From 0690438ceec17db5f1c0880d2488deb5a44ebe6e Mon Sep 17 00:00:00 2001
From: Jake Vanderplas <vanderplas@astro.washington.edu>
Date: Mon, 19 Dec 2011 11:06:14 -0800
Subject: [PATCH] remove duplicative utils.fixes.arpack_eigsh
---
doc/developers/utilities.rst | 2 --
sklearn/cluster/spectral.py | 6 +++---
sklearn/utils/fixes.py | 13 -------------
3 files changed, 3 insertions(+), 18 deletions(-)
diff --git a/doc/developers/utilities.rst b/doc/developers/utilities.rst
index 5f4364bcc1..e1232657dc 100644
--- a/doc/developers/utilities.rst
+++ b/doc/developers/utilities.rst
@@ -192,8 +192,6 @@ ARPACK
method. A limited version of ``svds`` is available in earlier
scipy versions.
-- :func:`fixes.arpack_eigsh` [TODO: remove this from spectral_clustering
- in favor of the above back-port]
Benchmarking
~~~~~~~~~~~~
diff --git a/sklearn/cluster/spectral.py b/sklearn/cluster/spectral.py
index f0acb80b64..bb4590c86f 100644
--- a/sklearn/cluster/spectral.py
+++ b/sklearn/cluster/spectral.py
@@ -61,7 +61,7 @@ def spectral_embedding(adjacency, n_components=8, mode=None,
"""
from scipy import sparse
- from ..utils.fixes import arpack_eigsh
+ from ..utils.arpack import eigsh
from scipy.sparse.linalg import lobpcg
try:
from pyamg import smoothed_aggregation_solver
@@ -102,8 +102,8 @@ def spectral_embedding(adjacency, n_components=8, mode=None,
# csr has the fastest matvec and is thus best suited to
# arpack
laplacian = laplacian.tocsr()
- lambdas, diffusion_map = arpack_eigsh(-laplacian, k=n_components,
- which='LA')
+ lambdas, diffusion_map = eigsh(-laplacian, k=n_components,
+ which='LA')
embedding = diffusion_map.T[::-1] * dd
elif mode == 'amg':
# Use AMG to get a preconditioner and speed up the eigenvalue
diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py
index 54c1c5a2aa..3f831933f1 100644
--- a/sklearn/utils/fixes.py
+++ b/sklearn/utils/fixes.py
@@ -130,19 +130,6 @@ def qr_economic(A, **kwargs):
return scipy.linalg.qr(A, econ=True, **kwargs)
-def arpack_eigsh(A, **kwargs):
- """Compat function for sparse symmetric eigen vectors decomposition
-
- Scipy 0.9 renamed eigen_symmetric to eigsh in
- scipy.sparse.linalg.eigen.arpack
- """
- from scipy.sparse.linalg.eigen import arpack
- if hasattr(arpack, 'eigsh'):
- return arpack.eigsh(A, **kwargs)
- else:
- return arpack.eigen_symmetric(A, **kwargs)
-
-
def savemat(file_name, mdict, oned_as="column", **kwargs):
"""MATLAB-format output routine that is compatible with SciPy 0.7's.
--
GitLab