Bug#997081: statsmodels: FTBFS: tests fail
Lucas Nussbaum
lucas at debian.org
Sat Oct 23 17:35:40 BST 2021
Source: statsmodels
Version: 0.12.2-2
Severity: serious
Justification: FTBFS
Tags: bookworm sid ftbfs
User: lucas at debian.org
Usertags: ftbfs-20211023 ftbfs-bullseye
Hi,
During a rebuild of all packages in sid, your package failed to build
on amd64.
Relevant part (hopefully):
> =================================== FAILURES ===================================
> ____________________________ TestMICE.test_combine _____________________________
>
> self = <statsmodels.imputation.tests.test_mice.TestMICE object at 0x7f18160740d0>
>
> @pytest.mark.slow
> def test_combine(self):
>
> np.random.seed(3897)
> x1 = np.random.normal(size=300)
> x2 = np.random.normal(size=300)
> y = x1 + x2 + np.random.normal(size=300)
> x1[0:100] = np.nan
> x2[250:] = np.nan
> df = pd.DataFrame({"x1": x1, "x2": x2, "y": y})
> idata = mice.MICEData(df)
> mi = mice.MICE("y ~ x1 + x2", sm.OLS, idata, n_skip=20)
> result = mi.fit(10, 20)
>
> fmi = np.asarray([0.1778143, 0.11057262, 0.29626521])
> > assert_allclose(result.frac_miss_info, fmi, atol=1e-5)
> E AssertionError:
> E Not equal to tolerance rtol=1e-07, atol=1e-05
> E
> E Mismatched elements: 3 / 3 (100%)
> E Max absolute difference: 0.17686937
> E Max relative difference: 1.59957657
> E x: array([0.230217, 0.287442, 0.322124])
> E y: array([0.177814, 0.110573, 0.296265])
>
> ../.pybuild/cpython3_3.9_statsmodels/build/statsmodels/imputation/tests/test_mice.py:366: AssertionError
> __________________________ test_corrpsd_threshold[0] ___________________________
>
> threshold = 0
>
> @pytest.mark.parametrize('threshold', [0, 1e-15, 1e-10, 1e-6])
> def test_corrpsd_threshold(threshold):
> x = np.array([[1, -0.9, -0.9], [-0.9, 1, -0.9], [-0.9, -0.9, 1]])
>
> y = corr_nearest(x, n_fact=100, threshold=threshold)
> evals = np.linalg.eigvalsh(y)
> > assert_allclose(evals[0], threshold, rtol=1e-6, atol=1e-15)
> E AssertionError:
> E Not equal to tolerance rtol=1e-06, atol=1e-15
> E
> E Mismatched elements: 1 / 1 (100%)
> E Max absolute difference: 1.05471187e-15
> E Max relative difference: inf
> E x: array(1.054712e-15)
> E y: array(0)
>
> ../.pybuild/cpython3_3.9_statsmodels/build/statsmodels/stats/tests/test_corrpsd.py:196: AssertionError
> _________________________________ test_mixedlm _________________________________
>
> def test_mixedlm():
>
> np.random.seed(3424)
>
> n = 200
>
> # The exposure (not time varying)
> x = np.random.normal(size=n)
> xv = np.outer(x, np.ones(3))
>
> # The mediator (with random intercept)
> mx = np.asarray([4., 4, 1])
> mx /= np.sqrt(np.sum(mx**2))
> med = mx[0] * np.outer(x, np.ones(3))
> med += mx[1] * np.outer(np.random.normal(size=n), np.ones(3))
> med += mx[2] * np.random.normal(size=(n, 3))
>
> # The outcome (exposure and mediator effects)
> ey = np.outer(x, np.r_[0, 0.5, 1]) + med
>
> # Random structure of the outcome (random intercept and slope)
> ex = np.asarray([5., 2, 2])
> ex /= np.sqrt(np.sum(ex**2))
> e = ex[0] * np.outer(np.random.normal(size=n), np.ones(3))
> e += ex[1] * np.outer(np.random.normal(size=n), np.r_[-1, 0, 1])
> e += ex[2] * np.random.normal(size=(n, 3))
> y = ey + e
>
> # Group membership
> idx = np.outer(np.arange(n), np.ones(3))
>
> # Time
> tim = np.outer(np.ones(n), np.r_[-1, 0, 1])
>
> df = pd.DataFrame({"y": y.flatten(), "x": xv.flatten(),
> "id": idx.flatten(), "time": tim.flatten(),
> "med": med.flatten()})
>
> mediator_model = sm.MixedLM.from_formula("med ~ x", groups="id", data=df)
> outcome_model = sm.MixedLM.from_formula("y ~ med + x", groups="id", data=df)
> me = Mediation(outcome_model, mediator_model, "x", "med")
> mr = me.fit(n_rep=2)
> st = mr.summary()
> pm = st.loc["Prop. mediated (average)", "Estimate"]
> > assert_allclose(pm, 0.52, rtol=1e-2, atol=1e-2)
> E AssertionError:
> E Not equal to tolerance rtol=0.01, atol=0.01
> E
> E Mismatched elements: 1 / 1 (100%)
> E Max absolute difference: 0.01958632
> E Max relative difference: 0.03766599
> E x: array(0.539586)
> E y: array(0.52)
>
> ../.pybuild/cpython3_3.9_statsmodels/build/statsmodels/stats/tests/test_mediation.py:214: AssertionError
> =============================== warnings summary ===============================
> ../../../../usr/lib/python3/dist-packages/_pytest/config/__init__.py:1183
> /usr/lib/python3/dist-packages/_pytest/config/__init__.py:1183: PytestDeprecationWarning: The --strict option is deprecated, use --strict-markers instead.
> self.issue_config_time_warning(
>
> base/tests/test_penalized.py: 4 warnings
> base/tests/test_shrink_pickle.py: 3 warnings
> discrete/tests/test_count_model.py: 6 warnings
> discrete/tests/test_discrete.py: 6 warnings
> genmod/tests/test_glm.py: 1 warning
> tsa/arima/estimators/tests/test_innovations.py: 1 warning
> tsa/statespace/tests/test_exponential_smoothing.py: 1 warning
> tsa/statespace/tests/test_fixed_params.py: 1 warning
> tsa/tests/test_arima.py: 1 warning
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
> warnings.warn("Maximum Likelihood optimization failed to "
>
> discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_fit_regularized
> discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_fit_regularized
> discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_fit_regularized
> discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_fit_regularized
> discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_fit_regularized
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 4 out of 4 parameters
> Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
> warnings.warn(message, ConvergenceWarning)
>
> discrete/tests/test_count_model.py: 8 warnings
> discrete/tests/test_discrete.py: 2 warnings
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/base/l1_solvers_common.py:144: ConvergenceWarning: Could not trim params automatically due to failed QC check. Trimming using trim_mode == 'size' will still work.
> warnings.warn(msg, ConvergenceWarning)
>
> discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_fit_regularized
> discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_fit_regularized
> discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_fit_regularized
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 3 out of 6 parameters
> Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
> warnings.warn(message, ConvergenceWarning)
>
> discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_fit_regularized
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 4 out of 6 parameters
> Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
> warnings.warn(message, ConvergenceWarning)
>
> discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_fit_regularized
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 1 out of 5 parameters
> Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
> warnings.warn(message, ConvergenceWarning)
>
> discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_fit_regularized
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 3 out of 7 parameters
> Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
> warnings.warn(message, ConvergenceWarning)
>
> discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_null
> discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP_predict2::test_mean
> tsa/tests/test_arima.py::test_predict_exog_missing
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/base/model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
> warnings.warn('Inverting hessian failed, no bse or cov_params '
>
> discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_fit_regularized
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 1 out of 2 parameters
> Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
> warnings.warn(message, ConvergenceWarning)
>
> discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_fit_regularized
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 1 out of 3 parameters
> Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
> warnings.warn(message, ConvergenceWarning)
>
> discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_fit_regularized
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 2 out of 5 parameters
> Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
> warnings.warn(message, ConvergenceWarning)
>
> discrete/tests/test_discrete.py::TestPoissonL1Compatability::test_params
> discrete/tests/test_discrete.py::TestNegativeBinomialGeoL1Compatability::test_params
> discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_fit_regularized
> discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_fit_regularized
> discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_fit_regularized
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/discrete/discrete_model.py:1062: RuntimeWarning: overflow encountered in exp
> return np.sum(-np.exp(XB) + endog*XB - gammaln(endog+1))
>
> discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_fit_regularized
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 8 out of 10 parameters
> Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
> warnings.warn(message, ConvergenceWarning)
>
> discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_fit_regularized
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 2 out of 11 parameters
> Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
> warnings.warn(message, ConvergenceWarning)
>
> genmod/tests/test_glm.py::TestGlmGamma::test_null_deviance
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/genmod/generalized_linear_model.py:293: DomainWarning: The inverse_power link function does not respect the domain of the Gamma family.
> warnings.warn((f"The {type(family.link).__name__} link function "
>
> genmod/tests/test_glm.py::TestGlmGammaIdentity::test_null_deviance
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/genmod/generalized_linear_model.py:293: DomainWarning: The identity link function does not respect the domain of the Gamma family.
> warnings.warn((f"The {type(family.link).__name__} link function "
>
> genmod/tests/test_glm.py::TestGlmInvgaussIdentity::test_null_deviance
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/genmod/generalized_linear_model.py:293: DomainWarning: The identity link function does not respect the domain of the InverseGaussian family.
> warnings.warn((f"The {type(family.link).__name__} link function "
>
> genmod/tests/test_glm.py::TestWtdGlmNegativeBinomial::test_null_deviance
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/genmod/generalized_linear_model.py:293: DomainWarning: The nbinom link function does not respect the domain of the NegativeBinomial family.
> warnings.warn((f"The {type(family.link).__name__} link function "
>
> graphics/tests/test_tsaplots.py::test_plot_pacf
> graphics/tests/test_tsaplots.py::test_plot_pacf
> graphics/tests/test_tsaplots.py::test_plot_pacf_kwargs
> graphics/tests/test_tsaplots.py::test_plot_pacf_kwargs
> graphics/tests/test_tsaplots.py::test_plot_pacf_kwargs
> graphics/tests/test_tsaplots.py::test_plot_pacf_irregular
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/regression/linear_model.py:1434: RuntimeWarning: invalid value encountered in sqrt
> return rho, np.sqrt(sigmasq)
>
> nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_unordered_CV_LS
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/nonparametric/kernel_density.py:679: RuntimeWarning: invalid value encountered in double_scalars
> CV += (G / m_x ** 2) - 2 * (f_X_Y / m_x)
>
> nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_continuousdata_lc_cvls
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/nonparametric/kernel_regression.py:251: RuntimeWarning: invalid value encountered in true_divide
> B_x = (G_numer * d_fx - G_denom * d_mx) / (G_denom**2)
>
> regression/tests/test_dimred.py::test_covreduce
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/regression/dimred.py:694: ConvergenceWarning: CovReduce optimization did not converge, |g|=1.287955
> warnings.warn(msg, ConvergenceWarning)
>
> regression/tests/test_processreg.py::test_formulas
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/regression/process_regression.py:632: UserWarning: Fitting did not converge, |gradient|=0.000053
> warnings.warn(msg)
>
> robust/tests/test_scale.py::TestMad::test_mad_empty
> /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice.
> return _methods._mean(a, axis=axis, dtype=dtype,
>
> robust/tests/test_scale.py::TestMad::test_mad_empty
> /usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars
> ret = ret.dtype.type(ret / rcount)
>
> robust/tests/test_scale.py::TestHuberAxes::test_axis1
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/robust/scale.py:298: RuntimeWarning: divide by zero encountered in true_divide
> subset = np.less_equal(np.abs((a - mu) / scale), self.c)
>
> sandbox/distributions/tests/test_extras.py::test_skewt
> /usr/lib/python3/dist-packages/scipy/stats/_continuous_distns.py:6315: RuntimeWarning: overflow encountered in double_scalars
> / (np.sqrt(r*np.pi)*(1+(x**2)/r)**((r+1)/2)))
>
> sandbox/tests/test_gam.py::TestGAMGaussianLogLink::test_predict
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/sandbox/gam.py:327: IterationLimitWarning:
> Maximum iteration reached.
>
> warnings.warn(iteration_limit_doc, IterationLimitWarning)
>
> stats/tests/test_corrpsd.py::TestCovPSD::test_cov_nearest
> stats/tests/test_corrpsd.py::TestCorrPSD1::test_nearest
> stats/tests/test_corrpsd.py::test_corrpsd_threshold[0]
> stats/tests/test_corrpsd.py::test_corrpsd_threshold[1e-15]
> stats/tests/test_corrpsd.py::test_corrpsd_threshold[1e-10]
> stats/tests/test_corrpsd.py::test_corrpsd_threshold[1e-06]
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/stats/correlation_tools.py:90: IterationLimitWarning:
> Maximum iteration reached.
>
> warnings.warn(iteration_limit_doc, IterationLimitWarning)
>
> stats/tests/test_descriptivestats.py::test_empty_columns
> stats/tests/test_descriptivestats.py::test_empty_columns
> /usr/lib/python3/dist-packages/numpy/lib/nanfunctions.py:1113: RuntimeWarning: All-NaN slice encountered
> r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
>
> stats/tests/test_diagnostic.py::TestDiagnosticG::test_normality
> stats/tests/test_diagnostic.py::TestDiagnosticGPandas::test_normality
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/stats/_adnorm.py:70: RuntimeWarning: divide by zero encountered in log1p
> s = np.sum((2 * i[sl1] - 1.0) / nobs * (np.log(z) + np.log1p(-z[sl2])),
>
> stats/tests/test_pairwise.py::TestTuckeyHSD2::test_plot_simultaneous_ci
> stats/tests/test_pairwise.py::TestTuckeyHSD2Pandas::test_plot_simultaneous_ci
> stats/tests/test_pairwise.py::TestTuckeyHSD2s::test_plot_simultaneous_ci
> stats/tests/test_pairwise.py::TestTuckeyHSD3::test_plot_simultaneous_ci
> stats/tests/test_pairwise.py::TestTuckeyHSD4::test_plot_simultaneous_ci
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/sandbox/stats/multicomp.py:775: UserWarning: FixedFormatter should only be used together with FixedLocator
> ax1.set_yticklabels(np.insert(self.groupsunique.astype(str), 0, ''))
>
> stats/tests/test_power.py::test_power_solver_warn
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/stats/power.py:106: RuntimeWarning: invalid value encountered in sqrt
> pow_ = stats.norm.sf(crit - d*np.sqrt(nobs)/sigma)
>
> stats/tests/test_tost.py::test_tost_asym
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/stats/weightstats.py:1477: RuntimeWarning: invalid value encountered in log
> low = transform(low)
>
> tsa/holtwinters/tests/test_holtwinters.py::test_start_params[add-mul]
> tsa/holtwinters/tests/test_holtwinters.py::test_start_params[mul-mul]
> tsa/holtwinters/tests/test_holtwinters.py::test_start_params[None-mul]
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/tsa/holtwinters/model.py:80: RuntimeWarning: overflow encountered in matmul
> return err.T @ err
>
> tsa/holtwinters/tests/test_holtwinters.py::test_start_params[add-mul]
> tsa/holtwinters/tests/test_holtwinters.py::test_alternative_minimizers[TNC]
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/tsa/holtwinters/model.py:920: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
> warnings.warn(
>
> tsa/holtwinters/tests/test_holtwinters.py::test_alternative_minimizers[trust-constr]
> /usr/lib/python3/dist-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.
> warn('delta_grad == 0.0. Check if the approximated '
>
> tsa/statespace/tests/test_mlemodel.py::test_integer_params
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/tsa/statespace/kalman_filter.py:1768: RuntimeWarning: invalid value encountered in double_scalars
> self.scale = np.sum(scale_obs[d:]) / nobs_k_endog
>
> tsa/statespace/tests/test_sarimax.py::test_plot_too_few_obs
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/tsa/statespace/sarimax.py:866: UserWarning: Too few observations to estimate starting parameters for ARMA and trend. All parameters except for variances will be set to zeros.
> warn('Too few observations to estimate starting parameters%s.'
>
> tsa/statespace/tests/test_sarimax.py::test_plot_too_few_obs
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/tsa/statespace/mlemodel.py:1220: RuntimeWarning: invalid value encountered in true_divide
> np.inner(score_obs, score_obs) /
>
> tsa/statespace/tests/test_sarimax.py::test_plot_too_few_obs
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/tsa/statespace/sarimax.py:866: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.
> warn('Too few observations to estimate starting parameters%s.'
>
> tsa/statespace/tests/test_sarimax.py::test_plot_too_few_obs
> /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3621: RuntimeWarning: Degrees of freedom <= 0 for slice
> return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
>
> tsa/statespace/tests/test_sarimax.py::test_plot_too_few_obs
> /usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide
> arrmean = um.true_divide(
>
> tsa/statespace/tests/test_sarimax.py::test_plot_too_few_obs
> /usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars
> ret = ret.dtype.type(ret / rcount)
>
> tsa/statespace/tests/test_varmax.py::TestVAR_exog::test_predict
> tsa/statespace/tests/test_varmax.py::TestVAR_exog2::test_predict
> tsa/statespace/tests/test_varmax.py::test_misc_exog
> tsa/statespace/tests/test_varmax.py::test_misc_exog
> tsa/statespace/tests/test_varmax.py::test_misc_exog
> tsa/statespace/tests/test_varmax.py::test_misc_exog
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/tsa/statespace/mlemodel.py:1766: ValueWarning: Exogenous array provided, but additional data is not required. `exog` argument ignored.
> warnings.warn('Exogenous array provided, but additional data'
>
> tsa/tests/test_exponential_smoothing.py::test_hessian
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/tsa/exponential_smoothing/base.py:262: PrecisionWarning: Calculation of the Hessian using finite differences is usually subject to substantial approximation errors.
> warnings.warn('Calculation of the Hessian using finite differences'
>
> tsa/vector_ar/tests/test_var.py::TestVARResults::test_plot_irf
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/tsa/vector_ar/plotting.py:208: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
> fig, axes = plt.subplots(nrows=nrows, ncols=ncols, sharex=True,
>
> tsa/vector_ar/tests/test_var.py::test_correct_nobs
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_statsmodels/build/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency Q-DEC will be used.
> warnings.warn('No frequency information was'
>
> -- Docs: https://docs.pytest.org/en/stable/warnings.html
> =========================== short test summary info ============================
> FAILED ../.pybuild/cpython3_3.9_statsmodels/build/statsmodels/imputation/tests/test_mice.py::TestMICE::test_combine
> FAILED ../.pybuild/cpython3_3.9_statsmodels/build/statsmodels/stats/tests/test_corrpsd.py::test_corrpsd_threshold[0]
> FAILED ../.pybuild/cpython3_3.9_statsmodels/build/statsmodels/stats/tests/test_mediation.py::test_mixedlm
> = 3 failed, 15025 passed, 289 skipped, 140 xfailed, 10 xpassed, 117 warnings in 1081.34s (0:18:01) =
> make[1]: *** [debian/rules:117: override_dh_auto_test] Error 1
The full build log is available from:
http://qa-logs.debian.net/2021/10/23/statsmodels_0.12.2-2_unstable.log
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 marking 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.
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