8000 ENH Add dtype preservation to SkewedChi2Sampler by rprkh · Pull Request #24349 · scikit-learn/scikit-learn · GitHub
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ENH Add dtype preservation to SkewedChi2Sampler #24349

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ENH Add dtype preservation to SkewedChi2Sampler #24349

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d20e864
Preserve dtype in SkewedChi2Sampler
rprkh Sep 4, 2022
af91eda
fix PR number
rprkh Sep 4, 2022
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6 changes: 6 additions & 0 deletions doc/whats_new/v1.2.rst
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Expand Up @@ -158,6 +158,12 @@ Changelog
:pr:`11860` by :user:`Pierre Ablin <pierreablin>`,
:pr:`22527` by :user:`Meekail Zain <micky774>` and `Thomas Fan`_.

:mod:`sklearn.kernel_approximation`
............................

- |Enhancement| :class:`kernel_approximation.SkewedChi2Sampler` now preserves
dtype for `numpy.float32` inputs. :pr:`24349` by `Rahil Parikh <rprkh>`.

:mod:`sklearn.linear_model`
...........................

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9 changes: 9 additions & 0 deletions sklearn/kernel_approximation.py
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Expand Up @@ -444,6 +444,9 @@ class SkewedChi2Sampler(
1.0
"""

def _more_tags(self):
return {"preserves_dtype": [np.float64, np.float32]}

def __init__(self, *, skewedness=1.0, n_components=100, random_state=None):
self.skewedness = skewedness
self.n_components = n_components
Expand Down Expand Up @@ -477,6 +480,12 @@ def fit(self, X, y=None):
# transform by inverse CDF of sech
self.random_weights_ = 1.0 / np.pi * np.log(np.tan(np.pi / 2.0 * uniform))
self.random_offset_ = random_state.uniform(0, 2 * np.pi, size=self.n_components)

# With this we preserve the dtype of X in transform() later
if X.dtype == np.float32:
self.random_weights_ = self.random_weights_.astype(X.dtype, copy=False)
self.random_offset_ = self.random_offset_.astype(X.dtype, copy=False)

self._n_features_out = self.n_components
return self

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