Source: statsmodels Version: 0.12.2-2 Severity: serious Justification: FTBFS Tags: bookworm sid ftbfs User: lu...@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.