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ENH Adds get_feature_names to neural_network module #22248

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Merged
merged 7 commits into from
Feb 4, 2022
Merged

ENH Adds get_feature_names to neural_network module #22248

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485fddd
ENH Adds get_feature_names to neural_network module
thomasjpfan Jan 20, 2022
226e8a0
ENH Adds PR number
thomasjpfan Jan 20, 2022
f97db54
DOC Minor grammar
thomasjpfan Jan 20, 2022
f258926
CLN Better docstring
thomasjpfan Jan 20, 2022
b26ac43
DOC Link to glossary
thomasjpfan Jan 20, 2022
a28199f
Merge branch 'main' into neural_network_get_feature_names_out
ogrisel Jan 27, 2022
476747a
Merge branch 'main' into neural_network_get_feature_names_out
ogrisel Feb 1, 2022
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3 changes: 3 additions & 0 deletions doc/whats_new/v1.1.rst
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Expand Up @@ -503,6 +503,9 @@ Changelog
messages when optimizers produce non-finite parameter weights. :pr:`22150`
by :user:`Christian Ritter <chritter>` and :user:`Norbert Preining <norbusan>`.

- |Enhancement| Adds :term:`get_feature_names_out` to
:class:`neural_network.BernoulliRBM`. :pr:`22248` by `Thomas Fan`_.

:mod:`sklearn.pipeline`
.......................

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5 changes: 4 additions & 1 deletion sklearn/neural_network/_rbm.py
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Expand Up @@ -15,14 +15,15 @@

from ..base import BaseEstimator
from ..base import TransformerMixin
from ..base import _ClassNamePrefixFeaturesOutMixin
from ..utils import check_random_state
from ..utils import gen_even_slices
from ..utils.extmath import safe_sparse_dot
from ..utils.extmath import log_logistic
from ..utils.validation import check_is_fitted


class BernoulliRBM(TransformerMixin, BaseEstimator):
class BernoulliRBM(_ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
"""Bernoulli Restricted Boltzmann Machine (RBM).

A Restricted Boltzmann Machine with binary visible units and
Expand Down Expand Up @@ -284,6 +285,7 @@ def partial_fit(self, X, y=None):
self.random_state_.normal(0, 0.01, (self.n_components, X.shape[1])),
order="F",
)
self._n_features_out = self.components_.shape[0]
if not hasattr(self, "intercept_hidden_"):
self.intercept_hidden_ = np.zeros(
self.n_components,
Expand Down Expand Up @@ -389,6 +391,7 @@ def fit(self, X, y=None):
order="F",
dtype=X.dtype,
)
self._n_features_out = self.components_.shape[0]
self.intercept_hidden_ = np.zeros(self.n_components, dtype=X.dtype)
self.intercept_visible_ = np.zeros(X.shape[1], dtype=X.dtype)
self.h_samples_ = np.zeros((self.batch_size, self.n_components), dtype=X.dtype)
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12 changes: 12 additions & 0 deletions sklearn/neural_network/tests/test_rbm.py
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Expand Up @@ -238,3 +238,15 @@ def test_convergence_dtype_consistency():
)
assert_allclose(rbm_64.components_, rbm_32.components_, rtol=1e-03, atol=0)
assert_allclose(rbm_64.h_samples_, rbm_32.h_samples_)


@pytest.mark.parametrize("method", ["fit", "partial_fit"])
def test_feature_names_out(method):
"""Check `get_feature_names_out` for `BernoulliRBM`."""
n_components = 10
rbm = BernoulliRBM(n_components=n_components)
getattr(rbm, method)(Xdigits)

names = rbm.get_feature_names_out()
expected_names = [f"bernoullirbm{i}" for i in range(n_components)]
assert_array_equal(expected_names, names)
1 change: 0 additions & 1 deletion sklearn/tests/test_common.py
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Expand Up @@ -383,7 +383,6 @@ def test_pandas_column_name_consistency(estimator):
"ensemble",
"kernel_approximation",
"preprocessing",
"neural_network",
]


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