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ENH avoid np.square(X) in enet_coordinate_descent to save memory #31665

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Merged
OmarManzoor merged 4 commits into from
Jun 27, 2025
Merged

ENH avoid np.square(X) in enet_coordinate_descent to save memory #31665

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3a2490a
ENH avoid np.square(X) in enet_coordinate_descent to save memory
lorentzenchr Jun 26, 2025
b1b621d
simpler solution
lorentzenchr Jun 26, 2025
da401b1
DOC add whatsnew
lorentzenchr Jun 26, 2025
47d0663
FIX again
lorentzenchr Jun 26, 2025
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3 changes: 3 additions & 0 deletions doc/whats_new/upcoming_changes/sklearn.linear_model/31665.enhancement.rst
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Original file line number Diff line number Diff line change
@@ -0,0 +1,3 @@
- class:`linear_model:ElasticNet` and class:`linear_model:Lasso` with
`precompute=False` uses less memory for dense `X` and is a bit faster.
By :user:`Christian Lorentzen <lorentzenchr>`
5 changes: 4 additions & 1 deletion sklearn/linear_model/_cd_fast.pyx
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Expand Up @@ -139,7 +139,10 @@ def enet_coordinate_descent(
cdef unsigned int n_features = X.shape[1]

# compute norms of the columns of X
cdef floating[::1] norm_cols_X = np.square(X).sum(axis=0)
# same as norm_cols_X = np.square(X).sum(axis=0)
cdef floating[::1] norm_cols_X = np.einsum(
"ij,ij->j", X, X, dtype=dtype, order="C"
)

# initial value of the residuals
cdef floating[::1] R = np.empty(n_samples, dtype=dtype)
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