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ENH Add get_feature_names_out for random_projection module #21330

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Merged
merged 5 commits into from
Nov 3, 2021
Merged

ENH Add get_feature_names_out for random_projection module #21330

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e080f95
Add get_feature_names_out for random_projection module
lesteve Oct 14, 2021
e5766ef
Use _ClassNamePrefixFeaturesOutMixin
lesteve Nov 2, 2021
5aff26c
Use list rather than set for parametrised test
lesteve Nov 2, 2021
6ddeaeb
Tweak changelog
lesteve Nov 2, 2021
ede358f
Add info about _n_features_out in docstring
lesteve Nov 2, 2021
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9 changes: 9 additions & 0 deletions doc/whats_new/v1.1.rst
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Expand Up @@ -162,6 +162,15 @@ Changelog
ndarray with `np.nan` when passed a `Float32` or `Float64` pandas extension
array with `pd.NA`. :pr:`21278` by `Thomas Fan`_.

:mod:`sklearn.random_projection`
................................

- |API| Adds :term:`get_feature_names_out` to all transformers in the
:mod:`~sklearn.random_projection` module:
:class:`~sklearn.random_projection.GaussianRandomProjection` and
:class:`~sklearn.random_projection.SparseRandomProjection`. :pr:`21330` by
:user:`Loïc Estève <lesteve>`.

Code and Documentation Contributors
-----------------------------------

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13 changes: 12 additions & 1 deletion sklearn/random_projection.py
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Expand Up @@ -34,6 +34,7 @@
import scipy.sparse as sp

from .base import BaseEstimator, TransformerMixin
from .base import _ClassNamePrefixFeaturesOutMixin

from .utils import check_random_state
from .utils.extmath import safe_sparse_dot
Expand Down Expand Up @@ -290,7 +291,9 @@ def _sparse_random_matrix(n_components, n_features, density="auto", random_state
return np.sqrt(1 / density) / np.sqrt(n_components) * components


class BaseRandomProjection(TransformerMixin, BaseEstimator, metaclass=ABCMeta):
class BaseRandomProjection(
TransformerMixin, BaseEstimator, _ClassNamePrefixFeaturesOutMixin, metaclass=ABCMeta
):
"""Base class for random projections.

Warning: This class should not be used directly.
Expand Down Expand Up @@ -420,6 +423,14 @@ def transform(self, X):
X_new = safe_sparse_dot(X, self.components_.T, dense_output=self.dense_output)
return X_new

@property
def _n_features_out(self):
"""Number of transformed output features.

Used by _ClassNamePrefixFeaturesOutMixin.get_feature_names_out.
"""
return self.n_components


class GaussianRandomProjection(BaseRandomProjection):
"""Reduce dimensionality through Gaussian random projection.
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1 change: 0 additions & 1 deletion sklearn/tests/test_common.py
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Expand Up @@ -371,7 +371,6 @@ def test_pandas_column_name_consistency(estimator):
"manifold",
"neighbors",
"neural_network",
"random_projection",
]


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16 changes: 15 additions & 1 deletion sklearn/tests/test_random_projection.py
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Expand Up @@ -24,7 +24,7 @@

all_SparseRandomProjection: List[Any] = [SparseRandomProjection]
all_DenseRandomProjection: List[Any] = [GaussianRandomProjection]
all_RandomProjection = set(all_SparseRandomProjection + all_DenseRandomProjection)
all_RandomProjection = all_SparseRandomProjection + all_DenseRandomProjection
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@lesteve lesteve Nov 2, 2021 •
edited
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This was done to avoid a pytest-xdist error about workers collecting different tests (different order).

It does not seem that crucial that all_RandomProjection is a set in this file.



# Make some random data with uniformly located non zero entries with
Expand Down Expand Up @@ -359,3 +359,17 @@ def test_johnson_lindenstrauss_min_dim():
Regression test for #17111: before #19374, 32-bit systems would fail.
"""
assert johnson_lindenstrauss_min_dim(100, eps=1e-5) == 368416070986


@pytest.mark.parametrize("random_projection_cls", all_RandomProjection)
def test_random_projection_feature_names_out(random_projection_cls):
random_projection = random_projection_cls(n_components=2)
random_projection.fit(data)
names_out = random_projection.get_feature_names_out()
class_name_lower = random_projection_cls.__name__.lower()
expected_names_out = np.array(
[f"{class_name_lower}{i}" for i in range(random_projection.n_components_)],
dtype=object,
)

assert_array_equal(names_out, expected_names_out)