From 0eeaa3afa0292cc17c9e0a33cfd12a4fb84bbefd Mon Sep 17 00:00:00 2001
From: Fabian Pedregosa <fabian.pedregosa@inria.fr>
Date: Wed, 9 Mar 2011 10:26:30 +0100
Subject: [PATCH] Do not import scipy.sparse globally.
Import time and for compatibility with old scipy.
---
scikits/learn/linear_model/ridge.py | 9 ++++++---
1 file changed, 6 insertions(+), 3 deletions(-)
diff --git a/scikits/learn/linear_model/ridge.py b/scikits/learn/linear_model/ridge.py
index 4dc849efb0..36a9e50e56 100644
--- a/scikits/learn/linear_model/ridge.py
+++ b/scikits/learn/linear_model/ridge.py
@@ -6,9 +6,6 @@ Ridge regression
# License: Simplified BSD
import numpy as np
-import scipy.sparse as sp
-from scipy import linalg
-from scipy.sparse import linalg as sp_linalg
from .base import LinearModel
from ..utils.extmath import safe_sparse_dot
@@ -85,6 +82,7 @@ class Ridge(LinearModel):
X, y, Xmean, ymean = \
LinearModel._center_data(X, y, self.fit_intercept)
+ import scipy.sparse as sp
if sp.issparse(X):
self._solve_sparse(X, y, sample_weight)
else:
@@ -116,6 +114,7 @@ class Ridge(LinearModel):
def _solve_sparse(self, X, y, sample_weight):
n_samples, n_features = X.shape
+ import scipy.sparse as sp
if n_features > n_samples or \
isinstance(sample_weight, np.ndarray) or \
sample_weight != 1.0:
@@ -132,14 +131,17 @@ class Ridge(LinearModel):
def _solve(self, A, b):
if self.solver == "cg":
# this solver cannot handle a 2-d b.
+ from scipy.sparse import linalg as sp_linalg
sol, error = sp_linalg.cg(A, b)
if error:
raise ValueError("Failed with error code %d" % error)
return sol
else:
+ import scipy.sparse as sp
# we are working with dense symmetric positive A
if sp.issparse(A):
A = A.todense()
+ from scipy import linalg
return linalg.solve(A, b, sym_pos=True, overwrite_a=True)
@@ -255,6 +257,7 @@ class _RidgeGCV(LinearModel):
def _pre_compute(self, X, y):
# even if X is very sparse, K is usually very dense
K = safe_sparse_dot(X, X.T, dense_output=True)
+ from scipy import linalg
v, Q = linalg.eigh(K)
return K, v, Q
--
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