Source: symfit
Version: 0.5.6-3
Severity: serious
Justification: FTBFS
Tags: trixie sid ftbfs
User: lu...@debian.org
Usertags: ftbfs-20240809 ftbfs-trixie
Hi,
During a rebuild of all packages in sid, your package failed to build
on amd64.
Relevant part (hopefully):
> make[1]: Entering directory '/<<PKGBUILDDIR>>'
> dh_auto_test
> I: pybuild base:311: cd /<<PKGBUILDDIR>>/.pybuild/cpython3_3.12/build;
> python3.12 -m pytest tests
> ============================= test session starts
> ==============================
> platform linux -- Python 3.12.5, pytest-8.3.2, pluggy-1.5.0
> rootdir: /<<PKGBUILDDIR>>/.pybuild/cpython3_3.12/build
> configfile: pytest.ini
> collected 123 items
>
> tests/test_argument.py .... [
> 3%]
> tests/test_auto_fit.py ...... [
> 8%]
> tests/test_constrained.py .....s............ [
> 22%]
> tests/test_distributions.py .. [
> 24%]
> tests/test_finite_difference.py ........ [
> 30%]
> tests/test_fit_result.py .......... [
> 39%]
> tests/test_general.py .........s.........F....s...... [
> 64%]
> tests/test_global_opt.py .... [
> 67%]
> tests/test_minimize.py ...... [
> 72%]
> tests/test_minimizers.py ....... [
> 78%]
> tests/test_model.py ............ [
> 87%]
> tests/test_objectives.py ..... [
> 91%]
> tests/test_ode.py ........ [
> 98%]
> tests/test_support.py ..
> [100%]
>
> =================================== FAILURES
> ===================================
> __________________ test_likelihood_fitting_bivariate_gaussian
> __________________
>
> def test_likelihood_fitting_bivariate_gaussian():
> """
> Fit using the likelihood method.
> """
> # Make variables and parameters
> x = Variable('x')
> y = Variable('y')
> x0 = Parameter('x0', value=0.6, min=0.5, max=0.7)
> sig_x = Parameter('sig_x', value=0.1, max=1.0)
> y0 = Parameter('y0', value=0.7, min=0.6, max=0.9)
> sig_y = Parameter('sig_y', value=0.05, max=1.0)
> rho = Parameter('rho', value=0.001, min=-1, max=1)
>
> pdf = BivariateGaussian(x=x, mu_x=x0, sig_x=sig_x, y=y, mu_y=y0,
> sig_y=sig_y, rho=rho)
>
> # Draw 100000 samples from a bivariate distribution
> mean = [0.59, 0.8]
> r = 0.6
> cov = np.array([[0.11 ** 2, 0.11 * 0.23 * r],
> [0.11 * 0.23 * r, 0.23 ** 2]])
> np.random.seed(42)
> # TODO: Do we really need 100k points?
> xdata, ydata = np.random.multivariate_normal(mean, cov, 100000).T
>
> fit = Fit(pdf, x=xdata, y=ydata, objective=LogLikelihood)
> fit_result = fit.execute()
>
> assert fit_result.value(x0) == pytest.approx(mean[0], 1e-2)
> assert fit_result.value(y0) == pytest.approx(mean[1], 1e-2)
> assert fit_result.value(sig_x) == pytest.approx(np.sqrt(cov[0, 0]),
> 1e-2)
> assert fit_result.value(sig_y) == pytest.approx(np.sqrt(cov[1, 1]),
> 1e-2)
> assert fit_result.value(rho) == pytest.approx(r, 1e-2)
>
> marginal = integrate(pdf, (y, -oo, oo), conds='none')
> fit = Fit(marginal, x=xdata, objective=LogLikelihood)
>
> with pytest.raises(NameError):
> # Should raise a NameError, not a TypeError, see #219
> > fit.execute()
>
> tests/test_general.py:615:
> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> _
> symfit/core/fit.py:573: in execute
> minimizer_ans = self.minimizer.execute(**minimize_options)
> symfit/core/minimizers.py:400: in execute
> return super(ScipyGradientMinimize, self).execute(jacobian=jacobian,
> **minimize_options)
> symfit/core/minimizers.py:419: in execute
> return super(ScipyBoundedMinimizer, self).execute(bounds=self.bounds,
> symfit/core/minimizers.py:339: in execute
> ans = minimize(
> /usr/lib/python3/dist-packages/scipy/optimize/_minimize.py:713: in minimize
> res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
> /usr/lib/python3/dist-packages/scipy/optimize/_lbfgsb_py.py:347: in
> _minimize_lbfgsb
> sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
> /usr/lib/python3/dist-packages/scipy/optimize/_optimize.py:288: in
> _prepare_scalar_function
> sf = ScalarFunction(fun, x0, args, grad, hess,
> /usr/lib/python3/dist-packages/scipy/optimize/_differentiable_functions.py:166:
> in __init__
> self._update_fun()
> /usr/lib/python3/dist-packages/scipy/optimize/_differentiable_functions.py:262:
> in _update_fun
> self._update_fun_impl()
> /usr/lib/python3/dist-packages/scipy/optimize/_differentiable_functions.py:163:
> in update_fun
> self.f = fun_wrapped(self.x)
> /usr/lib/python3/dist-packages/scipy/optimize/_differentiable_functions.py:145:
> in fun_wrapped
> fx = fun(np.copy(x), *args)
> symfit/core/objectives.py:454: in __call__
> evaluated_func = super(LogLikelihood, self).__call__(
> symfit/core/objectives.py:93: in __call__
> result = self.model(**key2str(parameters))._asdict()
> symfit/core/models.py:706: in __call__
> return ModelOutput(self.keys(), self.eval_components(*args, **kwargs))
> symfit/core/models.py:654: in eval_components
> kwargs[symbol.name] = components[symbol](**dependencies_kwargs)
> <lambdifygenerated-1398>:2: in _lambdifygenerated
> return (1/2)*exp(-x0**2/(-2*rho**2*sig_x**2 +
> 2*sig_x**2))*exp(-y0**2/(-2*rho**2*sig_y**2 +
> 2*sig_y**2))*exp(-x**2/(-2*rho**2*sig_x**2 +
> 2*sig_x**2))*exp(2*x0*x/(-2*rho**2*sig_x**2 +
> 2*sig_x**2))*exp(2*rho*x0*y0/(-2*rho**2*sig_x*sig_y +
> 2*sig_x*sig_y))*exp(-2*rho*y0*x/(-2*rho**2*sig_x*sig_y +
> 2*sig_x*sig_y))*quad(lambda y: exp(-y**2/(-2*rho**2*sig_y**2 +
> 2*sig_y**2))*exp(2*y0*y/(-2*rho**2*sig_y**2 +
> 2*sig_y**2))*exp(-2*rho*x0*y/(-2*rho**2*sig_x*sig_y +
> 2*sig_x*sig_y))*exp(2*rho*x*y/(-2*rho**2*sig_x*sig_y + 2*sig_x*sig_y)), -inf,
> inf)[0]/(sig_x*sig_y*pi*sqrt(-(rho - 1)*(rho + 1)))
> /usr/lib/python3/dist-packages/scipy/integrate/_quadpack_py.py:464: in quad
> retval = _quad(func, a, b, args, full_output, epsabs, epsrel, limit,
> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> _
>
> func = <function _lambdifygenerated.<locals>.<lambda> at 0x7ff39297bf60>
> a = -inf, b = inf, args = (), full_output = 0, epsabs = 1.49e-08
> epsrel = 1.49e-08, limit = 50, points = None
>
> def _quad(func,a,b,args,full_output,epsabs,epsrel,limit,points):
> infbounds = 0
> if (b != np.inf and a != -np.inf):
> pass # standard integration
> elif (b == np.inf and a != -np.inf):
> infbounds = 1
> bound = a
> elif (b == np.inf and a == -np.inf):
> infbounds = 2
> bound = 0 # ignored
> elif (b != np.inf and a == -np.inf):
> infbounds = -1
> bound = b
> else:
> raise RuntimeError("Infinity comparisons don't work for you.")
>
> if points is None:
> if infbounds == 0:
> return
> _quadpack._qagse(func,a,b,args,full_output,epsabs,epsrel,limit)
> else:
> > return _quadpack._qagie(func, bound, infbounds, args,
> > full_output,
> epsabs, epsrel, limit)
> E TypeError: only length-1 arrays can be converted to Python
> scalars
>
> /usr/lib/python3/dist-packages/scipy/integrate/_quadpack_py.py:613: TypeError
> =============================== warnings summary
> ===============================
> symfit/core/printing.py:13
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.12/build/symfit/core/printing.py:13:
> DeprecationWarning: pkg_resources is deprecated as an API. See
> https://setuptools.pypa.io/en/latest/pkg_resources.html
> import pkg_resources
>
> tests/test_auto_fit.py: 3 warnings
> tests/test_constrained.py: 14 warnings
> tests/test_finite_difference.py: 1 warning
> tests/test_fit_result.py: 5 warnings
> tests/test_general.py: 16 warnings
> tests/test_minimizers.py: 2 warnings
> tests/test_objectives.py: 1 warning
> tests/test_ode.py: 1 warning
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.12/build/symfit/core/fit.py:278:
> DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be
> removed in NumPy 2.0. Please use `prod` instead.
> cov_matrix = self._covariance_matrix(best_fit_params,
>
> tests/test_auto_fit.py: 2 warnings
> tests/test_constrained.py: 13 warnings
> tests/test_finite_difference.py: 2 warnings
> tests/test_fit_result.py: 1 warning
> tests/test_general.py: 12 warnings
> tests/test_global_opt.py: 3 warnings
> tests/test_ode.py: 7 warnings
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.12/build/symfit/core/fit.py:301:
> DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be
> removed in NumPy 2.0. Please use `prod` instead.
> cov_matrix = self._covariance_matrix(best_fit_params,
>
> tests/test_constrained.py: 196 warnings
>
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.12/build/tests/test_constrained.py:766:
> DeprecationWarning: 'scipy.integrate.simps' is deprecated in favour of
> 'scipy.integrate.simpson' and will be removed in SciPy 1.14.0
> {Y: lambda x, y: simps(y, x) - 1}, # Integrate using simps
>
> tests/test_general.py::test_likelihood_fitting_exponential
> /usr/lib/python3/dist-packages/_pytest/python.py:159: DeprecationWarning:
> `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0.
> Please use `prod` instead.
> result = testfunction(**testargs)
>
> -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
> =========================== short test summary info
> ============================
> FAILED tests/test_general.py::test_likelihood_fitting_bivariate_gaussian -
> Ty...
> ====== 1 failed, 119 passed, 3 skipped, 281 warnings in 117.77s (0:01:57)
> ======
> E: pybuild pybuild:389: test: plugin distutils failed with: exit code=1: cd
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.12/build; python3.12 -m pytest tests
> dh_auto_test: error: pybuild --test --test-pytest -i python{version} -p 3.12
> returned exit code 13
The full build log is available from:
http://qa-logs.debian.net/2024/08/09/symfit_0.5.6-3_unstable.log
All bugs filed during this archive rebuild are listed at:
https://bugs.debian.org/cgi-bin/pkgreport.cgi?tag=ftbfs-20240809;users=lu...@debian.org
or:
https://udd.debian.org/bugs/?release=na&merged=ign&fnewerval=7&flastmodval=7&fusertag=only&fusertagtag=ftbfs-20240809&fusertaguser=lu...@debian.org&allbugs=1&cseverity=1&ctags=1&caffected=1#results
A list of current common problems and possible solutions is available at
http://wiki.debian.org/qa.debian.org/FTBFS . You're welcome to contribute!
If you reassign this bug to another package, please mark it as 'affects'-ing
this package. See https://www.debian.org/Bugs/server-control#affects
If you fail to reproduce this, please provide a build log and diff it with mine
so that we can identify if something relevant changed in the meantime.
