8000 ENH Optimize dot product order for LogisticRegression for dense matrices by jjerphan · Pull Request #19571 · scikit-learn/scikit-learn · GitHub
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ENH Optimize dot product order for LogisticRegression for dense matrices #19571

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Merged
merged 5 commits into from
Mar 14, 2021
Merged

ENH Optimize dot product order for LogisticRegression for dense matrices #19571

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4f473cf
Use multi_dot for Hessian and gradient product
jjerphan Feb 26, 2021
22a620d
Add safe dot product for sparse matrices
jjerphan Feb 26, 2021
83b61d9
Revert to previous multiplication chain for sparse matrices
jjerphan Mar 12, 2021
3f9ad3d
Add whats_new entry for #19571
jjerphan Mar 12, 2021
71cd307
Update doc/whats_new/v1.0.rst
jjerphan Mar 13, 2021
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5 changes: 5 additions & 0 deletions doc/whats_new/v1.0.rst
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Expand Up @@ -113,6 +113,11 @@ Changelog
:mod:`sklearn.linear_model`
...........................

- |Efficiency| The implementation of :class:`linear_model.LogisticRegression`
has been optimised for dense matrices when using `solver='newton-cg'` and
`multi_class!='multinomial'`.
:pr:`19571` by :user:`Julien Jerphanion <jjerphan>`.

- |Enhancement| Validate user-supplied gram matrix passed to linear models
via the `precompute` argument. :pr:`19004` by :user:`Adam Midvidy <amidvidy>`.

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5 changes: 4 additions & 1 deletion sklearn/linear_model/_logistic.py
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Expand Up @@ -233,7 +233,10 @@ def _logistic_grad_hess(w, X, y, alpha, sample_weight=None):

def Hs(s):
ret = np.empty_like(s)
ret[:n_features] = X.T.dot(dX.dot(s[:n_features]))
if sparse.issparse(X):
ret[:n_features] = X.T.dot(dX.dot(s[:n_features]))
else:
ret[:n_features] = np.linalg.multi_dot([X.T, dX, s[:n_features]])
ret[:n_features] += alpha * s[:n_features]

# For the fit intercept case.
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