[Debian-med-packaging] Bug#1129897: python-hmmlearn: FTBFS: FAILED hmmlearn/tests/test_gaussian_hmm.py::TestGaussianHMMWithTiedCovars::test_fit_with_priors[scaling]
Santiago Vila
sanvila at debian.org
Thu Mar 5 23:49:14 GMT 2026
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
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