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| author | Ben Woodcroft <donttrustben@gmail.com> | 2017-12-14 18:45:21 +1000 |
|---|---|---|
| committer | Ben Woodcroft <donttrustben@gmail.com> | 2017-12-14 22:50:19 +1000 |
| commit | 8a6cd65a2a66107b5a1ae12138a9af0002f55983 (patch) | |
| tree | 45dc80b4bc741f5bc5c6b57353e85bc8f19ab68a /gnu/packages/patches | |
| parent | 4e0b3583eab7dd67c2e13ad614e48f8d31c59c90 (diff) | |
| download | guix-8a6cd65a2a66107b5a1ae12138a9af0002f55983.tar guix-8a6cd65a2a66107b5a1ae12138a9af0002f55983.tar.gz | |
gnu: python-scikit-learn: Patch test non-determinism.
* gnu/packages/patches/python-scikit-learn-fix-test-non-determinism.patch:
New file.
* gnu/packages/machine-learning.scm (python-scikit-learn)[source]: Use it.
* gnu/local.mk (dist_patch_DATA): Add it.
Diffstat (limited to 'gnu/packages/patches')
| -rw-r--r-- | gnu/packages/patches/python-scikit-learn-fix-test-non-determinism.patch | 25 |
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 |