Your message dated Mon, 04 May 2020 08:49:56 +0000
with message-id <e1jvwnu-0007bx...@fasolo.debian.org>
and subject line Bug#959139: fixed in scikit-learn 0.22.2.post1+dfsg-6
has caused the Debian Bug report #959139,
regarding numpy breaks scikit-learn arm64 autopkgtest: assert_uniform_grid(Y,
try_name)
to be marked as done.
This means that you claim that the problem has been dealt with.
If this is not the case it is now your responsibility to reopen the
Bug report if necessary, and/or fix the problem forthwith.
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--
959139: https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=959139
Debian Bug Tracking System
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--- Begin Message ---
Source: numpy, scikit-learn
Control: found -1 numpy/1:1.18.3-1
Control: found -1 scikit-learn/0.22.2.post1+dfsg-5
Severity: serious
Tags: sid bullseye
X-Debbugs-CC: debian...@lists.debian.org
User: debian...@lists.debian.org
Usertags: breaks needs-update
Dear maintainer(s),
With a recent upload of numpy the autopkgtest of scikit-learn fails in
testing on arm64 when that autopkgtest is run with the binary packages
of numpy from unstable. It passes when run with only packages from
testing. In tabular form:
pass fail
numpy from testing 1:1.18.3-1
scikit-learn from testing 0.22.2.post1+dfsg-5
all others from testing from testing
I copied some of the output at the bottom of this report.
Currently this regression is blocking the migration of numpy to testing
[1]. Due to the nature of this issue, I filed this bug report against
both packages. Can you please investigate the situation and reassign the
bug to the right package?
More information about this bug and the reason for filing it can be found on
https://wiki.debian.org/ContinuousIntegration/RegressionEmailInformation
Paul
[1] https://qa.debian.org/excuses.php?package=numpy
https://ci.debian.net/data/autopkgtest/testing/arm64/s/scikit-learn/5194679/log.gz
=================================== FAILURES
===================================
________________________ test_uniform_grid[barnes_hut]
_________________________
method = 'barnes_hut'
@pytest.mark.parametrize('method', ['barnes_hut', 'exact'])
def test_uniform_grid(method):
"""Make sure that TSNE can approximately recover a uniform 2D grid
Due to ties in distances between point in X_2d_grid, this test
is platform
dependent for ``method='barnes_hut'`` due to numerical imprecision.
Also, t-SNE is not assured to converge to the right solution
because bad
initialization can lead to convergence to bad local minimum (the
optimization problem is non-convex). To avoid breaking the test
too often,
we re-run t-SNE from the final point when the convergence is not
good
enough.
"""
seeds = [0, 1, 2]
n_iter = 500
for seed in seeds:
tsne = TSNE(n_components=2, init='random', random_state=seed,
perplexity=20, n_iter=n_iter, method=method)
Y = tsne.fit_transform(X_2d_grid)
try_name = "{}_{}".format(method, seed)
try:
> assert_uniform_grid(Y, try_name)
/usr/lib/python3/dist-packages/sklearn/manifold/tests/test_t_sne.py:784:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
Y = array([[ 52.326397 , -15.92225 ],
[ 46.679527 , -20.175953 ],
[ 40.870537 , -24.181147 ],
...[-35.291374 , 22.122814 ],
[-42.2738 , 18.793724 ],
[-48.922283 , 15.606232 ]], dtype=float32)
try_name = 'barnes_hut_1'
def assert_uniform_grid(Y, try_name=None):
# Ensure that the resulting embedding leads to approximately
# uniformly spaced points: the distance to the closest neighbors
# should be non-zero and approximately constant.
nn = NearestNeighbors(n_neighbors=1).fit(Y)
dist_to_nn = nn.kneighbors(return_distance=True)[0].ravel()
assert dist_to_nn.min() > 0.1
smallest_to_mean = dist_to_nn.min() / np.mean(dist_to_nn)
largest_to_mean = dist_to_nn.max() / np.mean(dist_to_nn)
assert smallest_to_mean > .5, try_name
> assert largest_to_mean < 2, try_name
E AssertionError: barnes_hut_1
E assert 6.67359409617653 < 2
/usr/lib/python3/dist-packages/sklearn/manifold/tests/test_t_sne.py:807:
AssertionError
During handling of the above exception, another exception occurred:
method = 'barnes_hut'
@pytest.mark.parametrize('method', ['barnes_hut', 'exact'])
def test_uniform_grid(method):
"""Make sure that TSNE can approximately recover a uniform 2D grid
Due to ties in distances between point in X_2d_grid, this test
is platform
dependent for ``method='barnes_hut'`` due to numerical imprecision.
Also, t-SNE is not assured to converge to the right solution
because bad
initialization can lead to convergence to bad local minimum (the
optimization problem is non-convex). To avoid breaking the test
too often,
we re-run t-SNE from the final point when the convergence is not
good
enough.
"""
seeds = [0, 1, 2]
n_iter = 500
for seed in seeds:
tsne = TSNE(n_components=2, init='random', random_state=seed,
perplexity=20, n_iter=n_iter, method=method)
Y = tsne.fit_transform(X_2d_grid)
try_name = "{}_{}".format(method, seed)
try:
assert_uniform_grid(Y, try_name)
except AssertionError:
# If the test fails a first time, re-run with init=Y to
see if
# this was caused by a bad initialization. Note that
this will
# also run an early_exaggeration step.
try_name += ":rerun"
tsne.init = Y
Y = tsne.fit_transform(X_2d_grid)
> assert_uniform_grid(Y, try_name)
/usr/lib/python3/dist-packages/sklearn/manifold/tests/test_t_sne.py:792:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
Y = array([[-18.169476 , 6.0802336 ],
[-18.278513 , 2.8822129 ],
[-18.671782 , -0.4646889 ],
...[ 22.550077 , 19.698557 ],
[ 21.399723 , 22.933178 ],
[ 16.22136 , 28.22955 ]], dtype=float32)
try_name = 'barnes_hut_1:rerun'
def assert_uniform_grid(Y, try_name=None):
# Ensure that the resulting embedding leads to approximately
# uniformly spaced points: the distance to the closest neighbors
# should be non-zero and approximately constant.
nn = NearestNeighbors(n_neighbors=1).fit(Y)
dist_to_nn = nn.kneighbors(return_distance=True)[0].ravel()
assert dist_to_nn.min() > 0.1
smallest_to_mean = dist_to_nn.min() / np.mean(dist_to_nn)
largest_to_mean = dist_to_nn.max() / np.mean(dist_to_nn)
assert smallest_to_mean > .5, try_name
> assert largest_to_mean < 2, try_name
E AssertionError: barnes_hut_1:rerun
E assert 2.145051767903112 < 2
/usr/lib/python3/dist-packages/sklearn/manifold/tests/test_t_sne.py:807:
AssertionError
signature.asc
Description: OpenPGP digital signature
--- End Message ---
--- Begin Message ---
Source: scikit-learn
Source-Version: 0.22.2.post1+dfsg-6
Done: Christian Kastner <c...@debian.org>
We believe that the bug you reported is fixed in the latest version of
scikit-learn, which is due to be installed in the Debian FTP archive.
A summary of the changes between this version and the previous one is
attached.
Thank you for reporting the bug, which will now be closed. If you
have further comments please address them to 959...@bugs.debian.org,
and the maintainer will reopen the bug report if appropriate.
Debian distribution maintenance software
pp.
Christian Kastner <c...@debian.org> (supplier of updated scikit-learn package)
(This message was generated automatically at their request; if you
believe that there is a problem with it please contact the archive
administrators by mailing ftpmas...@ftp-master.debian.org)
-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA512
Format: 1.8
Date: Mon, 04 May 2020 08:36:38 +0200
Source: scikit-learn
Architecture: source
Version: 0.22.2.post1+dfsg-6
Distribution: unstable
Urgency: medium
Maintainer: Debian Science Team
<debian-science-maintain...@lists.alioth.debian.org>
Changed-By: Christian Kastner <c...@debian.org>
Closes: 959139
Changes:
scikit-learn (0.22.2.post1+dfsg-6) unstable; urgency=medium
.
* Team upload.
.
* Add Fix-for-test_uniform_grid.patch
Cherry-picked from upstream. (Closes: #959139)
* Bump debhelper to 12 and switch to debhelper-compat
* Mark python3-sklearn-lib as Multi-Arch: same
* Re-enable build of documentation, which the following updates needed for
the new scikit-learn-modern theme:
- Update update python-sklearn-doc.{doc-base, links)
- Bump Depends from libjs-bootstrap to libjs-bootstrap4
- Add Use-local-MathJax.patch
- Add Disable-BinderHub-links.patch
* debian/rules refactoring and minor improvements
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