8000 [WIP] Handle n_samples > n_features efficiently for sparse matricies in RidgeCV by orausch · Pull Request #13327 · scikit-learn/scikit-learn · GitHub
[go: up one dir, main page]
Skip to content

[WIP] Handle n_samples > n_features efficiently for sparse matricies in RidgeCV #13327

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 1 commit into from
Closed

[WIP] Handle n_samples > n_features efficiently for sparse matricies in RidgeCV #13327

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 7 additions & 6 deletions sklearn/linear_model/ridge.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -910,6 +910,7 @@ def __init__(self, alphas=(0.1, 1.0, 10.0),

def _pre_compute(self, X, y, centered_kernel=True):
# even if X is very sparse, K is usually very dense
n_samples, n_features = X.shape
K = safe_sparse_dot(X, X.T, dense_output=True)
# the following emulates an additional constant regressor
# corresponding to fit_intercept=True
Expand Down Expand Up @@ -960,13 +961,14 @@ def _values(self, alpha, y, v, Q, QT_y):
return y - (c / G_diag), c

def _pre_compute_svd(self, X, y, centered_kernel=True):
if sparse.issparse(X):
raise TypeError("SVD not supported for sparse matrices")
if centered_kernel:
X = np.hstack((X, np.ones((X.shape[0], 1))))
# to emulate fit_intercept=True situation, add a column on ones
# Note that by centering, the other columns are orthogonal to that one
U, s, _ = linalg.svd(X, full_matrices=0)
if sparse.issparse(X):
U, s, _ = sp_linalg.svds(X)
else:
U, s, _ = linalg.svd(X, full_matrices=0)
v = s ** 2
UT_y = np.dot(U.T, y)
return v, U, UT_y
Expand Down Expand Up @@ -1027,7 +1029,7 @@ def fit(self, X, y, sample_weight=None):
with_sw = len(np.shape(sample_weight))

if gcv_mode is None or gcv_mode == 'auto':
if sparse.issparse(X) or n_features > n_samples or with_sw:
if n_features > n_samples or with_sw:
gcv_mode = 'eigen'
else:
gcv_mode = 'svd'
Expand Down Expand Up @@ -1283,8 +1285,7 @@ class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV):
See glossary entry for :term:`cross-validation estimator`.

By default, it performs Generalized Cross-Validation, which is a form of
efficient Leave-One-Out cross-validation. Currently, only the n_features >
n_samples case is handled efficiently.
efficient Leave-One-Out cross-validation.

Read more in the :ref:`User Guide <ridge_regression>`.

Expand Down
0