Source: scipy, lmfit-py Control: found -1 scipy/1.7.1-1 Control: found -1 lmfit-py/1.0.1-6 Severity: serious Tags: sid bookworm X-Debbugs-CC: debian...@lists.debian.org User: debian...@lists.debian.org Usertags: breaks needs-update
Dear maintainer(s),
With a recent upload of scipy the autopkgtest of lmfit-py fails in
testing when that autopkgtest is run with the binary packages of scipy
from unstable. It passes when run with only packages from testing. In
tabular form:
pass fail
scipy from testing 1.7.1-1
lmfit-py from testing 1.0.1-6
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 scipy 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=scipy
https://ci.debian.net/data/autopkgtest/testing/amd64/l/lmfit-py/14750990/log.gz
=================================== FAILURES
===================================
___________________________ test_ampgo_local_solver
____________________________
minimizer_Alpine02 = <lmfit.minimizer.Minimizer object at 0x7fe60c347700>
def test_ampgo_local_solver(minimizer_Alpine02):
"""Test AMPGO algorithm with local solver."""
kws = {'local': 'Nelder-Mead'}
msg = r'Method Nelder-Mead cannot handle constraints nor bounds'
with pytest.warns(RuntimeWarning, match=msg):
> out = minimizer_Alpine02.minimize(method='ampgo', **kws)
E Failed: DID NOT WARN. No warnings of type (<class
'RuntimeWarning'>,) was emitted. The list of emitted warnings is: [].
tests/test_ampgo.py:65: Failed
___________________________ test_shgo_scipy_vs_lmfit
___________________________
def test_shgo_scipy_vs_lmfit():
"""Test SHGO algorithm in lmfit versus SciPy."""
bounds = [(-512, 512), (-512, 512)]
result_scipy = scipy.optimize.shgo(eggholder, bounds, n=30,
sampling_method='sobol')
> assert len(result_scipy.xl) == 13
E assert 6 == 13
E + where 6 = len(array([[ 324.99187533, 216.0475439 ],\n
[-105.87688985, 423.15324143],\n [-242.97923629,
274.38032063],\n [-506.25823477, 6.3131022 ],\n [
91.00922754, -391.28375925],\n [-219.40615102, -244.06022436]]))
E + where array([[ 324.99187533, 216.0475439 ],\n
[-105.87688985, 423.15324143],\n [-242.97923629,
274.38032063],\n [-506.25823477, 6.3131022 ],\n [
91.00922754, -391.28375925],\n [-219.40615102, -244.06022436]]) =
fun: -582.3062800548453\n funl: array([-582.30628005,
-565.99778097, -559.78685655, -557.36868733,\n -426....,\n
[-506.25823477, 6.3131022 ],\n [ 91.00922754,
-391.28375925],\n [-219.40615102, -244.06022436]]).xl
tests/test_shgo.py:32: AssertionError
__________________________ test_shgo_scipy_vs_lmfit_2
__________________________
def test_shgo_scipy_vs_lmfit_2():
"""Test SHGO algorithm in lmfit versus SciPy."""
bounds = [(-512, 512), (-512, 512)]
result_scipy = scipy.optimize.shgo(eggholder, bounds, n=60, iters=5,
sampling_method='sobol')
> assert len(result_scipy.xl) == 39
E assert 74 == 39
E + where 74 = len(array([[ 479.0454122 , 434.50588415],\n
[-465.69415426, 385.71669786],\n [ 347.32696865,
499.41539073],\n ...,\n [ -86.07840448, -6.42281733],\n [
47.99461331, -1.55083384],\n [ 31.65189625, -85.22716952]]))
E + where array([[ 479.0454122 , 434.50588415],\n
[-465.69415426, 385.71669786],\n [ 347.32696865,
499.41539073],\n ...,\n [ -86.07840448, -6.42281733],\n [
47.99461331, -1.55083384],\n [ 31.65189625, -85.22716952]]) =
fun: -955.2551521442906\n funl: array([-955.25515214,
-894.57890039, -888.94912527, -821.19646107,\n -821....,\n [
-86.07840448, -6.42281733],\n [ 47.99461331, -1.55083384],\n
[ 31.65189625, -85.22716952]]).xl
tests/test_shgo.py:51: AssertionError
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