{class_name_lower}{i}" for i in range(est.components_.shape[1])],
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dtype=object,
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)
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assert_array_equal(names_out, expected_names_out)

‎sklearn/tests/test_common.py

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@@ -386,7 +386,6 @@ def test_pandas_column_name_consistency(estimator):
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"kernel_approximation",
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"preprocessing",
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"manifold",
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"neighbors",
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"neural_network",
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]
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