Your message dated Thu, 29 Oct 2026 17:05:19 +0000
with message-id <[email protected]>
and subject line Bug#1129897: fixed in python-hmmlearn 0.3.3-2
has caused the Debian Bug report #1129897,
regarding python-hmmlearn: FTBFS: FAILED
hmmlearn/tests/test_gaussian_hmm.py::TestGaussianHMMWithTiedCovars::test_fit_with_priors[scaling]
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.
(NB: If you are a system administrator and have no idea what this
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misconfiguration somewhere. Please contact [email protected]
immediately.)
--
1129897: https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=1129897
Debian Bug Tracking System
Contact [email protected] with problems
--- Begin Message ---
Package: src:python-hmmlearn
Version: 0.3.2-3
Severity: serious
Tags: ftbfs forky sid
Dear maintainer:
During a rebuild of all packages in unstable, this package failed to build.
Below you will find the last part of the build log (probably the most
relevant part, but not necessarily). If required, the full build log
is available here:
https://people.debian.org/~sanvila/build-logs/202603/
About the archive rebuild: The build was made on virtual machines from AWS,
using sbuild and a reduced chroot with only build-essential packages.
If you cannot reproduce the bug please contact me privately, as I
am willing to provide ssh access to a virtual machine where the bug is
fully reproducible.
If this is really a bug in one of the build-depends, please use
reassign and add an affects on src:python-hmmlearn, so that this is still
visible in the BTS web page for this package.
Thanks.
--------------------------------------------------------------------------------
[...]
debian/rules clean
dh clean --buildsystem=pybuild
dh_auto_clean -O--buildsystem=pybuild
dh_autoreconf_clean -O--buildsystem=pybuild
dh_clean -O--buildsystem=pybuild
debian/rules binary
dh binary --buildsystem=pybuild
dh_update_autotools_config -O--buildsystem=pybuild
dh_autoreconf -O--buildsystem=pybuild
dh_auto_configure -O--buildsystem=pybuild
dh_auto_build -O--buildsystem=pybuild
I: pybuild plugin_pyproject:142: Building wheel for python3.14 with "build"
module
I: pybuild base:384: python3.14 -m build --skip-dependency-check --no-isolation
--wheel --outdir /<<PKGBUILDDIR>>/.pybuild/cpython3_3.14_hmmlearn
* Building wheel...
/usr/lib/python3/dist-packages/setuptools/dist.py:759:
SetuptoolsDeprecationWarning: License classifiers are deprecated.
[... snipped ...]
hmmlearn/tests/test_kl_divergence.py ..... [ 76%]
hmmlearn/tests/test_multinomial_hmm.py ................ [ 81%]
hmmlearn/tests/test_poisson_hmm.py ............ [ 85%]
hmmlearn/tests/test_utils.py ... [ 86%]
hmmlearn/tests/test_variational_categorical.py ............ [ 90%]
hmmlearn/tests/test_variational_gaussian.py ............................ [ 98%]
.... [100%]
=============================== warnings summary ===============================
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gaussian_hmm.py: 1
warning
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_multisequence.py:
5 warnings
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py: 12
warnings
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_poisson_hmm.py: 1
warning
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_variational_gaussian.py:
27 warnings
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/base.py:509:
RuntimeWarning: underflow encountered in multiply
posteriors = fwdlattice * bwdlattice
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_multisequence.py:
4 warnings
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py: 13
warnings
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_poisson_hmm.py: 1
warning
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_variational_gaussian.py:
27 warnings
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/utils.py:29:
RuntimeWarning: underflow encountered in divide
a /= a_sum
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithSphericalCovars::test_fit_zero_variance[scaling]
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/base.py:598:
RuntimeWarning: overflow encountered in exp
return np.exp(self._compute_log_likelihood(X))
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithSphericalCovars::test_fit_zero_variance[scaling]
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithSphericalCovars::test_fit_zero_variance[log]
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/utils.py:55:
RuntimeWarning: invalid value encountered in subtract
a -= a_lse
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance[scaling]
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance[log]
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/hmm.py:744:
RuntimeWarning: invalid value encountered in divide
self.weights_ = w_n / w_d
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance[scaling]
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance[log]
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/hmm.py:756:
RuntimeWarning: invalid value encountered in divide
self.means_ = m_n / m_d[:, :, None]
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance[scaling]
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance[log]
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithFullCovars::test_fit_zero_variance[scaling]
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithFullCovars::test_fit_zero_variance[log]
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/hmm.py:809:
RuntimeWarning: invalid value encountered in divide
self.covars_ = c_n / c_d
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance[scaling]
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance[log]
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithFullCovars::test_fit_zero_variance[scaling]
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithFullCovars::test_fit_zero_variance[log]
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/_emissions.py:208:
RuntimeWarning: divide by zero encountered in log
log_cur_weights = np.log(self.weights_[i_comp])
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_variational_categorical.py:
10 warnings
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_variational_gaussian.py:
20 warnings
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/base.py:1215:
RuntimeWarning: underflow encountered in exp
self.startprob_subnorm_ = np.exp(startprob_log_subnorm)
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_variational_categorical.py:
8 warnings
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_variational_gaussian.py:
12 warnings
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/base.py:1220:
RuntimeWarning: underflow encountered in exp
self.transmat_subnorm_ = np.exp(transmat_log_subnorm)
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_variational_categorical.py:
1 warning
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_variational_gaussian.py:
31 warnings
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/base.py:1153:
RuntimeWarning: underflow encountered in exp
return np.exp(self._compute_subnorm_log_likelihood(X))
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_variational_gaussian.py:
30 warnings
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/_emissions.py:153:
RuntimeWarning: underflow encountered in matmul
stats['obs'] += posteriors.T @ X
.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/tests/test_variational_gaussian.py:
12 warnings
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.14_hmmlearn/build/hmmlearn/_emissions.py:157:
RuntimeWarning: underflow encountered in matmul
stats['obs**2'] += posteriors.T @ X**2
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
========== 294 passed, 17 xfailed, 9 xpassed, 230 warnings in 26.30s ===========
I: pybuild base:384: cd /<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_hmmlearn/build;
python3.13 -m pytest --pyargs hmmlearn
set RNG seed to 1165331465
============================= test session starts ==============================
platform linux -- Python 3.13.12, pytest-9.0.2, pluggy-1.6.0
rootdir: /<<PKGBUILDDIR>>
configfile: setup.cfg
plugins: typeguard-4.4.4
collected 320 items
hmmlearn/tests/test_base.py ................ [ 5%]
hmmlearn/tests/test_categorical_hmm.py ...................... [ 11%]
hmmlearn/tests/test_gaussian_hmm.py .................................... [ 23%]
......................................F....................... [ 42%]
hmmlearn/tests/test_gmm_hmm.py xxxxxXxxxxxXxxxxxX [ 48%]
hmmlearn/tests/test_gmm_hmm_multisequence.py ........ [ 50%]
hmmlearn/tests/test_gmm_hmm_new.py ..............XX................XX... [ 62%]
.............XX................xx........ [ 75%]
hmmlearn/tests/test_kl_divergence.py ..... [ 76%]
hmmlearn/tests/test_multinomial_hmm.py ................ [ 81%]
hmmlearn/tests/test_poisson_hmm.py ............ [ 85%]
hmmlearn/tests/test_utils.py ... [ 86%]
hmmlearn/tests/test_variational_categorical.py ............ [ 90%]
hmmlearn/tests/test_variational_gaussian.py ............................ [ 98%]
.... [100%]
=================================== FAILURES ===================================
_________ TestGaussianHMMWithTiedCovars.test_fit_with_priors[scaling] __________
self = <hmmlearn.tests.test_gaussian_hmm.TestGaussianHMMWithTiedCovars object
at 0x7f48b8f25f40>
implementation = 'scaling', init_params = 'mc', params = 'stmc', n_iter = 20
@pytest.mark.parametrize("implementation", ["scaling", "log"])
def test_fit_with_priors(self, implementation, init_params='mc',
params='stmc', n_iter=20):
# We have a few options to make this a robust test, such as
# a. increase the amount of training data to ensure convergence
# b. Only learn some of the parameters (simplify the problem)
# c. Increase the number of iterations
#
# (c) seems to not affect the ci/cd time too much.
startprob_prior = 10 * self.startprob + 2.0
transmat_prior = 10 * self.transmat + 2.0
means_prior = self.means
means_weight = 2.0
covars_weight = 2.0
if self.covariance_type in ('full', 'tied'):
covars_weight += self.n_features
covars_prior = self.covars
h = hmm.GaussianHMM(self.n_components, self.covariance_type,
implementation=implementation)
h.startprob_ = self.startprob
h.startprob_prior = startprob_prior
h.transmat_ = normalized(
self.transmat + np.diag(self.prng.rand(self.n_components)), 1)
h.transmat_prior = transmat_prior
h.means_ = 20 * self.means
h.means_prior = means_prior
h.means_weight = means_weight
h.covars_ = self.covars
h.covars_prior = covars_prior
h.covars_weight = covars_weight
lengths = [200] * 10
X, _state_sequence = h.sample(sum(lengths), random_state=self.prng)
# Re-initialize the parameters and check that we can converge to
# the original parameter values.
h_learn = hmm.GaussianHMM(self.n_components, self.covariance_type,
init_params=init_params, params=params,
implementation=implementation,)
# don't use random parameters for testing
init = 1. / h_learn.n_components
h_learn.startprob_ = np.full(h_learn.n_components, init)
h_learn.transmat_ = \
np.full((h_learn.n_components, h_learn.n_components), init)
h_learn.n_iter = 0
h_learn.fit(X, lengths=lengths)
assert_log_likelihood_increasing(h_learn, X, lengths, n_iter)
# Make sure we've converged to the right parameters.
# In general, to account for state switching,
# compare sorted values.
# a) means
assert_allclose(sorted(h.means_.ravel().tolist()),
sorted(h_learn.means_.ravel().tolist()),
0.01)
# b) covars are hard to estimate precisely from a relatively small
# sample, thus the large threshold
# account for how we store the covars_compressed
orig = np.broadcast_to(h._covars_, h_learn._covars_.shape)
> assert_allclose(
sorted(orig.ravel().tolist()),
sorted(h_learn._covars_.ravel().tolist()),
10)
E AssertionError:
E Not equal to tolerance rtol=10, atol=0
E
E Mismatched elements: 2 / 9 (22.2%)
E Max absolute difference among violations: 0.02119294
E Max relative difference among violations: 48.39799969
E ACTUAL: array([-0.215859, -0.215859, -0.020755, -0.020755, 0.370281,
0.370281,
E 0.624374, 0.679083, 3.635532])
E DESIRED: array([-1.927959e-01, -1.927959e-01, 4.378887e-04,
4.378887e-04,
E 3.559196e-01, 3.559196e-01, 6.293362e-01, 6.718302e-01,
E 3.494150e+00])
hmmlearn/tests/test_gaussian_hmm.py:251: AssertionError
=============================== warnings summary ===============================
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gaussian_hmm.py: 2
warnings
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_multisequence.py:
4 warnings
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py: 13
warnings
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_poisson_hmm.py: 1
warning
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_variational_gaussian.py:
27 warnings
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/utils.py:29:
RuntimeWarning: underflow encountered in divide
a /= a_sum
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gaussian_hmm.py: 2
warnings
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_variational_gaussian.py:
30 warnings
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/_emissions.py:153:
RuntimeWarning: underflow encountered in matmul
stats['obs'] += posteriors.T @ X
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gaussian_hmm.py: 1
warning
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_multisequence.py:
5 warnings
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py: 12
warnings
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_poisson_hmm.py: 1
warning
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_variational_gaussian.py:
27 warnings
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/base.py:509:
RuntimeWarning: underflow encountered in multiply
posteriors = fwdlattice * bwdlattice
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gaussian_hmm.py::TestGaussianHMMWithFullCovars::test_fit_zero_variance[scaling]
/usr/lib/python3/dist-packages/numpy/_core/numeric.py:995: RuntimeWarning:
underflow encountered in multiply
return multiply(a.ravel()[:, newaxis], b.ravel()[newaxis, :], out)
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithSphericalCovars::test_fit_zero_variance[scaling]
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/base.py:598:
RuntimeWarning: overflow encountered in exp
return np.exp(self._compute_log_likelihood(X))
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithSphericalCovars::test_fit_zero_variance[scaling]
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithSphericalCovars::test_fit_zero_variance[log]
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/utils.py:55:
RuntimeWarning: invalid value encountered in subtract
a -= a_lse
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance[scaling]
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance[log]
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/hmm.py:744:
RuntimeWarning: invalid value encountered in divide
self.weights_ = w_n / w_d
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance[scaling]
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance[log]
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/hmm.py:756:
RuntimeWarning: invalid value encountered in divide
self.means_ = m_n / m_d[:, :, None]
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance[scaling]
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance[log]
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithFullCovars::test_fit_zero_variance[scaling]
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithFullCovars::test_fit_zero_variance[log]
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/hmm.py:809:
RuntimeWarning: invalid value encountered in divide
self.covars_ = c_n / c_d
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance[scaling]
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance[log]
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithFullCovars::test_fit_zero_variance[scaling]
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithFullCovars::test_fit_zero_variance[log]
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/_emissions.py:208:
RuntimeWarning: divide by zero encountered in log
log_cur_weights = np.log(self.weights_[i_comp])
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_variational_categorical.py:
10 warnings
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_variational_gaussian.py:
20 warnings
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/base.py:1215:
RuntimeWarning: underflow encountered in exp
self.startprob_subnorm_ = np.exp(startprob_log_subnorm)
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_variational_categorical.py:
8 warnings
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_variational_gaussian.py:
12 warnings
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/base.py:1220:
RuntimeWarning: underflow encountered in exp
self.transmat_subnorm_ = np.exp(transmat_log_subnorm)
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_variational_categorical.py:
1 warning
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_variational_gaussian.py:
31 warnings
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/base.py:1153:
RuntimeWarning: underflow encountered in exp
return np.exp(self._compute_subnorm_log_likelihood(X))
.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/tests/test_variational_gaussian.py:
12 warnings
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_hmmlearn/build/hmmlearn/_emissions.py:157:
RuntimeWarning: underflow encountered in matmul
stats['obs**2'] += posteriors.T @ X**2
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================== short test summary info ============================
FAILED
hmmlearn/tests/test_gaussian_hmm.py::TestGaussianHMMWithTiedCovars::test_fit_with_priors[scaling]
===== 1 failed, 293 passed, 17 xfailed, 9 xpassed, 235 warnings in 26.23s ======
E: pybuild pybuild:483: test: plugin pyproject failed with: exit code=1: cd
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_hmmlearn/build; python3.13 -m pytest
--pyargs hmmlearn
dh_auto_test: error: pybuild --test --test-pytest -i python{version} -p "3.14
3.13" returned exit code 13
make: *** [debian/rules:9: binary] Error 25
dpkg-buildpackage: error: debian/rules binary subprocess failed with exit
status 2
--------------------------------------------------------------------------------
--- End Message ---
--- Begin Message ---
Source: python-hmmlearn
Source-Version: 0.3.3-2
Done: Andreas Tille <[email protected]>
We believe that the bug you reported is fixed in the latest version of
python-hmmlearn, 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 [email protected],
and the maintainer will reopen the bug report if appropriate.
Debian distribution maintenance software
pp.
Andreas Tille <[email protected]> (supplier of updated python-hmmlearn 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 [email protected])
-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA512
Format: 1.8
Date: Thu, 29 Oct 2026 17:33:48 +0100
Source: python-hmmlearn
Architecture: source
Version: 0.3.3-2
Distribution: unstable
Urgency: medium
Maintainer: Debian Med Packaging Team
<[email protected]>
Changed-By: Andreas Tille <[email protected]>
Closes: 1129897
Changes:
python-hmmlearn (0.3.3-2) unstable; urgency=medium
.
[ Michael R. Crusoe ]
* d/rules: test invocation: match upstream including the test inside
the tutorial.
* d/watch: upgrade to format 5
.
[ Andreas Tille ]
* Standards-Version: 4.7.4 (routine-update)
* Reflow Uploaders field (cme)
* d/patches: use the random_state passed to make_covar_matrix() for tied
covariances as well; without it the test data came from the global NumPy
RNG, which made TestGaussianHMMWithTiedCovars fail on some seeds
(Closes: #1129897).
* d/rules: only run the test suite for the default Python, since
python3-sklearn is built for the default Python only.
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