summaryrefslogtreecommitdiff
path: root/gnu/packages/patches/python-scikit-learn-fix-test-non-determinism.patch
diff options
context:
space:
mode:
authorMarius Bakke <mbakke@fastmail.com>2017-12-19 01:42:40 +0100
committerMarius Bakke <mbakke@fastmail.com>2017-12-19 01:42:40 +0100
commit32cd878be0bb7e153fcaa6f3bfa2632867390ff9 (patch)
treefc1ff93949817c9d172c84d0410ac9225cad57ae /gnu/packages/patches/python-scikit-learn-fix-test-non-determinism.patch
parent753425610274ccb59cce13490c096027c61621d0 (diff)
parent98bd11cfe7b931e9c6d6bf002a8a225fb7a1025b (diff)
downloadguix-patches-32cd878be0bb7e153fcaa6f3bfa2632867390ff9.tar
guix-patches-32cd878be0bb7e153fcaa6f3bfa2632867390ff9.tar.gz
Merge branch 'master' into core-updates
Diffstat (limited to 'gnu/packages/patches/python-scikit-learn-fix-test-non-determinism.patch')
-rw-r--r--gnu/packages/patches/python-scikit-learn-fix-test-non-determinism.patch25
1 files changed, 25 insertions, 0 deletions
diff --git a/gnu/packages/patches/python-scikit-learn-fix-test-non-determinism.patch b/gnu/packages/patches/python-scikit-learn-fix-test-non-determinism.patch
new file mode 100644
index 0000000000..90328cc0eb
--- /dev/null
+++ b/gnu/packages/patches/python-scikit-learn-fix-test-non-determinism.patch
@@ -0,0 +1,25 @@
+This patch stops a test sometimes failing because of non-determinism. See
+https://github.com/scikit-learn/scikit-learn/pull/9542
+
+From ff9f6db6e8b59c2b3528c8137ed4054f57c1d7c4 Mon Sep 17 00:00:00 2001
+From: Hanmin Qin <qinhanmin2005@sina.com>
+Date: Sun, 13 Aug 2017 22:13:49 +0800
+Subject: [PATCH] add random_state
+
+---
+ sklearn/tests/test_kernel_ridge.py | 2 +-
+ 1 file changed, 1 insertion(+), 1 deletion(-)
+
+diff --git a/sklearn/tests/test_kernel_ridge.py b/sklearn/tests/test_kernel_ridge.py
+index 4750a096ac6..979875870b6 100644
+--- a/sklearn/tests/test_kernel_ridge.py
++++ b/sklearn/tests/test_kernel_ridge.py
+@@ -10,7 +10,7 @@
+ from sklearn.utils.testing import assert_array_almost_equal
+
+
+-X, y = make_regression(n_features=10)
++X, y = make_regression(n_features=10, random_state=0)
+ Xcsr = sp.csr_matrix(X)
+ Xcsc = sp.csc_matrix(X)
+ Y = np.array([y, y]).T