>>> py3-bayeso: Building community/py3-bayeso 0.6.0-r1 (using abuild 3.16.0_rc4-r0) started Tue, 04 Nov 2025 10:05:05 +0000 >>> py3-bayeso: Validating /home/buildozer/aports/community/py3-bayeso/APKBUILD... >>> py3-bayeso: Analyzing dependencies... >>> py3-bayeso: Installing for build: build-base python3 py3-cma py3-numpy py3-scipy py3-tqdm py3-gpep517 py3-setuptools py3-wheel py3-pytest-xdist py3-pytest-benchmark py3-pytest-timeout ( 1/104) Installing libbz2 (1.0.8-r6) ( 2/104) Installing libffi (3.5.2-r0) ( 3/104) Installing gdbm (1.26-r0) ( 4/104) Installing xz-libs (5.8.1-r0) ( 5/104) Installing mpdecimal (4.0.1-r0) ( 6/104) Installing libpanelw (6.5_p20251010-r0) ( 7/104) Installing sqlite-libs (3.50.4-r1) ( 8/104) Installing python3 (3.12.12-r0) ( 9/104) Installing python3-pycache-pyc0 (3.12.12-r0) ( 10/104) Installing pyc (3.12.12-r0) ( 11/104) Installing python3-pyc (3.12.12-r0) ( 12/104) Installing libgfortran (15.2.0-r2) ( 13/104) Installing openblas (0.3.30-r0) ( 14/104) 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OK: 785 MiB in 209 packages >>> py3-bayeso: Cleaning up srcdir >>> py3-bayeso: Cleaning up pkgdir >>> py3-bayeso: Cleaning up tmpdir >>> py3-bayeso: Fetching https://distfiles.alpinelinux.org/distfiles/edge/bayeso-0.6.0.tar.gz /var/cache/distfiles/bayeso-0.6.0.tar.gz: OK >>> py3-bayeso: Fetching https://distfiles.alpinelinux.org/distfiles/edge/bayeso-0.6.0.tar.gz /var/cache/distfiles/bayeso-0.6.0.tar.gz: OK >>> py3-bayeso: Unpacking /var/cache/distfiles/bayeso-0.6.0.tar.gz... 2025-11-04 10:06:04,188 gpep517 INFO Building wheel via backend setuptools.build_meta /usr/lib/python3.12/site-packages/setuptools/config/_apply_pyprojecttoml.py:82: SetuptoolsDeprecationWarning: `project.license` as a TOML table is deprecated !! ******************************************************************************** Please use a simple string containing a SPDX expression for `project.license`. You can also use `project.license-files`. (Both options available on setuptools>=77.0.0). By 2026-Feb-18, you need to update your project and remove deprecated calls or your builds will no longer be supported. See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! corresp(dist, value, root_dir) /usr/lib/python3.12/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! dist._finalize_license_expression() /usr/lib/python3.12/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: MIT License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! self._finalize_license_expression() 2025-11-04 10:06:04,283 root INFO running bdist_wheel 2025-11-04 10:06:04,335 root INFO running build 2025-11-04 10:06:04,335 root INFO running build_py 2025-11-04 10:06:04,346 root INFO creating build/lib/bayeso 2025-11-04 10:06:04,347 root INFO copying bayeso/constants.py -> build/lib/bayeso 2025-11-04 10:06:04,347 root INFO copying bayeso/covariance.py -> build/lib/bayeso 2025-11-04 10:06:04,348 root INFO copying bayeso/thompson_sampling.py -> build/lib/bayeso 2025-11-04 10:06:04,348 root INFO copying bayeso/__init__.py -> build/lib/bayeso 2025-11-04 10:06:04,349 root INFO copying bayeso/acquisition.py -> build/lib/bayeso 2025-11-04 10:06:04,349 root INFO creating build/lib/bayeso/bo 2025-11-04 10:06:04,350 root INFO copying bayeso/bo/bo_w_tp.py -> build/lib/bayeso/bo 2025-11-04 10:06:04,350 root INFO copying bayeso/bo/bo_w_gp.py -> build/lib/bayeso/bo 2025-11-04 10:06:04,351 root INFO copying bayeso/bo/bo_w_trees.py -> build/lib/bayeso/bo 2025-11-04 10:06:04,351 root INFO copying bayeso/bo/base_bo.py -> build/lib/bayeso/bo 2025-11-04 10:06:04,351 root INFO copying bayeso/bo/__init__.py -> build/lib/bayeso/bo 2025-11-04 10:06:04,352 root INFO creating build/lib/bayeso/gp 2025-11-04 10:06:04,352 root INFO copying bayeso/gp/gp_likelihood.py -> build/lib/bayeso/gp 2025-11-04 10:06:04,353 root INFO copying bayeso/gp/__init__.py -> build/lib/bayeso/gp 2025-11-04 10:06:04,353 root INFO copying bayeso/gp/gp.py -> build/lib/bayeso/gp 2025-11-04 10:06:04,354 root INFO copying bayeso/gp/gp_kernel.py -> build/lib/bayeso/gp 2025-11-04 10:06:04,354 root INFO creating build/lib/bayeso/tp 2025-11-04 10:06:04,355 root INFO copying bayeso/tp/tp_likelihood.py -> build/lib/bayeso/tp 2025-11-04 10:06:04,355 root INFO copying bayeso/tp/tp_kernel.py -> build/lib/bayeso/tp 2025-11-04 10:06:04,355 root INFO copying bayeso/tp/__init__.py -> build/lib/bayeso/tp 2025-11-04 10:06:04,356 root INFO copying bayeso/tp/tp.py -> build/lib/bayeso/tp 2025-11-04 10:06:04,356 root INFO creating build/lib/bayeso/trees 2025-11-04 10:06:04,357 root INFO copying bayeso/trees/trees_random_forest.py -> build/lib/bayeso/trees 2025-11-04 10:06:04,357 root INFO copying bayeso/trees/trees_generic_trees.py -> build/lib/bayeso/trees 2025-11-04 10:06:04,358 root INFO copying bayeso/trees/__init__.py -> build/lib/bayeso/trees 2025-11-04 10:06:04,358 root INFO copying bayeso/trees/trees_common.py -> build/lib/bayeso/trees 2025-11-04 10:06:04,359 root INFO creating build/lib/bayeso/wrappers 2025-11-04 10:06:04,359 root INFO copying bayeso/wrappers/wrappers_bo_class.py -> build/lib/bayeso/wrappers 2025-11-04 10:06:04,359 root INFO copying bayeso/wrappers/__init__.py -> build/lib/bayeso/wrappers 2025-11-04 10:06:04,360 root INFO copying bayeso/wrappers/wrappers_bo_function.py -> build/lib/bayeso/wrappers 2025-11-04 10:06:04,360 root INFO creating build/lib/bayeso/utils 2025-11-04 10:06:04,361 root INFO copying bayeso/utils/utils_logger.py -> build/lib/bayeso/utils 2025-11-04 10:06:04,361 root INFO copying bayeso/utils/utils_gp.py -> build/lib/bayeso/utils 2025-11-04 10:06:04,362 root INFO copying bayeso/utils/utils_common.py -> build/lib/bayeso/utils 2025-11-04 10:06:04,362 root INFO copying bayeso/utils/__init__.py -> build/lib/bayeso/utils 2025-11-04 10:06:04,362 root INFO copying bayeso/utils/utils_plotting.py -> build/lib/bayeso/utils 2025-11-04 10:06:04,363 root INFO copying bayeso/utils/utils_bo.py -> build/lib/bayeso/utils 2025-11-04 10:06:04,363 root INFO copying bayeso/utils/utils_covariance.py -> build/lib/bayeso/utils 2025-11-04 10:06:04,364 root INFO running egg_info 2025-11-04 10:06:04,374 root INFO creating bayeso.egg-info 2025-11-04 10:06:04,374 root INFO writing bayeso.egg-info/PKG-INFO 2025-11-04 10:06:04,379 root INFO writing dependency_links to bayeso.egg-info/dependency_links.txt 2025-11-04 10:06:04,381 root INFO writing requirements to bayeso.egg-info/requires.txt 2025-11-04 10:06:04,381 root INFO writing top-level names to bayeso.egg-info/top_level.txt 2025-11-04 10:06:04,381 root INFO writing manifest file 'bayeso.egg-info/SOURCES.txt' 2025-11-04 10:06:04,395 root INFO reading manifest file 'bayeso.egg-info/SOURCES.txt' 2025-11-04 10:06:04,396 root INFO reading manifest template 'MANIFEST.in' 2025-11-04 10:06:04,396 root INFO adding license file 'LICENSE' 2025-11-04 10:06:04,398 root INFO writing manifest file 'bayeso.egg-info/SOURCES.txt' 2025-11-04 10:06:04,422 root INFO installing to build/bdist.linux-loongarch64/wheel 2025-11-04 10:06:04,422 root INFO running install 2025-11-04 10:06:04,439 root INFO running install_lib 2025-11-04 10:06:04,450 root INFO creating build/bdist.linux-loongarch64/wheel 2025-11-04 10:06:04,450 root INFO creating build/bdist.linux-loongarch64/wheel/bayeso 2025-11-04 10:06:04,451 root INFO creating build/bdist.linux-loongarch64/wheel/bayeso/utils 2025-11-04 10:06:04,451 root INFO copying build/lib/bayeso/utils/utils_logger.py -> build/bdist.linux-loongarch64/wheel/./bayeso/utils 2025-11-04 10:06:04,451 root INFO copying build/lib/bayeso/utils/utils_gp.py -> build/bdist.linux-loongarch64/wheel/./bayeso/utils 2025-11-04 10:06:04,452 root INFO copying build/lib/bayeso/utils/utils_common.py -> build/bdist.linux-loongarch64/wheel/./bayeso/utils 2025-11-04 10:06:04,452 root INFO copying build/lib/bayeso/utils/__init__.py -> build/bdist.linux-loongarch64/wheel/./bayeso/utils 2025-11-04 10:06:04,452 root INFO copying build/lib/bayeso/utils/utils_plotting.py -> build/bdist.linux-loongarch64/wheel/./bayeso/utils 2025-11-04 10:06:04,453 root INFO copying build/lib/bayeso/utils/utils_bo.py -> build/bdist.linux-loongarch64/wheel/./bayeso/utils 2025-11-04 10:06:04,453 root INFO copying build/lib/bayeso/utils/utils_covariance.py -> build/bdist.linux-loongarch64/wheel/./bayeso/utils 2025-11-04 10:06:04,454 root INFO creating build/bdist.linux-loongarch64/wheel/bayeso/bo 2025-11-04 10:06:04,454 root INFO copying build/lib/bayeso/bo/bo_w_tp.py -> build/bdist.linux-loongarch64/wheel/./bayeso/bo 2025-11-04 10:06:04,454 root INFO copying build/lib/bayeso/bo/bo_w_gp.py -> build/bdist.linux-loongarch64/wheel/./bayeso/bo 2025-11-04 10:06:04,455 root INFO copying build/lib/bayeso/bo/bo_w_trees.py -> build/bdist.linux-loongarch64/wheel/./bayeso/bo 2025-11-04 10:06:04,455 root INFO copying build/lib/bayeso/bo/base_bo.py -> build/bdist.linux-loongarch64/wheel/./bayeso/bo 2025-11-04 10:06:04,455 root INFO copying build/lib/bayeso/bo/__init__.py -> build/bdist.linux-loongarch64/wheel/./bayeso/bo 2025-11-04 10:06:04,456 root INFO copying build/lib/bayeso/constants.py -> build/bdist.linux-loongarch64/wheel/./bayeso 2025-11-04 10:06:04,456 root INFO creating build/bdist.linux-loongarch64/wheel/bayeso/tp 2025-11-04 10:06:04,456 root INFO copying build/lib/bayeso/tp/tp_likelihood.py -> build/bdist.linux-loongarch64/wheel/./bayeso/tp 2025-11-04 10:06:04,457 root INFO copying build/lib/bayeso/tp/tp_kernel.py -> build/bdist.linux-loongarch64/wheel/./bayeso/tp 2025-11-04 10:06:04,457 root INFO copying build/lib/bayeso/tp/__init__.py -> build/bdist.linux-loongarch64/wheel/./bayeso/tp 2025-11-04 10:06:04,457 root INFO copying build/lib/bayeso/tp/tp.py -> build/bdist.linux-loongarch64/wheel/./bayeso/tp 2025-11-04 10:06:04,458 root INFO copying build/lib/bayeso/covariance.py -> build/bdist.linux-loongarch64/wheel/./bayeso 2025-11-04 10:06:04,458 root INFO creating build/bdist.linux-loongarch64/wheel/bayeso/gp 2025-11-04 10:06:04,458 root INFO copying build/lib/bayeso/gp/gp_likelihood.py -> build/bdist.linux-loongarch64/wheel/./bayeso/gp 2025-11-04 10:06:04,459 root INFO copying build/lib/bayeso/gp/__init__.py -> build/bdist.linux-loongarch64/wheel/./bayeso/gp 2025-11-04 10:06:04,459 root INFO copying build/lib/bayeso/gp/gp.py -> build/bdist.linux-loongarch64/wheel/./bayeso/gp 2025-11-04 10:06:04,459 root INFO copying build/lib/bayeso/gp/gp_kernel.py -> build/bdist.linux-loongarch64/wheel/./bayeso/gp 2025-11-04 10:06:04,460 root INFO copying build/lib/bayeso/thompson_sampling.py -> build/bdist.linux-loongarch64/wheel/./bayeso 2025-11-04 10:06:04,460 root INFO copying build/lib/bayeso/__init__.py -> build/bdist.linux-loongarch64/wheel/./bayeso 2025-11-04 10:06:04,460 root INFO copying build/lib/bayeso/acquisition.py -> build/bdist.linux-loongarch64/wheel/./bayeso 2025-11-04 10:06:04,461 root INFO creating build/bdist.linux-loongarch64/wheel/bayeso/trees 2025-11-04 10:06:04,461 root INFO copying build/lib/bayeso/trees/trees_random_forest.py -> build/bdist.linux-loongarch64/wheel/./bayeso/trees 2025-11-04 10:06:04,462 root INFO copying build/lib/bayeso/trees/trees_generic_trees.py -> build/bdist.linux-loongarch64/wheel/./bayeso/trees 2025-11-04 10:06:04,462 root INFO copying build/lib/bayeso/trees/__init__.py -> build/bdist.linux-loongarch64/wheel/./bayeso/trees 2025-11-04 10:06:04,462 root INFO copying build/lib/bayeso/trees/trees_common.py -> build/bdist.linux-loongarch64/wheel/./bayeso/trees 2025-11-04 10:06:04,463 root INFO creating build/bdist.linux-loongarch64/wheel/bayeso/wrappers 2025-11-04 10:06:04,463 root INFO copying build/lib/bayeso/wrappers/wrappers_bo_class.py -> build/bdist.linux-loongarch64/wheel/./bayeso/wrappers 2025-11-04 10:06:04,463 root INFO copying build/lib/bayeso/wrappers/__init__.py -> build/bdist.linux-loongarch64/wheel/./bayeso/wrappers 2025-11-04 10:06:04,464 root INFO copying build/lib/bayeso/wrappers/wrappers_bo_function.py -> build/bdist.linux-loongarch64/wheel/./bayeso/wrappers 2025-11-04 10:06:04,464 root INFO running install_egg_info 2025-11-04 10:06:04,473 root INFO Copying bayeso.egg-info to build/bdist.linux-loongarch64/wheel/./bayeso-0.6.0-py3.12.egg-info 2025-11-04 10:06:04,475 root INFO running install_scripts 2025-11-04 10:06:04,477 root INFO creating build/bdist.linux-loongarch64/wheel/bayeso-0.6.0.dist-info/WHEEL 2025-11-04 10:06:04,477 wheel INFO creating '/home/buildozer/aports/community/py3-bayeso/src/bayeso-0.6.0/.dist/.tmp-6965zqz3/bayeso-0.6.0-py3-none-any.whl' and adding 'build/bdist.linux-loongarch64/wheel' to it 2025-11-04 10:06:04,478 wheel INFO adding 'bayeso/__init__.py' 2025-11-04 10:06:04,478 wheel INFO adding 'bayeso/acquisition.py' 2025-11-04 10:06:04,479 wheel INFO adding 'bayeso/constants.py' 2025-11-04 10:06:04,479 wheel INFO adding 'bayeso/covariance.py' 2025-11-04 10:06:04,480 wheel INFO adding 'bayeso/thompson_sampling.py' 2025-11-04 10:06:04,480 wheel INFO adding 'bayeso/bo/__init__.py' 2025-11-04 10:06:04,481 wheel INFO adding 'bayeso/bo/base_bo.py' 2025-11-04 10:06:04,481 wheel INFO adding 'bayeso/bo/bo_w_gp.py' 2025-11-04 10:06:04,482 wheel INFO adding 'bayeso/bo/bo_w_tp.py' 2025-11-04 10:06:04,482 wheel INFO adding 'bayeso/bo/bo_w_trees.py' 2025-11-04 10:06:04,483 wheel INFO adding 'bayeso/gp/__init__.py' 2025-11-04 10:06:04,483 wheel INFO adding 'bayeso/gp/gp.py' 2025-11-04 10:06:04,483 wheel INFO adding 'bayeso/gp/gp_kernel.py' 2025-11-04 10:06:04,484 wheel INFO adding 'bayeso/gp/gp_likelihood.py' 2025-11-04 10:06:04,484 wheel INFO adding 'bayeso/tp/__init__.py' 2025-11-04 10:06:04,485 wheel INFO adding 'bayeso/tp/tp.py' 2025-11-04 10:06:04,485 wheel INFO adding 'bayeso/tp/tp_kernel.py' 2025-11-04 10:06:04,485 wheel INFO adding 'bayeso/tp/tp_likelihood.py' 2025-11-04 10:06:04,486 wheel INFO adding 'bayeso/trees/__init__.py' 2025-11-04 10:06:04,486 wheel INFO adding 'bayeso/trees/trees_common.py' 2025-11-04 10:06:04,487 wheel INFO adding 'bayeso/trees/trees_generic_trees.py' 2025-11-04 10:06:04,487 wheel INFO adding 'bayeso/trees/trees_random_forest.py' 2025-11-04 10:06:04,487 wheel INFO adding 'bayeso/utils/__init__.py' 2025-11-04 10:06:04,488 wheel INFO adding 'bayeso/utils/utils_bo.py' 2025-11-04 10:06:04,488 wheel INFO adding 'bayeso/utils/utils_common.py' 2025-11-04 10:06:04,488 wheel INFO adding 'bayeso/utils/utils_covariance.py' 2025-11-04 10:06:04,489 wheel INFO adding 'bayeso/utils/utils_gp.py' 2025-11-04 10:06:04,489 wheel INFO adding 'bayeso/utils/utils_logger.py' 2025-11-04 10:06:04,490 wheel INFO adding 'bayeso/utils/utils_plotting.py' 2025-11-04 10:06:04,490 wheel INFO adding 'bayeso/wrappers/__init__.py' 2025-11-04 10:06:04,491 wheel INFO adding 'bayeso/wrappers/wrappers_bo_class.py' 2025-11-04 10:06:04,491 wheel INFO adding 'bayeso/wrappers/wrappers_bo_function.py' 2025-11-04 10:06:04,492 wheel INFO adding 'bayeso-0.6.0.dist-info/licenses/LICENSE' 2025-11-04 10:06:04,492 wheel INFO adding 'bayeso-0.6.0.dist-info/METADATA' 2025-11-04 10:06:04,492 wheel INFO adding 'bayeso-0.6.0.dist-info/WHEEL' 2025-11-04 10:06:04,493 wheel INFO adding 'bayeso-0.6.0.dist-info/top_level.txt' 2025-11-04 10:06:04,493 wheel INFO adding 'bayeso-0.6.0.dist-info/RECORD' 2025-11-04 10:06:04,494 root INFO removing build/bdist.linux-loongarch64/wheel 2025-11-04 10:06:04,496 gpep517 INFO The backend produced .dist/bayeso-0.6.0-py3-none-any.whl bayeso-0.6.0-py3-none-any.whl /usr/lib/python3.12/site-packages/pytest_benchmark/logger.py:46: PytestBenchmarkWarning: Benchmarks are automatically disabled because xdist plugin is active.Benchmarks cannot be performed reliably in a parallelized environment. warner(PytestBenchmarkWarning(text)) ============================= test session starts ============================== platform linux -- Python 3.12.12, pytest-8.4.2, pluggy-1.6.0 benchmark: 4.0.0 (defaults: timer=time.perf_counter disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000) rootdir: /home/buildozer/aports/community/py3-bayeso/src/bayeso-0.6.0 configfile: pytest.ini plugins: xdist-3.8.0, timeout-2.3.1, benchmark-4.0.0 timeout: 30.0s timeout method: signal timeout func_only: False created: 32/32 workers 32 workers [252 items] ........................................................................ 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[100%] =================================== FAILURES =================================== ____________________________ test_optimize_str_acq _____________________________ [gw20] linux -- Python 3.12.12 /home/buildozer/aports/community/py3-bayeso/src/bayeso-0.6.0/.testenv/bin/python3 def test_optimize_str_acq(): np.random.seed(42) arr_range_1 = np.array([ [0.0, 10.0], [-2.0, 2.0], [-5.0, 5.0], ]) dim_X = arr_range_1.shape[0] num_X = 5 X = np.random.randn(num_X, dim_X) Y = np.random.randn(num_X, 1) model_bo = BO(arr_range_1, str_acq='pi') next_point, dict_info = model_bo.optimize(X, Y) next_points = dict_info['next_points'] acquisitions = dict_info['acquisitions'] cov_X_X = dict_info['cov_X_X'] inv_cov_X_X = dict_info['inv_cov_X_X'] hyps = dict_info['hyps'] time_overall = dict_info['time_overall'] time_surrogate = dict_info['time_surrogate'] time_acq = dict_info['time_acq'] assert isinstance(next_point, np.ndarray) assert isinstance(next_points, np.ndarray) assert isinstance(acquisitions, np.ndarray) assert isinstance(cov_X_X, np.ndarray) assert isinstance(inv_cov_X_X, np.ndarray) assert isinstance(hyps, dict) assert isinstance(time_overall, float) assert isinstance(time_surrogate, float) assert isinstance(time_acq, float) assert len(next_point.shape) == 1 assert len(next_points.shape) == 2 assert len(acquisitions.shape) == 1 assert next_point.shape[0] == dim_X assert next_points.shape[1] == dim_X assert next_points.shape[0] == acquisitions.shape[0] model_bo = BO(arr_range_1, str_acq='ucb') > next_point, dict_info = model_bo.optimize(X, Y) ^^^^^^^^^^^^^^^^^^^^^^^ tests/common/test_bo_bo_w_tp.py:423: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ bayeso/bo/bo_w_tp.py:381: in optimize next_point, next_points = self._optimize(fun_negative_acquisition, bayeso/bo/bo_w_tp.py:137: in _optimize next_point = minimize( /usr/lib/python3.12/site-packages/scipy/optimize/_minimize.py:784: in minimize res = _minimize_lbfgsb(fun, x0, args, jac, bounds, _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ fun = . at 0x7fffa20540e0> x0 = array([ 8.51404595, 1.46808194, -1.35574541]), args = (), jac = None bounds = array([[ 0., -2., -5.], [10., 2., 5.]]), disp = False maxcor = 10, ftol = 2.220446049250313e-09, gtol = 1e-05, eps = 1e-08 maxfun = 15000, maxiter = 15000, iprint = callback = None, maxls = 20, finite_diff_rel_step = None, workers = None unknown_options = {}, m = 10, pgtol = 1e-05, factr = np.float64(10000000.0) n = 3 sf = func_and_grad = > nbd = array([2, 2, 2], dtype=int32) def _minimize_lbfgsb(fun, x0, args=(), jac=None, bounds=None, disp=_NoValue, maxcor=10, ftol=2.2204460492503131e-09, gtol=1e-5, eps=1e-8, maxfun=15000, maxiter=15000, iprint=_NoValue, callback=None, maxls=20, finite_diff_rel_step=None, workers=None, **unknown_options): """ Minimize a scalar function of one or more variables using the L-BFGS-B algorithm. Options ------- disp : None or int Deprecated option that previously controlled the text printed on the screen during the problem solution. Now the code does not emit any output and this keyword has no function. .. deprecated:: 1.15.0 This keyword is deprecated and will be removed from SciPy 1.18.0. maxcor : int The maximum number of variable metric corrections used to define the limited memory matrix. (The limited memory BFGS method does not store the full hessian but uses this many terms in an approximation to it.) ftol : float The iteration stops when ``(f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= ftol``. gtol : float The iteration will stop when ``max{|proj g_i | i = 1, ..., n} <= gtol`` where ``proj g_i`` is the i-th component of the projected gradient. eps : float or ndarray If `jac is None` the absolute step size used for numerical approximation of the jacobian via forward differences. maxfun : int Maximum number of function evaluations before minimization terminates. Note that this function may violate the limit if the gradients are evaluated by numerical differentiation. maxiter : int Maximum number of algorithm iterations. iprint : int, optional Deprecated option that previously controlled the text printed on the screen during the problem solution. Now the code does not emit any output and this keyword has no function. .. deprecated:: 1.15.0 This keyword is deprecated and will be removed from SciPy 1.18.0. maxls : int, optional Maximum number of line search steps (per iteration). Default is 20. finite_diff_rel_step : None or array_like, optional If ``jac in ['2-point', '3-point', 'cs']`` the relative step size to use for numerical approximation of the jacobian. The absolute step size is computed as ``h = rel_step * sign(x) * max(1, abs(x))``, possibly adjusted to fit into the bounds. For ``method='3-point'`` the sign of `h` is ignored. If None (default) then step is selected automatically. workers : int, map-like callable, optional A map-like callable, such as `multiprocessing.Pool.map` for evaluating any numerical differentiation in parallel. This evaluation is carried out as ``workers(fun, iterable)``. .. versionadded:: 1.16.0 Notes ----- The option `ftol` is exposed via the `scipy.optimize.minimize` interface, but calling `scipy.optimize.fmin_l_bfgs_b` directly exposes `factr`. The relationship between the two is ``ftol = factr * numpy.finfo(float).eps``. I.e., `factr` multiplies the default machine floating-point precision to arrive at `ftol`. If the minimization is slow to converge the optimizer may halt if the total number of function evaluations exceeds `maxfun`, or the number of algorithm iterations has reached `maxiter` (whichever comes first). If this is the case then ``result.success=False``, and an appropriate error message is contained in ``result.message``. """ _check_unknown_options(unknown_options) m = maxcor pgtol = gtol factr = ftol / np.finfo(float).eps x0 = asarray(x0).ravel() n, = x0.shape if disp is not _NoValue: warnings.warn("scipy.optimize: The `disp` and `iprint` options of the " "L-BFGS-B solver are deprecated and will be removed in " "SciPy 1.18.0.", DeprecationWarning, stacklevel=3) if iprint is not _NoValue: warnings.warn("scipy.optimize: The `disp` and `iprint` options of the " "L-BFGS-B solver are deprecated and will be removed in " "SciPy 1.18.0.", DeprecationWarning, stacklevel=3) # historically old-style bounds were/are expected by lbfgsb. # That's still the case but we'll deal with new-style from here on, # it's easier if bounds is None: pass elif len(bounds) != n: raise ValueError('length of x0 != length of bounds') else: bounds = np.array(old_bound_to_new(bounds)) # check bounds if (bounds[0] > bounds[1]).any(): raise ValueError( "LBFGSB - one of the lower bounds is greater than an upper bound." ) # initial vector must lie within the bounds. Otherwise ScalarFunction and # approx_derivative will cause problems x0 = np.clip(x0, bounds[0], bounds[1]) # _prepare_scalar_function can use bounds=None to represent no bounds sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps, bounds=bounds, finite_diff_rel_step=finite_diff_rel_step, workers=workers) func_and_grad = sf.fun_and_grad nbd = zeros(n, np.int32) low_bnd = zeros(n, float64) upper_bnd = zeros(n, float64) bounds_map = {(-np.inf, np.inf): 0, (1, np.inf): 1, (1, 1): 2, (-np.inf, 1): 3} if bounds is not None: for i in range(0, n): L, U = bounds[0, i], bounds[1, i] if not np.isinf(L): low_bnd[i] = L L = 1 if not np.isinf(U): upper_bnd[i] = U U = 1 nbd[i] = bounds_map[L, U] if not maxls > 0: raise ValueError('maxls must be positive.') x = array(x0, dtype=np.float64) f = array(0.0, dtype=np.float64) g = zeros((n,), dtype=np.float64) wa = zeros(2*m*n + 5*n + 11*m*m + 8*m, float64) iwa = zeros(3*n, dtype=np.int32) task = zeros(2, dtype=np.int32) ln_task = zeros(2, dtype=np.int32) lsave = zeros(4, dtype=np.int32) isave = zeros(44, dtype=np.int32) dsave = zeros(29, dtype=float64) n_iterations = 0 while True: # g may become float32 if a user provides a function that calculates # the Jacobian in float32 (see gh-18730). The underlying code expects # float64, so upcast it g = g.astype(np.float64) # x, f, g, wa, iwa, task, csave, lsave, isave, dsave = \ > _lbfgsb.setulb(m, x, low_bnd, upper_bnd, nbd, f, g, factr, pgtol, wa, iwa, task, lsave, isave, dsave, maxls, ln_task) E Failed: Timeout >30.0s /usr/lib/python3.12/site-packages/scipy/optimize/_lbfgsb_py.py:461: Failed ----------------------------- Captured stdout call ----------------------------- ~~~~~~~~~~~~~~~~~~~~~ Stack of (140737239710472) ~~~~~~~~~~~~~~~~~~~~~ File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 411, in _perform_spawn reply.run() File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 341, in run self._result = func(*args, **kwargs) File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 1160, in _thread_receiver msg = Message.from_io(io) File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 567, in from_io header = io.read(9) # type 1, channel 4, payload 4 File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 534, in read data = self._read(numbytes - len(buf)) ____________________________ test_optimize_str_acq _____________________________ [gw11] linux -- Python 3.12.12 /home/buildozer/aports/community/py3-bayeso/src/bayeso-0.6.0/.testenv/bin/python3 def test_optimize_str_acq(): np.random.seed(42) arr_range_1 = np.array([ [0.0, 10.0], [-2.0, 2.0], [-5.0, 5.0], ]) dim_X = arr_range_1.shape[0] num_X = 5 X = np.random.randn(num_X, dim_X) Y = np.random.randn(num_X, 1) model_bo = BO(arr_range_1, str_acq='pi') next_point, dict_info = model_bo.optimize(X, Y) next_points = dict_info['next_points'] acquisitions = dict_info['acquisitions'] cov_X_X = dict_info['cov_X_X'] inv_cov_X_X = dict_info['inv_cov_X_X'] hyps = dict_info['hyps'] time_overall = dict_info['time_overall'] time_surrogate = dict_info['time_surrogate'] time_acq = dict_info['time_acq'] assert isinstance(next_point, np.ndarray) assert isinstance(next_points, np.ndarray) assert isinstance(acquisitions, np.ndarray) assert isinstance(cov_X_X, np.ndarray) assert isinstance(inv_cov_X_X, np.ndarray) assert isinstance(hyps, dict) assert isinstance(time_overall, float) assert isinstance(time_surrogate, float) assert isinstance(time_acq, float) assert len(next_point.shape) == 1 assert len(next_points.shape) == 2 assert len(acquisitions.shape) == 1 assert next_point.shape[0] == dim_X assert next_points.shape[1] == dim_X assert next_points.shape[0] == acquisitions.shape[0] model_bo = BO(arr_range_1, str_acq='ucb') next_point, dict_info = model_bo.optimize(X, Y) next_points = dict_info['next_points'] acquisitions = dict_info['acquisitions'] cov_X_X = dict_info['cov_X_X'] inv_cov_X_X = dict_info['inv_cov_X_X'] hyps = dict_info['hyps'] time_overall = dict_info['time_overall'] time_surrogate = dict_info['time_surrogate'] time_acq = dict_info['time_acq'] assert isinstance(next_point, np.ndarray) assert isinstance(next_points, np.ndarray) assert isinstance(acquisitions, np.ndarray) assert isinstance(cov_X_X, np.ndarray) assert isinstance(inv_cov_X_X, np.ndarray) assert isinstance(hyps, dict) assert isinstance(time_overall, float) assert isinstance(time_surrogate, float) assert isinstance(time_acq, float) assert len(next_point.shape) == 1 assert len(next_points.shape) == 2 assert len(acquisitions.shape) == 1 assert next_point.shape[0] == dim_X assert next_points.shape[1] == dim_X assert next_points.shape[0] == acquisitions.shape[0] model_bo = BO(arr_range_1, str_acq='aei') next_point, dict_info = model_bo.optimize(X, Y) next_points = dict_info['next_points'] acquisitions = dict_info['acquisitions'] cov_X_X = dict_info['cov_X_X'] inv_cov_X_X = dict_info['inv_cov_X_X'] hyps = dict_info['hyps'] time_overall = dict_info['time_overall'] time_surrogate = dict_info['time_surrogate'] time_acq = dict_info['time_acq'] assert isinstance(next_point, np.ndarray) assert isinstance(next_points, np.ndarray) assert isinstance(acquisitions, np.ndarray) assert isinstance(cov_X_X, np.ndarray) assert isinstance(inv_cov_X_X, np.ndarray) assert isinstance(hyps, dict) assert isinstance(time_overall, float) assert isinstance(time_surrogate, float) assert isinstance(time_acq, float) assert len(next_point.shape) == 1 assert len(next_points.shape) == 2 assert len(acquisitions.shape) == 1 assert next_point.shape[0] == dim_X assert next_points.shape[1] == dim_X assert next_points.shape[0] == acquisitions.shape[0] model_bo = BO(arr_range_1, str_acq='pure_exploit') > next_point, dict_info = model_bo.optimize(X, Y) ^^^^^^^^^^^^^^^^^^^^^^^ tests/common/test_bo_bo_w_gp.py:485: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ bayeso/bo/bo_w_gp.py:459: in optimize next_point, next_points = self._optimize(fun_negative_acquisition, bayeso/bo/bo_w_gp.py:146: in _optimize next_point = minimize( /usr/lib/python3.12/site-packages/scipy/optimize/_minimize.py:784: in minimize res = _minimize_lbfgsb(fun, x0, args, jac, bounds, /usr/lib/python3.12/site-packages/scipy/optimize/_lbfgsb_py.py:469: in _minimize_lbfgsb f, g = func_and_grad(x) ^^^^^^^^^^^^^^^^ /usr/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py:404: in fun_and_grad self._update_grad() /usr/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py:366: in _update_grad self.g = self._wrapped_grad(self.x, f0=self.f) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py:41: in __call__ g, dct = approx_derivative( /usr/lib/python3.12/site-packages/scipy/optimize/_numdiff.py:593: in approx_derivative J, _nfev = _dense_difference(fun_wrapped, x0, f0, h, /usr/lib/python3.12/site-packages/scipy/optimize/_numdiff.py:686: in _dense_difference df = [f_eval - f0 for f_eval in f_evals] ^^^^^^^ /usr/lib/python3.12/site-packages/scipy/optimize/_numdiff.py:879: in __call__ f = np.atleast_1d(self.fun(x, *self.args, **self.kwargs)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = x = array([ 6.52595682, -1.77850294, -0.7075442 ]) def __call__(self, x): # Send a copy because the user may overwrite it. # The user of this class might want `x` to remain unchanged. fx = self.f(np.copy(x), *self.args) self.nfev += 1 # Make sure the function returns a true scalar if not np.isscalar(fx): try: > fx = np.asarray(fx).item() ^^^^^^^^^^^^^^^^^^^^^ E Failed: Timeout >30.0s /usr/lib/python3.12/site-packages/scipy/_lib/_util.py:596: Failed ----------------------------- Captured stdout call ----------------------------- ~~~~~~~~~~~~~~~~~~~~~ Stack of (140737231715080) ~~~~~~~~~~~~~~~~~~~~~ File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 411, in _perform_spawn reply.run() File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 341, in run self._result = func(*args, **kwargs) File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 1160, in _thread_receiver msg = Message.from_io(io) File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 567, in from_io header = io.read(9) # type 1, channel 4, payload 4 File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 534, in read data = self._read(numbytes - len(buf)) ____________________________ test_optimize_use_ard _____________________________ [gw17] linux -- Python 3.12.12 /home/buildozer/aports/community/py3-bayeso/src/bayeso-0.6.0/.testenv/bin/python3 def test_optimize_use_ard(): np.random.seed(42) arr_range = np.array([ [0.0, 10.0], [-2.0, 2.0], [-5.0, 5.0], ]) dim_X = arr_range.shape[0] num_X = 5 X = np.random.randn(num_X, dim_X) Y = np.random.randn(num_X, 1) model_bo = BO(arr_range, use_ard=False) next_point, dict_info = model_bo.optimize(X, Y) next_points = dict_info['next_points'] acquisitions = dict_info['acquisitions'] cov_X_X = dict_info['cov_X_X'] inv_cov_X_X = dict_info['inv_cov_X_X'] hyps = dict_info['hyps'] time_overall = dict_info['time_overall'] time_surrogate = dict_info['time_surrogate'] time_acq = dict_info['time_acq'] assert isinstance(next_point, np.ndarray) assert isinstance(next_points, np.ndarray) assert isinstance(acquisitions, np.ndarray) assert isinstance(cov_X_X, np.ndarray) assert isinstance(inv_cov_X_X, np.ndarray) assert isinstance(hyps, dict) assert isinstance(time_overall, float) assert isinstance(time_surrogate, float) assert isinstance(time_acq, float) assert len(next_point.shape) == 1 assert len(next_points.shape) == 2 assert len(acquisitions.shape) == 1 assert next_point.shape[0] == dim_X assert next_points.shape[1] == dim_X assert next_points.shape[0] == acquisitions.shape[0] assert isinstance(hyps['lengthscales'], float) X = np.array([ [3.0, 0.0, 1.0], [2.0, -1.0, 4.0], [9.0, 1.5, 3.0], ]) Y = np.array([ [100.0], [100.0], [100.0], ]) model_bo = BO(arr_range, use_ard=True) > next_point, dict_info = model_bo.optimize(X, Y) ^^^^^^^^^^^^^^^^^^^^^^^ tests/common/test_bo_bo_w_tp.py:650: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ bayeso/bo/bo_w_tp.py:381: in optimize next_point, next_points = self._optimize(fun_negative_acquisition, bayeso/bo/bo_w_tp.py:137: in _optimize next_point = minimize( /usr/lib/python3.12/site-packages/scipy/optimize/_minimize.py:784: in minimize res = _minimize_lbfgsb(fun, x0, args, jac, bounds, /usr/lib/python3.12/site-packages/scipy/optimize/_lbfgsb_py.py:469: in _minimize_lbfgsb f, g = func_and_grad(x) ^^^^^^^^^^^^^^^^ /usr/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py:404: in fun_and_grad self._update_grad() /usr/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py:366: in _update_grad self.g = self._wrapped_grad(self.x, f0=self.f) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py:41: in __call__ g, dct = approx_derivative( /usr/lib/python3.12/site-packages/scipy/optimize/_numdiff.py:593: in approx_derivative J, _nfev = _dense_difference(fun_wrapped, x0, f0, h, /usr/lib/python3.12/site-packages/scipy/optimize/_numdiff.py:686: in _dense_difference df = [f_eval - f0 for f_eval in f_evals] ^^^^^^^ /usr/lib/python3.12/site-packages/scipy/optimize/_numdiff.py:879: in __call__ f = np.atleast_1d(self.fun(x, *self.args, **self.kwargs)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.12/site-packages/scipy/_lib/_util.py:590: in __call__ fx = self.f(np.copy(x), *self.args) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ bayeso/bo/bo_w_tp.py:378: in fun_negative_acquisition = lambda X_test: -1.0 * self.compute_acquisitions( bayeso/bo/bo_w_tp.py:304: in compute_acquisitions acquisitions = fun_acquisition( bayeso/utils/utils_common.py:33: in _validate_types return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ bayeso/acquisition.py:88: in ei + pred_std * scipy.stats.norm.pdf(val_z) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.12/site-packages/scipy/stats/_distn_infrastructure.py:2071: in pdf if np.any(cond): ^^^^^^^^^^^^ /usr/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:2580: in any return _wrapreduction_any_all(a, np.logical_or, 'any', axis, out, _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ obj = array([1]), ufunc = , method = 'any', axis = None out = None, kwargs = {'keepdims': , 'where': } def _wrapreduction_any_all(obj, ufunc, method, axis, out, **kwargs): # Same as above function, but dtype is always bool (but never passed on) > passkwargs = {k: v for k, v in kwargs.items() ^^^^ if v is not np._NoValue} E Failed: Timeout >30.0s /usr/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:91: Failed ----------------------------- Captured stdout call ----------------------------- ~~~~~~~~~~~~~~~~~~~~~ Stack of (140737205631752) ~~~~~~~~~~~~~~~~~~~~~ File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 411, in _perform_spawn reply.run() File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 341, in run self._result = func(*args, **kwargs) File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 1160, in _thread_receiver msg = Message.from_io(io) File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 567, in from_io header = io.read(9) # type 1, channel 4, payload 4 File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 534, in read data = self._read(numbytes - len(buf)) ____________________________ test_optimize_use_ard _____________________________ [gw12] linux -- Python 3.12.12 /home/buildozer/aports/community/py3-bayeso/src/bayeso-0.6.0/.testenv/bin/python3 def test_optimize_use_ard(): np.random.seed(42) arr_range = np.array([ [0.0, 10.0], [-2.0, 2.0], [-5.0, 5.0], ]) dim_X = arr_range.shape[0] num_X = 5 X = np.random.randn(num_X, dim_X) Y = np.random.randn(num_X, 1) model_bo = BO(arr_range, use_ard=False) next_point, dict_info = model_bo.optimize(X, Y) next_points = dict_info['next_points'] acquisitions = dict_info['acquisitions'] cov_X_X = dict_info['cov_X_X'] inv_cov_X_X = dict_info['inv_cov_X_X'] hyps = dict_info['hyps'] time_overall = dict_info['time_overall'] time_surrogate = dict_info['time_surrogate'] time_acq = dict_info['time_acq'] assert isinstance(next_point, np.ndarray) assert isinstance(next_points, np.ndarray) assert isinstance(acquisitions, np.ndarray) assert isinstance(cov_X_X, np.ndarray) assert isinstance(inv_cov_X_X, np.ndarray) assert isinstance(hyps, dict) assert isinstance(time_overall, float) assert isinstance(time_surrogate, float) assert isinstance(time_acq, float) assert len(next_point.shape) == 1 assert len(next_points.shape) == 2 assert len(acquisitions.shape) == 1 assert next_point.shape[0] == dim_X assert next_points.shape[1] == dim_X assert next_points.shape[0] == acquisitions.shape[0] assert isinstance(hyps['lengthscales'], float) X = np.array([ [3.0, 0.0, 1.0], [2.0, -1.0, 4.0], [9.0, 1.5, 3.0], ]) Y = np.array([ [100.0], [100.0], [100.0], ]) model_bo = BO(arr_range, use_ard=True) > next_point, dict_info = model_bo.optimize(X, Y) ^^^^^^^^^^^^^^^^^^^^^^^ tests/common/test_bo_bo_w_gp.py:763: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ bayeso/bo/bo_w_gp.py:459: in optimize next_point, next_points = self._optimize(fun_negative_acquisition, bayeso/bo/bo_w_gp.py:146: in _optimize next_point = minimize( /usr/lib/python3.12/site-packages/scipy/optimize/_minimize.py:784: in minimize res = _minimize_lbfgsb(fun, x0, args, jac, bounds, /usr/lib/python3.12/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps, /usr/lib/python3.12/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function sf = ScalarFunction(fun, x0, args, grad, hess, /usr/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__ self._update_fun() /usr/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun fx = self._wrapped_fun(self.x) ^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.12/site-packages/scipy/_lib/_util.py:590: in __call__ fx = self.f(np.copy(x), *self.args) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ bayeso/bo/bo_w_gp.py:456: in fun_negative_acquisition = lambda X_test: -1.0 * self.compute_acquisitions( bayeso/bo/bo_w_gp.py:314: in compute_acquisitions acquisitions = fun_acquisition( bayeso/utils/utils_common.py:33: in _validate_types return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ bayeso/acquisition.py:88: in ei + pred_std * scipy.stats.norm.pdf(val_z) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.12/site-packages/scipy/stats/_distn_infrastructure.py:2067: in pdf cond1 = self._support_mask(x, *args) & (scale > 0) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = x = array([0.00011402]), args = () > def _support_mask(self, x, *args): E Failed: Timeout >30.0s /usr/lib/python3.12/site-packages/scipy/stats/_distn_infrastructure.py:1028: Failed ----------------------------- Captured stdout call ----------------------------- ~~~~~~~~~~~~~~~~~~~~~ Stack of (140737252752136) ~~~~~~~~~~~~~~~~~~~~~ File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 411, in _perform_spawn reply.run() File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 341, in run self._result = func(*args, **kwargs) File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 1160, in _thread_receiver msg = Message.from_io(io) File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 567, in from_io header = io.read(9) # type 1, channel 4, payload 4 File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 534, in read data = self._read(numbytes - len(buf)) ____________________________ test_run_single_round _____________________________ [gw4] linux -- Python 3.12.12 /home/buildozer/aports/community/py3-bayeso/src/bayeso-0.6.0/.testenv/bin/python3 def test_run_single_round(): np.random.seed(42) arr_range = np.array([ [-5.0, 5.0], ]) dim_X = arr_range.shape[0] num_X = 3 num_iter = 10 fun_target = lambda x: x**2 - 2.0 * x + 1.0 model_bo = bo.BO(arr_range, debug=True) with pytest.raises(AssertionError) as error: package_target.run_single_round(1, fun_target, num_X, num_iter) with pytest.raises(AssertionError) as error: package_target.run_single_round(model_bo, 1, num_X, num_iter) with pytest.raises(AssertionError) as error: package_target.run_single_round(model_bo, fun_target, 1.2, num_iter) with pytest.raises(AssertionError) as error: package_target.run_single_round(model_bo, fun_target, num_X, 1.2) with pytest.raises(AssertionError) as error: package_target.run_single_round(model_bo, fun_target, num_X, num_iter, str_initial_method_bo=1) with pytest.raises(AssertionError) as error: package_target.run_single_round(model_bo, fun_target, num_X, num_iter, str_initial_method_bo='abc') with pytest.raises(AssertionError) as error: package_target.run_single_round(model_bo, fun_target, num_X, num_iter, str_initial_method_bo='grid') with pytest.raises(AssertionError) as error: package_target.run_single_round(model_bo, fun_target, num_X, num_iter, str_sampling_method_ao=1) with pytest.raises(AssertionError) as error: package_target.run_single_round(model_bo, fun_target, num_X, num_iter, str_sampling_method_ao='abc') with pytest.raises(AssertionError) as error: package_target.run_single_round(model_bo, fun_target, num_X, num_iter, seed=1.2) > X_final, Y_final, time_all_final, time_surrogate_final, time_acq_final = package_target.run_single_round(model_bo, fun_target, num_X, num_iter, str_initial_method_bo='uniform') ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ tests/common/test_wrappers_bo_function.py:175: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ bayeso/utils/utils_common.py:33: in _validate_types return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ bayeso/wrappers/wrappers_bo_function.py:299: in run_single_round = run_single_round_with_initial_inputs( bayeso/utils/utils_common.py:33: in _validate_types return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ bayeso/wrappers/wrappers_bo_function.py:198: in run_single_round_with_initial_inputs = run_single_round_with_all_initial_information( bayeso/utils/utils_common.py:33: in _validate_types return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ bayeso/wrappers/wrappers_bo_function.py:88: in run_single_round_with_all_initial_information next_point, dict_info = model_bo.optimize(X_final, Y_final, bayeso/bo/bo_w_gp.py:459: in optimize next_point, next_points = self._optimize(fun_negative_acquisition, bayeso/bo/bo_w_gp.py:146: in _optimize next_point = minimize( /usr/lib/python3.12/site-packages/scipy/optimize/_minimize.py:784: in minimize res = _minimize_lbfgsb(fun, x0, args, jac, bounds, /usr/lib/python3.12/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps, /usr/lib/python3.12/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function sf = ScalarFunction(fun, x0, args, grad, hess, /usr/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py:283: in __init__ self._update_grad() /usr/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py:366: in _update_grad self.g = self._wrapped_grad(self.x, f0=self.f) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py:41: in __call__ g, dct = approx_derivative( /usr/lib/python3.12/site-packages/scipy/optimize/_numdiff.py:593: in approx_derivative J, _nfev = _dense_difference(fun_wrapped, x0, f0, h, /usr/lib/python3.12/site-packages/scipy/optimize/_numdiff.py:686: in _dense_difference df = [f_eval - f0 for f_eval in f_evals] ^^^^^^^ /usr/lib/python3.12/site-packages/scipy/optimize/_numdiff.py:879: in __call__ f = np.atleast_1d(self.fun(x, *self.args, **self.kwargs)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.12/site-packages/scipy/_lib/_util.py:590: in __call__ fx = self.f(np.copy(x), *self.args) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ bayeso/bo/bo_w_gp.py:456: in fun_negative_acquisition = lambda X_test: -1.0 * self.compute_acquisitions( bayeso/bo/bo_w_gp.py:314: in compute_acquisitions acquisitions = fun_acquisition( bayeso/utils/utils_common.py:33: in _validate_types return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ bayeso/acquisition.py:88: in ei + pred_std * scipy.stats.norm.pdf(val_z) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.12/site-packages/scipy/stats/_distn_infrastructure.py:2071: in pdf if np.any(cond): ^^^^^^^^^^^^ /usr/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:2580: in any return _wrapreduction_any_all(a, np.logical_or, 'any', axis, out, _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ obj = array([1]), ufunc = , method = 'any', axis = None out = None, kwargs = {'keepdims': , 'where': } passkwargs = {} def _wrapreduction_any_all(obj, ufunc, method, axis, out, **kwargs): # Same as above function, but dtype is always bool (but never passed on) passkwargs = {k: v for k, v in kwargs.items() if v is not np._NoValue} if type(obj) is not mu.ndarray: try: reduction = getattr(obj, method) except AttributeError: pass else: return reduction(axis=axis, out=out, **passkwargs) > return ufunc.reduce(obj, axis, bool, out, **passkwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E Failed: Timeout >30.0s /usr/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:102: Failed ----------------------------- Captured stdout call ----------------------------- ~~~~~~~~~~~~~~~~~~~~~ Stack of (140737226603272) ~~~~~~~~~~~~~~~~~~~~~ File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 411, in _perform_spawn reply.run() File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 341, in run self._result = func(*args, **kwargs) File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 1160, in _thread_receiver msg = Message.from_io(io) File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 567, in from_io header = io.read(9) # type 1, channel 4, payload 4 File "/usr/lib/python3.12/site-packages/execnet/gateway_base.py", line 534, in read data = self._read(numbytes - len(buf)) ----------------------------- Captured stderr call ----------------------------- [INFO-bo_w_gp-11/04/2025-10:06:17] range_X: [[-5.000, 5.000]] [INFO-bo_w_gp-11/04/2025-10:06:17] str_cov: matern52 [INFO-bo_w_gp-11/04/2025-10:06:17] str_acq: ei [INFO-bo_w_gp-11/04/2025-10:06:17] str_optimizer_method_gp: BFGS [INFO-bo_w_gp-11/04/2025-10:06:17] str_optimizer_method_bo: L-BFGS-B [INFO-bo_w_gp-11/04/2025-10:06:17] str_modelselection_method: ml [INFO-bo_w_gp-11/04/2025-10:06:17] num_init: 3 [INFO-bo_w_gp-11/04/2025-10:06:17] num_iter: 10 [INFO-bo_w_gp-11/04/2025-10:06:17] str_initial_method_bo: sobol [INFO-bo_w_gp-11/04/2025-10:06:17] str_sampling_method_ao: abc [INFO-bo_w_gp-11/04/2025-10:06:17] num_samples_ao: 128 [INFO-bo_w_gp-11/04/2025-10:06:17] str_mlm_method: regular [INFO-bo_w_gp-11/04/2025-10:06:17] seed: None [DEBUG-bo_w_gp-11/04/2025-10:06:17] samples: [[1.152], [-4.035], [-0.224]] [DEBUG-bo_w_gp-11/04/2025-10:06:17] X_init: [[1.152], [-4.035], [-0.224]] [INFO-bo_w_gp-11/04/2025-10:06:17] Iteration 1 [INFO-bo_w_gp-11/04/2025-10:06:17] range_X: [[-5.000, 5.000]] [INFO-bo_w_gp-11/04/2025-10:06:17] str_cov: matern52 [INFO-bo_w_gp-11/04/2025-10:06:17] str_acq: ei [INFO-bo_w_gp-11/04/2025-10:06:17] str_optimizer_method_gp: BFGS [INFO-bo_w_gp-11/04/2025-10:06:17] str_optimizer_method_bo: L-BFGS-B [INFO-bo_w_gp-11/04/2025-10:06:17] str_modelselection_method: ml [INFO-bo_w_gp-11/04/2025-10:06:17] num_init: 3 [INFO-bo_w_gp-11/04/2025-10:06:17] num_iter: 10 [INFO-bo_w_gp-11/04/2025-10:06:17] str_initial_method_bo: uniform [INFO-bo_w_gp-11/04/2025-10:06:17] str_sampling_method_ao: sobol [INFO-bo_w_gp-11/04/2025-10:06:17] num_samples_ao: 128 [INFO-bo_w_gp-11/04/2025-10:06:17] str_mlm_method: regular [INFO-bo_w_gp-11/04/2025-10:06:17] seed: None [DEBUG-bo_w_gp-11/04/2025-10:06:17] samples: [[-2.625], [-2.597], [-2.723]] [DEBUG-bo_w_gp-11/04/2025-10:06:17] X_init: [[-2.625], [-2.597], [-2.723]] [INFO-bo_w_gp-11/04/2025-10:06:17] Iteration 1 [DEBUG-bo_w_gp-11/04/2025-10:06:17] Responses are normalized. [DEBUG-gp_kernel-11/04/2025-10:06:17] str_optimizer_method: BFGS [DEBUG-gp_kernel-11/04/2025-10:06:17] str_modelselection_method: ml [DEBUG-gp_kernel-11/04/2025-10:06:17] use_gradient: True [DEBUG-gp_kernel-11/04/2025-10:06:17] negative log marginal likelihood: 2.668104 [DEBUG-gp_kernel-11/04/2025-10:06:17] scipy message: Desired error not necessarily achieved due to precision loss. [DEBUG-gp_kernel-11/04/2025-10:06:17] hyps optimized: {'noise': 0.010, 'signal': 15.408, 'lengthscales': [0.405]} [DEBUG-gp_kernel-11/04/2025-10:06:17] time consumed to construct gpr: 0.3679 sec. [DEBUG-bo_w_gp-11/04/2025-10:06:17] samples: [[1.585], [-0.041], [-4.413], [3.491], [3.883], [-2.929], [-2.306], [0.726], [0.226], [-1.731], [-3.658], [4.541], [3.056], [-4.048], [-0.873], [2.492], [2.156], [-1.198], [-4.350], [2.765], [4.873], [-3.334], [-1.433], [0.516], [1.032], [-2.030], [-2.578], [4.203], [3.188], [-4.688], [-0.388], [1.266], [1.425], [-0.542], [-4.847], [3.342], [4.357], [-2.737], [-2.184], [1.191], [0.357], [-1.279], [-3.175], [4.719], [2.611], [-4.191], [-1.044], [1.997], [2.339], [-0.714], [-3.895], [2.897], [4.382], [-3.504], [-1.572], [0.073], [0.879], [-2.465], [-3.083], [4.042], [3.650], [-4.567], [-0.200], [1.739], [1.813], [-0.310], [-4.610], [3.735], [3.969], [-2.969], [-2.420], [0.798], [0.125], [-1.667], [-3.568], [4.483], [2.843], [-3.803], [-0.651], [2.234], [1.946], [-0.951], [-4.126], [2.509], [4.775], [-3.268], [-1.341], [0.460], [1.116], [-2.072], [-2.695], [4.273], [3.414], [-4.960], [-0.588], [1.507], [1.349], [-0.434], [-4.801], [3.260], [4.120], [-2.537], [-1.918], [0.957], [0.619], [-1.495], [-3.426], [4.929], [2.663], [-4.285], [-1.104], [2.104], [2.388], [-0.810], [-3.957], [3.001], [4.641], [-3.722], [-1.826], [0.279], [0.644], [-2.261], [-2.815], [3.810], [3.576], [-4.457], [-0.152], [1.659]] [DEBUG-bo_w_gp-11/04/2025-10:06:17] acquired sample: [1.585] [DEBUG-bo_w_gp-11/04/2025-10:06:17] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:17] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:17] acquired sample: [3.491] [DEBUG-bo_w_gp-11/04/2025-10:06:17] acquired sample: [3.883] [DEBUG-bo_w_gp-11/04/2025-10:06:17] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:17] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [4.541] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [3.056] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [2.492] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [2.156] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [2.765] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [4.873] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [1.032] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [4.203] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [3.188] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [1.266] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [1.425] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [3.342] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [4.357] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-2.737] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:18] acquired sample: [1.191] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [4.719] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [2.611] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [1.997] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [2.339] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [2.897] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [4.382] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [4.042] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [3.650] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [1.739] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [1.813] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [3.735] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [3.969] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:19] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [4.483] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [2.843] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [2.234] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [1.946] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [2.509] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [4.775] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [1.116] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-2.695] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [4.273] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [3.414] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [1.507] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [1.349] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [3.260] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [4.120] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [0.957] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [4.929] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [2.663] [DEBUG-bo_w_gp-11/04/2025-10:06:20] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [2.104] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [2.388] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [3.001] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [4.641] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [-2.815] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [3.810] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [3.576] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [1.659] [DEBUG-bo_w_gp-11/04/2025-10:06:21] overall time consumed to acquire: 4.4658 sec. [DEBUG-bo_w_gp-11/04/2025-10:06:21] next_point: [-2.260] [DEBUG-bo_w_gp-11/04/2025-10:06:21] time consumed to evaluate: 0.0000 sec. [INFO-bo_w_gp-11/04/2025-10:06:21] Iteration 2 [DEBUG-bo_w_gp-11/04/2025-10:06:21] Responses are normalized. [DEBUG-gp_kernel-11/04/2025-10:06:21] str_optimizer_method: BFGS [DEBUG-gp_kernel-11/04/2025-10:06:21] str_modelselection_method: ml [DEBUG-gp_kernel-11/04/2025-10:06:21] use_gradient: True [DEBUG-gp_kernel-11/04/2025-10:06:21] negative log marginal likelihood: 1.289884 [DEBUG-gp_kernel-11/04/2025-10:06:21] scipy message: Desired error not necessarily achieved due to precision loss. [DEBUG-gp_kernel-11/04/2025-10:06:21] hyps optimized: {'noise': 0.010, 'signal': 37.778, 'lengthscales': [3.754]} [DEBUG-gp_kernel-11/04/2025-10:06:21] time consumed to construct gpr: 0.0843 sec. [DEBUG-bo_w_gp-11/04/2025-10:06:21] samples: [[-2.980], [4.586], [0.688], [-1.591], [-0.606], [2.134], [3.217], [-4.198], [-4.401], [2.873], [1.775], [-0.794], [-2.009], [0.403], [4.324], [-3.421], [-3.685], [4.041], [0.139], [-2.292], [-1.155], [1.433], [2.512], [-4.743], [-3.856], [3.578], [2.477], [-0.245], [-1.308], [0.951], [4.869], [-2.716], [-2.560], [4.713], [1.107], [-1.463], [-0.090], [2.321], [3.734], [-4.012], [-4.898], [2.667], [1.279], [-1.001], [-2.446], [0.293], [3.886], [-3.530], [-3.266], [4.169], [0.557], [-2.163], [-0.640], [1.621], [3.028], [-4.556], [-4.354], [3.373], [1.979], [-0.451], [-1.746], [0.843], [4.430], [-2.824], [-2.930], [4.526], [0.903], [-1.797], [-0.361], [1.880], [3.316], [-4.288], [-4.613], [3.094], [1.711], [-0.739], [-2.103], [0.506], [4.064], [-3.170], [-3.473], [3.820], [0.203], [-2.347], [-1.061], [1.330], [2.772], [-4.994], [-3.906], [3.638], [2.261], [-0.039], [-1.553], [1.206], [4.770], [-2.626], [-2.781], [4.924], [1.052], [-1.399], [-0.193], [2.415], [3.483], [-3.752], [-4.838], [2.616], [1.485], [-1.216], [-2.191], [0.048], [3.976], [-3.629], [-3.326], [4.220], [0.351], [-1.948], [-0.895], [1.866], [2.938], [-4.457], [-4.133], [3.161], [2.034], [-0.515], [-1.643], [0.749], [4.681], [-3.084]] [DEBUG-bo_w_gp-11/04/2025-10:06:21] acquired sample: [-2.980] [DEBUG-bo_w_gp-11/04/2025-10:06:22] acquired sample: [0.946] [DEBUG-bo_w_gp-11/04/2025-10:06:22] acquired sample: [0.688] [DEBUG-bo_w_gp-11/04/2025-10:06:22] acquired sample: [1.044] [DEBUG-bo_w_gp-11/04/2025-10:06:22] acquired sample: [1.052] [DEBUG-bo_w_gp-11/04/2025-10:06:22] acquired sample: [1.041] [DEBUG-bo_w_gp-11/04/2025-10:06:22] acquired sample: [0.710] [DEBUG-bo_w_gp-11/04/2025-10:06:23] acquired sample: [-4.199] [DEBUG-bo_w_gp-11/04/2025-10:06:23] acquired sample: [-4.410] [DEBUG-bo_w_gp-11/04/2025-10:06:23] acquired sample: [0.943] [DEBUG-bo_w_gp-11/04/2025-10:06:23] acquired sample: [0.990] [DEBUG-bo_w_gp-11/04/2025-10:06:23] acquired sample: [0.965] [DEBUG-bo_w_gp-11/04/2025-10:06:23] acquired sample: [0.922] [DEBUG-bo_w_gp-11/04/2025-10:06:23] acquired sample: [0.944] [DEBUG-bo_w_gp-11/04/2025-10:06:23] acquired sample: [0.979] [DEBUG-bo_w_gp-11/04/2025-10:06:23] acquired sample: [-3.421] [DEBUG-bo_w_gp-11/04/2025-10:06:23] acquired sample: [-3.685] [DEBUG-bo_w_gp-11/04/2025-10:06:23] acquired sample: [0.918] [DEBUG-bo_w_gp-11/04/2025-10:06:24] acquired sample: [0.893] [DEBUG-bo_w_gp-11/04/2025-10:06:24] acquired sample: [-2.292] [DEBUG-bo_w_gp-11/04/2025-10:06:24] acquired sample: [0.919] [DEBUG-bo_w_gp-11/04/2025-10:06:24] acquired sample: [0.926] [DEBUG-bo_w_gp-11/04/2025-10:06:24] acquired sample: [0.957] [DEBUG-bo_w_gp-11/04/2025-10:06:24] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:24] acquired sample: [-3.856] [DEBUG-bo_w_gp-11/04/2025-10:06:24] acquired sample: [0.916] [DEBUG-bo_w_gp-11/04/2025-10:06:24] acquired sample: [0.942] [DEBUG-bo_w_gp-11/04/2025-10:06:24] acquired sample: [0.944] [DEBUG-bo_w_gp-11/04/2025-10:06:24] acquired sample: [0.951] [DEBUG-bo_w_gp-11/04/2025-10:06:25] acquired sample: [0.949] [DEBUG-bo_w_gp-11/04/2025-10:06:25] acquired sample: [0.910] [DEBUG-bo_w_gp-11/04/2025-10:06:25] acquired sample: [-2.716] [DEBUG-bo_w_gp-11/04/2025-10:06:25] acquired sample: [-2.560] [DEBUG-bo_w_gp-11/04/2025-10:06:25] acquired sample: [0.963] [DEBUG-bo_w_gp-11/04/2025-10:06:25] acquired sample: [0.957] [DEBUG-bo_w_gp-11/04/2025-10:06:25] acquired sample: [0.943] [DEBUG-bo_w_gp-11/04/2025-10:06:25] acquired sample: [0.945] [DEBUG-bo_w_gp-11/04/2025-10:06:26] acquired sample: [0.960] [DEBUG-bo_w_gp-11/04/2025-10:06:26] acquired sample: [0.941] [DEBUG-bo_w_gp-11/04/2025-10:06:26] acquired sample: [-4.012] [DEBUG-bo_w_gp-11/04/2025-10:06:26] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:26] acquired sample: [0.935] [DEBUG-bo_w_gp-11/04/2025-10:06:26] acquired sample: [0.989] [DEBUG-bo_w_gp-11/04/2025-10:06:26] acquired sample: [1.036] [DEBUG-bo_w_gp-11/04/2025-10:06:26] acquired sample: [-2.446] [DEBUG-bo_w_gp-11/04/2025-10:06:26] acquired sample: [1.026] [DEBUG-bo_w_gp-11/04/2025-10:06:26] acquired sample: [1.062] [DEBUG-bo_w_gp-11/04/2025-10:06:26] acquired sample: [-3.530] [DEBUG-bo_w_gp-11/04/2025-10:06:26] acquired sample: [-3.266] [DEBUG-bo_w_gp-11/04/2025-10:06:26] acquired sample: [0.942] [DEBUG-bo_w_gp-11/04/2025-10:06:26] acquired sample: [0.978] [DEBUG-bo_w_gp-11/04/2025-10:06:27] acquired sample: [0.983] [DEBUG-bo_w_gp-11/04/2025-10:06:27] acquired sample: [1.079] [DEBUG-bo_w_gp-11/04/2025-10:06:27] acquired sample: [0.828] [DEBUG-bo_w_gp-11/04/2025-10:06:27] acquired sample: [0.920] [DEBUG-bo_w_gp-11/04/2025-10:06:27] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:27] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:27] acquired sample: [1.105] [DEBUG-bo_w_gp-11/04/2025-10:06:27] acquired sample: [0.964] [DEBUG-bo_w_gp-11/04/2025-10:06:27] acquired sample: [1.071] [DEBUG-bo_w_gp-11/04/2025-10:06:27] acquired sample: [0.975] [DEBUG-bo_w_gp-11/04/2025-10:06:27] acquired sample: [0.935] [DEBUG-bo_w_gp-11/04/2025-10:06:28] acquired sample: [1.204] [DEBUG-bo_w_gp-11/04/2025-10:06:28] acquired sample: [-2.824] [DEBUG-bo_w_gp-11/04/2025-10:06:28] acquired sample: [-2.930] [DEBUG-bo_w_gp-11/04/2025-10:06:28] acquired sample: [1.049] [DEBUG-bo_w_gp-11/04/2025-10:06:28] acquired sample: [0.938] [DEBUG-bo_w_gp-11/04/2025-10:06:28] acquired sample: [0.942] [DEBUG-bo_w_gp-11/04/2025-10:06:28] acquired sample: [1.200] [DEBUG-bo_w_gp-11/04/2025-10:06:28] acquired sample: [0.920] [DEBUG-bo_w_gp-11/04/2025-10:06:28] acquired sample: [0.944] [DEBUG-bo_w_gp-11/04/2025-10:06:28] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:28] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:29] acquired sample: [1.005] [DEBUG-bo_w_gp-11/04/2025-10:06:29] acquired sample: [0.939] [DEBUG-bo_w_gp-11/04/2025-10:06:29] acquired sample: [0.939] [DEBUG-bo_w_gp-11/04/2025-10:06:29] acquired sample: [0.945] [DEBUG-bo_w_gp-11/04/2025-10:06:29] acquired sample: [0.943] [DEBUG-bo_w_gp-11/04/2025-10:06:29] acquired sample: [0.948] [DEBUG-bo_w_gp-11/04/2025-10:06:29] acquired sample: [-3.170] [DEBUG-bo_w_gp-11/04/2025-10:06:29] acquired sample: [-3.473] [DEBUG-bo_w_gp-11/04/2025-10:06:29] acquired sample: [0.804] [DEBUG-bo_w_gp-11/04/2025-10:06:30] acquired sample: [0.879] [DEBUG-bo_w_gp-11/04/2025-10:06:30] acquired sample: [-2.347] [DEBUG-bo_w_gp-11/04/2025-10:06:30] acquired sample: [0.949] [DEBUG-bo_w_gp-11/04/2025-10:06:30] acquired sample: [0.962] [DEBUG-bo_w_gp-11/04/2025-10:06:30] acquired sample: [0.958] [DEBUG-bo_w_gp-11/04/2025-10:06:30] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:30] acquired sample: [-3.906] [DEBUG-bo_w_gp-11/04/2025-10:06:30] acquired sample: [0.937] [DEBUG-bo_w_gp-11/04/2025-10:06:30] acquired sample: [0.943] [DEBUG-bo_w_gp-11/04/2025-10:06:30] acquired sample: [1.066] [DEBUG-bo_w_gp-11/04/2025-10:06:30] acquired sample: [1.011] [DEBUG-bo_w_gp-11/04/2025-10:06:30] acquired sample: [1.080] [DEBUG-bo_w_gp-11/04/2025-10:06:30] acquired sample: [0.942] [DEBUG-bo_w_gp-11/04/2025-10:06:30] acquired sample: [-2.626] [DEBUG-bo_w_gp-11/04/2025-10:06:30] acquired sample: [-2.781] [DEBUG-bo_w_gp-11/04/2025-10:06:31] acquired sample: [0.947] [DEBUG-bo_w_gp-11/04/2025-10:06:31] acquired sample: [0.941] [DEBUG-bo_w_gp-11/04/2025-10:06:31] acquired sample: [0.942] [DEBUG-bo_w_gp-11/04/2025-10:06:31] acquired sample: [0.943] [DEBUG-bo_w_gp-11/04/2025-10:06:31] acquired sample: [1.094] [DEBUG-bo_w_gp-11/04/2025-10:06:31] acquired sample: [0.939] [DEBUG-bo_w_gp-11/04/2025-10:06:31] acquired sample: [-3.752] [DEBUG-bo_w_gp-11/04/2025-10:06:31] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:31] acquired sample: [0.923] [DEBUG-bo_w_gp-11/04/2025-10:06:32] acquired sample: [1.007] [DEBUG-bo_w_gp-11/04/2025-10:06:32] acquired sample: [1.196] [DEBUG-bo_w_gp-11/04/2025-10:06:32] acquired sample: [0.921] [DEBUG-bo_w_gp-11/04/2025-10:06:32] acquired sample: [0.943] [DEBUG-bo_w_gp-11/04/2025-10:06:32] acquired sample: [0.931] [DEBUG-bo_w_gp-11/04/2025-10:06:32] acquired sample: [-3.629] [DEBUG-bo_w_gp-11/04/2025-10:06:32] acquired sample: [-3.326] [DEBUG-bo_w_gp-11/04/2025-10:06:32] acquired sample: [0.943] [DEBUG-bo_w_gp-11/04/2025-10:06:32] acquired sample: [0.927] [DEBUG-bo_w_gp-11/04/2025-10:06:32] acquired sample: [0.935] [DEBUG-bo_w_gp-11/04/2025-10:06:33] acquired sample: [0.923] [DEBUG-bo_w_gp-11/04/2025-10:06:33] acquired sample: [0.772] [DEBUG-bo_w_gp-11/04/2025-10:06:33] acquired sample: [0.935] [DEBUG-bo_w_gp-11/04/2025-10:06:33] acquired sample: [-5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:33] acquired sample: [-4.133] [DEBUG-bo_w_gp-11/04/2025-10:06:33] acquired sample: [0.952] [DEBUG-bo_w_gp-11/04/2025-10:06:33] acquired sample: [0.942] [DEBUG-bo_w_gp-11/04/2025-10:06:33] acquired sample: [1.162] [DEBUG-bo_w_gp-11/04/2025-10:06:33] acquired sample: [0.939] [DEBUG-bo_w_gp-11/04/2025-10:06:33] acquired sample: [0.942] [DEBUG-bo_w_gp-11/04/2025-10:06:33] acquired sample: [0.934] [DEBUG-bo_w_gp-11/04/2025-10:06:33] acquired sample: [-3.084] [DEBUG-bo_w_gp-11/04/2025-10:06:33] overall time consumed to acquire: 12.0208 sec. [DEBUG-bo_w_gp-11/04/2025-10:06:33] next_point: [0.942] [DEBUG-bo_w_gp-11/04/2025-10:06:33] time consumed to evaluate: 0.0000 sec. [INFO-bo_w_gp-11/04/2025-10:06:33] Iteration 3 [DEBUG-bo_w_gp-11/04/2025-10:06:33] Responses are normalized. [DEBUG-gp_kernel-11/04/2025-10:06:33] str_optimizer_method: BFGS [DEBUG-gp_kernel-11/04/2025-10:06:33] str_modelselection_method: ml [DEBUG-gp_kernel-11/04/2025-10:06:33] use_gradient: True [DEBUG-gp_kernel-11/04/2025-10:06:33] negative log marginal likelihood: 0.751849 [DEBUG-gp_kernel-11/04/2025-10:06:33] scipy message: Desired error not necessarily achieved due to precision loss. [DEBUG-gp_kernel-11/04/2025-10:06:33] hyps optimized: {'noise': 0.010, 'signal': 16.541, 'lengthscales': [6.650]} [DEBUG-gp_kernel-11/04/2025-10:06:33] time consumed to construct gpr: 0.0966 sec. [DEBUG-bo_w_gp-11/04/2025-10:06:33] samples: [[-0.230], [3.584], [0.137], [-3.657], [-4.897], [1.386], [4.814], [-1.450], [-2.414], [3.903], [2.330], [-3.966], [-2.713], [1.068], [2.620], [-1.141], [-0.814], [2.929], [0.731], [-2.993], [-4.234], [1.964], [4.141], [-2.037], [-1.756], [4.485], [1.663], [-4.558], [-3.293], [0.407], [3.210], [-0.471], [-0.341], [3.383], [0.580], [-3.163], [-4.376], [1.803], [4.625], [-1.573], [-1.897], [4.324], [2.147], [-4.095], [-2.820], [0.861], [3.059], [-0.642], [-0.997], [2.798], [1.245], [-2.568], [-3.799], [2.465], [4.038], [-2.246], [-1.315], [4.982], [1.554], [-4.762], [-3.480], [0.282], [3.729], [-0.052], [-0.141], [3.661], [0.234], [-3.588], [-4.811], [1.446], [4.894], [-1.383], [-2.332], [3.968], [2.415], [-3.904], [-2.619], [1.140], [2.712], [-1.067], [-0.728], [2.989], [0.811], [-2.925], [-4.145], [2.041], [4.238], [-1.968], [-1.662], [4.557], [1.755], [-4.484], [-3.212], [0.472], [3.294], [-0.408], [-0.576], [3.159], [0.337], [-3.379], [-4.628], [1.577], [4.379], [-1.806], [-2.145], [4.093], [1.896], [-4.323], [-3.060], [0.643], [2.821], [-0.863], [-1.249], [2.572], [1.000], [-2.802], [-4.034], [2.242], [3.795], [-2.462], [-1.555], [4.764], [1.316], [-4.983], [-3.728], [0.051], [3.479], [-0.281]] [DEBUG-bo_w_gp-11/04/2025-10:06:33] acquired sample: [-0.230] [DEBUG-bo_w_gp-11/04/2025-10:06:33] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [0.137] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-3.657] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-4.897] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-1.450] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-2.414] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-3.966] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-2.713] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [1.068] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-1.141] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-0.814] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-2.993] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-4.234] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-2.037] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-1.756] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-4.558] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-3.293] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-0.471] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [0.417] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-3.163] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-4.376] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-1.573] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-1.897] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-4.095] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [-2.820] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [0.213] [DEBUG-bo_w_gp-11/04/2025-10:06:34] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [0.393] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-0.997] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-2.568] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-3.799] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-2.246] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-1.315] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-4.762] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-3.480] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [0.282] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-0.141] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-3.588] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-4.811] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-1.383] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-2.332] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-3.904] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-2.619] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [1.140] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-1.067] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-0.728] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [0.173] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-2.925] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-4.145] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-1.968] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-1.662] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-4.484] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-3.212] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-0.282] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-0.576] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-3.379] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-4.628] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-1.806] [DEBUG-bo_w_gp-11/04/2025-10:06:35] acquired sample: [-2.145] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [-4.323] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [-3.060] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [0.536] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [-0.863] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [-1.249] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [-2.802] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [-4.034] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [-2.462] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [-1.555] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [1.316] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [-4.983] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [-3.728] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [0.051] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:36] overall time consumed to acquire: 2.5455 sec. [DEBUG-bo_w_gp-11/04/2025-10:06:36] next_point: [5.000] [DEBUG-bo_w_gp-11/04/2025-10:06:36] time consumed to evaluate: 0.0000 sec. [INFO-bo_w_gp-11/04/2025-10:06:36] Iteration 4 [DEBUG-bo_w_gp-11/04/2025-10:06:36] Responses are normalized. [DEBUG-gp_kernel-11/04/2025-10:06:36] str_optimizer_method: BFGS [DEBUG-gp_kernel-11/04/2025-10:06:36] str_modelselection_method: ml [DEBUG-gp_kernel-11/04/2025-10:06:36] use_gradient: True [DEBUG-gp_kernel-11/04/2025-10:06:36] negative log marginal likelihood: 1.170193 [DEBUG-gp_kernel-11/04/2025-10:06:36] scipy message: Desired error not necessarily achieved due to precision loss. [DEBUG-gp_kernel-11/04/2025-10:06:36] hyps optimized: {'noise': 0.010, 'signal': 17.463, 'lengthscales': [7.424]} [DEBUG-gp_kernel-11/04/2025-10:06:36] time consumed to construct gpr: 0.0572 sec. [DEBUG-bo_w_gp-11/04/2025-10:06:36] samples: [[0.163], [-4.201], [-1.861], [2.501], [3.937], [-0.447], [-3.097], [1.285], [2.236], [-3.383], [-0.730], [4.886], [3.510], [-2.129], [-4.466], [1.170], [0.831], [-4.802], [-2.452], [3.179], [4.597], [-1.017], [-3.677], [1.934], [1.577], [-2.812], [-0.150], [4.236], [2.841], [-1.528], [-3.875], [0.492], [0.387], [-3.997], [-1.344], [3.038], [4.104], [-0.260], [-2.598], [1.763], [2.070], [-3.569], [-1.229], [4.407], [3.286], [-2.333], [-4.983], [0.633], [0.959], [-4.655], [-1.993], [3.619], [4.704], [-0.930], [-3.277], [2.354], [1.469], [-2.900], [-0.549], [3.817], [2.714], [-1.674], [-4.335], [0.052], [0.137], [-4.224], [-1.564], [2.800], [3.882], [-0.499], [-2.849], [1.535], [2.445], [-3.171], [-0.824], [4.794], [3.689], [-1.947], [-4.609], [1.030], [0.738], [-4.893], [-2.243], [3.390], [4.454], [-1.158], [-3.497], [2.116], [1.873], [-2.513], [-0.175], [4.213], [3.089], [-1.277], [-3.930], [0.439], [0.557], [-3.824], [-1.477], [2.907], [4.322], [-0.039], [-2.702], [1.662], [2.005], [-3.631], [-0.971], [4.667], [3.270], [-2.346], [-4.696], [0.922], [1.216], [-4.395], [-2.057], [3.557], [4.991], [-0.640], [-3.293], [2.340], [1.337], [-3.030], [-0.380], [3.989], [2.610], [-1.776], [-4.116], [0.273]] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [0.406] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [-4.201] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [-1.861] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [2.501] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [3.937] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [0.431] [DEBUG-bo_w_gp-11/04/2025-10:06:36] acquired sample: [-3.097] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [0.433] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [2.236] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [-3.383] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [-0.730] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [4.886] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [2.675] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [-2.129] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [-4.466] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [0.172] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [0.226] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [-4.802] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [-2.452] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [3.017] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [4.597] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [-1.017] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [-3.677] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [0.397] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [1.593] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [-2.812] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [0.620] [DEBUG-bo_w_gp-11/04/2025-10:06:37] acquired sample: [4.236] [DEBUG-bo_w_gp-11/04/2025-10:06:38] acquired sample: [0.451] [DEBUG-bo_w_gp-11/04/2025-10:06:38] acquired sample: [-1.528] [DEBUG-bo_w_gp-11/04/2025-10:06:38] acquired sample: [-3.875] [DEBUG-bo_w_gp-11/04/2025-10:06:38] acquired sample: [0.492] [DEBUG-bo_w_gp-11/04/2025-10:06:38] acquired sample: [0.435] [DEBUG-bo_w_gp-11/04/2025-10:06:38] acquired sample: [-3.997] [DEBUG-bo_w_gp-11/04/2025-10:06:38] acquired sample: [-1.344] [DEBUG-bo_w_gp-11/04/2025-10:06:38] acquired sample: [1.624] [DEBUG-bo_w_gp-11/04/2025-10:06:38] acquired sample: [4.104] [DEBUG-bo_w_gp-11/04/2025-10:06:38] acquired sample: [-0.260] [DEBUG-bo_w_gp-11/04/2025-10:06:38] acquired sample: [-2.598] [DEBUG-bo_w_gp-11/04/2025-10:06:38] acquired sample: [0.400] [DEBUG-bo_w_gp-11/04/2025-10:06:38] acquired sample: [0.374] [DEBUG-bo_w_gp-11/04/2025-10:06:38] acquired sample: [-3.569] [DEBUG-bo_w_gp-11/04/2025-10:06:38] acquired sample: [-1.229] [DEBUG-bo_w_gp-11/04/2025-10:06:38] acquired sample: [4.407] [DEBUG-bo_w_gp-11/04/2025-10:06:39] acquired sample: [0.428] [DEBUG-bo_w_gp-11/04/2025-10:06:39] acquired sample: [-2.333] [DEBUG-bo_w_gp-11/04/2025-10:06:39] acquired sample: [-4.983] [DEBUG-bo_w_gp-11/04/2025-10:06:39] acquired sample: [0.433] [DEBUG-bo_w_gp-11/04/2025-10:06:39] acquired sample: [0.297] [DEBUG-bo_w_gp-11/04/2025-10:06:39] acquired sample: [-4.655] [DEBUG-bo_w_gp-11/04/2025-10:06:39] acquired sample: [-1.993] [DEBUG-bo_w_gp-11/04/2025-10:06:39] acquired sample: [0.434] [DEBUG-bo_w_gp-11/04/2025-10:06:39] acquired sample: [4.704] [DEBUG-bo_w_gp-11/04/2025-10:06:39] acquired sample: [-0.930] [DEBUG-bo_w_gp-11/04/2025-10:06:39] acquired sample: [-3.277] [DEBUG-bo_w_gp-11/04/2025-10:06:39] acquired sample: [0.407] [DEBUG-bo_w_gp-11/04/2025-10:06:39] acquired sample: [1.676] [DEBUG-bo_w_gp-11/04/2025-10:06:39] acquired sample: [-2.900] [DEBUG-bo_w_gp-11/04/2025-10:06:40] acquired sample: [-0.549] [DEBUG-bo_w_gp-11/04/2025-10:06:40] acquired sample: [3.813] [DEBUG-bo_w_gp-11/04/2025-10:06:40] acquired sample: [2.714] [DEBUG-bo_w_gp-11/04/2025-10:06:40] acquired sample: [-1.674] [DEBUG-bo_w_gp-11/04/2025-10:06:40] acquired sample: [-4.335] [DEBUG-bo_w_gp-11/04/2025-10:06:40] acquired sample: [0.215] [DEBUG-bo_w_gp-11/04/2025-10:06:40] acquired sample: [0.137] [DEBUG-bo_w_gp-11/04/2025-10:06:40] acquired sample: [-4.224] [DEBUG-bo_w_gp-11/04/2025-10:06:40] acquired sample: [-1.564] [DEBUG-bo_w_gp-11/04/2025-10:06:40] acquired sample: [0.432] [DEBUG-bo_w_gp-11/04/2025-10:06:40] acquired sample: [3.882] [DEBUG-bo_w_gp-11/04/2025-10:06:40] acquired sample: [0.276] [DEBUG-bo_w_gp-11/04/2025-10:06:40] acquired sample: [-2.849] [DEBUG-bo_w_gp-11/04/2025-10:06:41] acquired sample: [1.596] [DEBUG-bo_w_gp-11/04/2025-10:06:41] acquired sample: [0.444] [DEBUG-bo_w_gp-11/04/2025-10:06:41] acquired sample: [-3.171] [DEBUG-bo_w_gp-11/04/2025-10:06:41] acquired sample: [-0.824] [DEBUG-bo_w_gp-11/04/2025-10:06:41] acquired sample: [4.794] [DEBUG-bo_w_gp-11/04/2025-10:06:41] acquired sample: [1.596] [DEBUG-bo_w_gp-11/04/2025-10:06:41] acquired sample: [-1.947] [DEBUG-bo_w_gp-11/04/2025-10:06:41] acquired sample: [-4.609] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [0.416] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [0.232] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [-4.893] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [-2.243] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [3.367] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [4.454] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [-1.158] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [-3.497] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [0.420] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [1.596] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [-2.513] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [0.272] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [4.213] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [0.583] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [-1.277] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [-3.930] [DEBUG-bo_w_gp-11/04/2025-10:06:42] acquired sample: [0.429] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [0.327] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [-3.824] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [-1.477] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [0.373] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [4.322] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [0.422] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [-2.702] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [1.594] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [1.492] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [-3.631] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [-0.971] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [4.667] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [2.993] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [-2.346] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [-4.696] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [2.088] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [1.594] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [-4.395] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [-2.057] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [2.999] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [4.991] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [0.440] [DEBUG-bo_w_gp-11/04/2025-10:06:43] acquired sample: [-3.293] [DEBUG-bo_w_gp-11/04/2025-10:06:44] acquired sample: [1.867] [DEBUG-bo_w_gp-11/04/2025-10:06:44] acquired sample: [0.355] [DEBUG-bo_w_gp-11/04/2025-10:06:44] acquired sample: [-3.030] [DEBUG-bo_w_gp-11/04/2025-10:06:44] acquired sample: [-0.380] [DEBUG-bo_w_gp-11/04/2025-10:06:44] acquired sample: [3.989] [DEBUG-bo_w_gp-11/04/2025-10:06:44] acquired sample: [0.339] [DEBUG-bo_w_gp-11/04/2025-10:06:44] acquired sample: [-1.776] [DEBUG-bo_w_gp-11/04/2025-10:06:44] acquired sample: [-4.116] [DEBUG-bo_w_gp-11/04/2025-10:06:44] acquired sample: [0.365] [DEBUG-bo_w_gp-11/04/2025-10:06:44] overall time consumed to acquire: 8.6048 sec. [DEBUG-bo_w_gp-11/04/2025-10:06:44] next_point: [0.431] [DEBUG-bo_w_gp-11/04/2025-10:06:44] time consumed to evaluate: 0.0000 sec. [INFO-bo_w_gp-11/04/2025-10:06:44] Iteration 5 [DEBUG-bo_w_gp-11/04/2025-10:06:44] Responses are normalized. [DEBUG-gp_kernel-11/04/2025-10:06:44] str_optimizer_method: BFGS [DEBUG-gp_kernel-11/04/2025-10:06:44] str_modelselection_method: ml [DEBUG-gp_kernel-11/04/2025-10:06:44] use_gradient: True [DEBUG-gp_kernel-11/04/2025-10:06:44] negative log marginal likelihood: 0.971741 [DEBUG-gp_kernel-11/04/2025-10:06:44] scipy message: Desired error not necessarily achieved due to precision loss. [DEBUG-gp_kernel-11/04/2025-10:06:44] hyps optimized: {'noise': 0.010, 'signal': 28.685, 'lengthscales': [11.466]} [DEBUG-gp_kernel-11/04/2025-10:06:44] time consumed to construct gpr: 0.0538 sec. [DEBUG-bo_w_gp-11/04/2025-10:06:44] samples: [[0.498], [-0.650], [-3.394], [2.911], [3.905], [-4.983], [-2.259], [1.473], [2.065], [-1.591], [-4.335], [4.477], [3.591], [-2.795], [-0.070], [1.159], [0.768], [-0.618], [-2.872], [3.357], [4.868], [-3.788], [-1.513], [2.299], [1.707], [-2.181], [-4.436], [4.295], [2.677], [-3.472], [-1.197], [0.107], [0.226], [-1.003], [-3.747], [2.639], [4.179], [-4.633], [-1.909], [1.747], [2.415], [-1.317], [-4.061], [4.827], [3.238], [-3.067], [-0.343], [0.806], [1.040], [-0.265], [-2.520], [3.629], [4.593], [-4.138], [-1.864], [2.024], [1.356], [-2.456], [-4.711], [3.945], [3.030], [-3.200], [-0.925], [0.460], [0.322], [-0.795], [-3.208], [3.066], [4.025], [-4.821], [-2.389], [1.301], [1.881], [-1.729], [-4.141], [4.625], [3.714], [-2.635], [-0.203], [0.990], [0.912], [-0.437], [-3.026], [3.167], [4.702], [-3.907], [-1.338], [2.428], [1.848], [-1.999], [-4.587], [4.103], [2.518], [-3.599], [-1.029], [0.244], [0.047], [-1.145], [-3.558], [2.791], [4.297], [-4.468], [-2.037], [1.573], [2.233], [-1.457], [-3.869], [4.977], [3.363], [-2.910], [-0.478], [0.639], [1.186], [-0.087], [-2.676], [3.441], [4.430], [-4.260], [-1.691], [2.156], [1.495], [-2.271], [-4.859], [3.750], [2.869], [-3.324], [-0.754], [0.594]] [DEBUG-bo_w_gp-11/04/2025-10:06:44] acquired sample: [0.498] [DEBUG-bo_w_gp-11/04/2025-10:06:44] acquired sample: [-0.650] [DEBUG-bo_w_gp-11/04/2025-10:06:44] acquired sample: [-3.394] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [2.911] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [3.905] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-4.983] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-2.259] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [1.386] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [1.391] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-1.591] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-4.335] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [4.477] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [3.591] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-2.795] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-0.070] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [1.159] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [1.075] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-0.618] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-2.872] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [3.357] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [4.868] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-3.788] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-1.513] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [1.617] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [1.118] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-2.181] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-4.436] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [4.295] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [2.677] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-3.472] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-1.197] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [0.107] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [0.226] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-1.003] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-3.747] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [2.639] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [4.179] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-4.633] [DEBUG-bo_w_gp-11/04/2025-10:06:45] acquired sample: [-1.909] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [1.747] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [2.415] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [-1.317] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [-4.061] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [4.827] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [3.238] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [-3.067] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [-0.343] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [0.806] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [1.040] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [-0.265] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [-2.520] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [3.629] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [4.593] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [-4.138] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [-1.864] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [2.024] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [1.356] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [-2.456] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [-4.711] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [3.945] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [3.027] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [-3.200] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [-0.925] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [0.460] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [0.322] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [-0.795] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [-3.208] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [3.055] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [4.025] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [-4.821] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [-2.389] [DEBUG-bo_w_gp-11/04/2025-10:06:46] acquired sample: [1.130] [DEBUG-bo_w_gp-11/04/2025-10:06:47] acquired sample: [1.116] [DEBUG-bo_w_gp-11/04/2025-10:06:47] acquired sample: [-1.729] [DEBUG-bo_w_gp-11/04/2025-10:06:47] acquired sample: [-4.141] [DEBUG-bo_w_gp-11/04/2025-10:06:47] acquired sample: [4.625] [DEBUG-bo_w_gp-11/04/2025-10:06:47] acquired sample: [3.714] [DEBUG-bo_w_gp-11/04/2025-10:06:47] acquired sample: [-2.635] [DEBUG-bo_w_gp-11/04/2025-10:06:47] acquired sample: [-0.203] [DEBUG-bo_w_gp-11/04/2025-10:06:47] acquired sample: [0.990] ------------------------------ Captured log call ------------------------------- INFO bo_w_gp:wrappers_bo_function.py:278 range_X: [[-5.000, 5.000]] INFO bo_w_gp:wrappers_bo_function.py:279 str_cov: matern52 INFO bo_w_gp:wrappers_bo_function.py:280 str_acq: ei INFO bo_w_gp:wrappers_bo_function.py:281 str_optimizer_method_gp: BFGS INFO bo_w_gp:wrappers_bo_function.py:282 str_optimizer_method_bo: L-BFGS-B INFO bo_w_gp:wrappers_bo_function.py:283 str_modelselection_method: ml INFO bo_w_gp:wrappers_bo_function.py:284 num_init: 3 INFO bo_w_gp:wrappers_bo_function.py:285 num_iter: 10 INFO bo_w_gp:wrappers_bo_function.py:286 str_initial_method_bo: sobol INFO bo_w_gp:wrappers_bo_function.py:287 str_sampling_method_ao: abc INFO bo_w_gp:wrappers_bo_function.py:288 num_samples_ao: 128 INFO bo_w_gp:wrappers_bo_function.py:289 str_mlm_method: regular INFO bo_w_gp:wrappers_bo_function.py:290 seed: None DEBUG bo_w_gp:base_bo.py:310 samples: [[1.152], [-4.035], [-0.224]] DEBUG bo_w_gp:wrappers_bo_function.py:296 X_init: [[1.152], [-4.035], [-0.224]] INFO bo_w_gp:wrappers_bo_function.py:85 Iteration 1 INFO bo_w_gp:wrappers_bo_function.py:278 range_X: [[-5.000, 5.000]] INFO bo_w_gp:wrappers_bo_function.py:279 str_cov: matern52 INFO bo_w_gp:wrappers_bo_function.py:280 str_acq: ei INFO bo_w_gp:wrappers_bo_function.py:281 str_optimizer_method_gp: BFGS INFO bo_w_gp:wrappers_bo_function.py:282 str_optimizer_method_bo: L-BFGS-B INFO bo_w_gp:wrappers_bo_function.py:283 str_modelselection_method: ml INFO bo_w_gp:wrappers_bo_function.py:284 num_init: 3 INFO bo_w_gp:wrappers_bo_function.py:285 num_iter: 10 INFO bo_w_gp:wrappers_bo_function.py:286 str_initial_method_bo: uniform INFO bo_w_gp:wrappers_bo_function.py:287 str_sampling_method_ao: sobol INFO bo_w_gp:wrappers_bo_function.py:288 num_samples_ao: 128 INFO bo_w_gp:wrappers_bo_function.py:289 str_mlm_method: regular INFO bo_w_gp:wrappers_bo_function.py:290 seed: None DEBUG bo_w_gp:base_bo.py:310 samples: [[-2.625], [-2.597], [-2.723]] DEBUG bo_w_gp:wrappers_bo_function.py:296 X_init: [[-2.625], [-2.597], [-2.723]] INFO bo_w_gp:wrappers_bo_function.py:85 Iteration 1 DEBUG bo_w_gp:bo_w_gp.py:375 Responses are normalized. DEBUG gp_kernel:gp_kernel.py:79 str_optimizer_method: BFGS DEBUG gp_kernel:gp_kernel.py:80 str_modelselection_method: ml DEBUG gp_kernel:gp_kernel.py:81 use_gradient: True DEBUG gp_kernel:gp_kernel.py:114 negative log marginal likelihood: 2.668104 DEBUG gp_kernel:gp_kernel.py:115 scipy message: Desired error not necessarily achieved due to precision loss. DEBUG gp_kernel:gp_kernel.py:154 hyps optimized: {'noise': 0.010, 'signal': 15.408, 'lengthscales': [0.405]} DEBUG gp_kernel:gp_kernel.py:155 time consumed to construct gpr: 0.3679 sec. DEBUG bo_w_gp:base_bo.py:310 samples: [[1.585], [-0.041], [-4.413], [3.491], [3.883], [-2.929], [-2.306], [0.726], [0.226], [-1.731], [-3.658], [4.541], [3.056], [-4.048], [-0.873], [2.492], [2.156], [-1.198], [-4.350], [2.765], [4.873], [-3.334], [-1.433], [0.516], [1.032], [-2.030], [-2.578], [4.203], [3.188], [-4.688], [-0.388], [1.266], [1.425], [-0.542], [-4.847], [3.342], [4.357], [-2.737], [-2.184], [1.191], [0.357], [-1.279], [-3.175], [4.719], [2.611], [-4.191], [-1.044], [1.997], [2.339], [-0.714], [-3.895], [2.897], [4.382], [-3.504], [-1.572], [0.073], [0.879], [-2.465], [-3.083], [4.042], [3.650], [-4.567], [-0.200], [1.739], [1.813], [-0.310], [-4.610], [3.735], [3.969], [-2.969], [-2.420], [0.798], [0.125], [-1.667], [-3.568], [4.483], [2.843], [-3.803], [-0.651], [2.234], [1.946], [-0.951], [-4.126], [2.509], [4.775], [-3.268], [-1.341], [0.460], [1.116], [-2.072], [-2.695], [4.273], [3.414], [-4.960], [-0.588], [1.507], [1.349], [-0.434], [-4.801], [3.260], [4.120], [-2.537], [-1.918], [0.957], [0.619], [-1.495], [-3.426], [4.929], [2.663], [-4.285], [-1.104], [2.104], [2.388], [-0.810], [-3.957], [3.001], [4.641], [-3.722], [-1.826], [0.279], [0.644], [-2.261], [-2.815], [3.810], [3.576], [-4.457], [-0.152], [1.659]] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.585] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.491] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.883] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.541] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.056] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.492] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.156] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.765] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.873] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.032] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.203] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.188] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.266] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.425] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.342] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.357] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.737] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.191] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.719] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.611] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.997] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.339] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.897] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.382] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.042] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.650] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.739] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.813] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.735] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.969] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.483] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.843] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.234] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.946] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.509] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.775] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.116] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.695] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.273] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.414] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.507] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.349] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.120] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.957] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.929] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.663] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.104] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.388] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.001] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.641] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.815] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.810] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.576] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.659] DEBUG bo_w_gp:bo_w_gp.py:488 overall time consumed to acquire: 4.4658 sec. DEBUG bo_w_gp:wrappers_bo_function.py:97 next_point: [-2.260] DEBUG bo_w_gp:wrappers_bo_function.py:113 time consumed to evaluate: 0.0000 sec. INFO bo_w_gp:wrappers_bo_function.py:85 Iteration 2 DEBUG bo_w_gp:bo_w_gp.py:375 Responses are normalized. DEBUG gp_kernel:gp_kernel.py:79 str_optimizer_method: BFGS DEBUG gp_kernel:gp_kernel.py:80 str_modelselection_method: ml DEBUG gp_kernel:gp_kernel.py:81 use_gradient: True DEBUG gp_kernel:gp_kernel.py:114 negative log marginal likelihood: 1.289884 DEBUG gp_kernel:gp_kernel.py:115 scipy message: Desired error not necessarily achieved due to precision loss. DEBUG gp_kernel:gp_kernel.py:154 hyps optimized: {'noise': 0.010, 'signal': 37.778, 'lengthscales': [3.754]} DEBUG gp_kernel:gp_kernel.py:155 time consumed to construct gpr: 0.0843 sec. DEBUG bo_w_gp:base_bo.py:310 samples: [[-2.980], [4.586], [0.688], [-1.591], [-0.606], [2.134], [3.217], [-4.198], [-4.401], [2.873], [1.775], [-0.794], [-2.009], [0.403], [4.324], [-3.421], [-3.685], [4.041], [0.139], [-2.292], [-1.155], [1.433], [2.512], [-4.743], [-3.856], [3.578], [2.477], [-0.245], [-1.308], [0.951], [4.869], [-2.716], [-2.560], [4.713], [1.107], [-1.463], [-0.090], [2.321], [3.734], [-4.012], [-4.898], [2.667], [1.279], [-1.001], [-2.446], [0.293], [3.886], [-3.530], [-3.266], [4.169], [0.557], [-2.163], [-0.640], [1.621], [3.028], [-4.556], [-4.354], [3.373], [1.979], [-0.451], [-1.746], [0.843], [4.430], [-2.824], [-2.930], [4.526], [0.903], [-1.797], [-0.361], [1.880], [3.316], [-4.288], [-4.613], [3.094], [1.711], [-0.739], [-2.103], [0.506], [4.064], [-3.170], [-3.473], [3.820], [0.203], [-2.347], [-1.061], [1.330], [2.772], [-4.994], [-3.906], [3.638], [2.261], [-0.039], [-1.553], [1.206], [4.770], [-2.626], [-2.781], [4.924], [1.052], [-1.399], [-0.193], [2.415], [3.483], [-3.752], [-4.838], [2.616], [1.485], [-1.216], [-2.191], [0.048], [3.976], [-3.629], [-3.326], [4.220], [0.351], [-1.948], [-0.895], [1.866], [2.938], [-4.457], [-4.133], [3.161], [2.034], [-0.515], [-1.643], [0.749], [4.681], [-3.084]] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.980] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.946] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.688] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.044] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.052] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.041] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.710] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.199] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.410] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.943] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.990] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.965] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.922] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.944] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.979] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.421] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.685] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.918] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.893] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.292] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.919] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.926] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.957] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.856] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.916] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.942] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.944] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.951] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.949] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.910] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.716] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.560] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.963] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.957] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.943] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.945] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.960] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.941] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.012] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.935] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.989] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.036] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.446] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.026] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.062] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.530] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.266] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.942] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.978] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.983] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.079] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.828] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.920] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.105] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.964] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.071] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.975] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.935] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.204] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.824] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.930] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.049] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.938] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.942] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.200] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.920] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.944] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.005] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.939] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.939] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.945] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.943] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.948] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.170] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.473] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.804] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.879] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.347] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.949] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.962] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.958] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.906] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.937] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.943] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.066] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.011] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.080] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.942] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.626] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.781] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.947] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.941] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.942] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.943] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.094] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.939] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.752] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.923] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.007] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.196] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.921] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.943] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.931] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.629] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.326] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.943] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.927] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.935] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.923] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.772] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.935] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.133] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.952] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.942] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.162] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.939] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.942] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.934] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.084] DEBUG bo_w_gp:bo_w_gp.py:488 overall time consumed to acquire: 12.0208 sec. DEBUG bo_w_gp:wrappers_bo_function.py:97 next_point: [0.942] DEBUG bo_w_gp:wrappers_bo_function.py:113 time consumed to evaluate: 0.0000 sec. INFO bo_w_gp:wrappers_bo_function.py:85 Iteration 3 DEBUG bo_w_gp:bo_w_gp.py:375 Responses are normalized. DEBUG gp_kernel:gp_kernel.py:79 str_optimizer_method: BFGS DEBUG gp_kernel:gp_kernel.py:80 str_modelselection_method: ml DEBUG gp_kernel:gp_kernel.py:81 use_gradient: True DEBUG gp_kernel:gp_kernel.py:114 negative log marginal likelihood: 0.751849 DEBUG gp_kernel:gp_kernel.py:115 scipy message: Desired error not necessarily achieved due to precision loss. DEBUG gp_kernel:gp_kernel.py:154 hyps optimized: {'noise': 0.010, 'signal': 16.541, 'lengthscales': [6.650]} DEBUG gp_kernel:gp_kernel.py:155 time consumed to construct gpr: 0.0966 sec. DEBUG bo_w_gp:base_bo.py:310 samples: [[-0.230], [3.584], [0.137], [-3.657], [-4.897], [1.386], [4.814], [-1.450], [-2.414], [3.903], [2.330], [-3.966], [-2.713], [1.068], [2.620], [-1.141], [-0.814], [2.929], [0.731], [-2.993], [-4.234], [1.964], [4.141], [-2.037], [-1.756], [4.485], [1.663], [-4.558], [-3.293], [0.407], [3.210], [-0.471], [-0.341], [3.383], [0.580], [-3.163], [-4.376], [1.803], [4.625], [-1.573], [-1.897], [4.324], [2.147], [-4.095], [-2.820], [0.861], [3.059], [-0.642], [-0.997], [2.798], [1.245], [-2.568], [-3.799], [2.465], [4.038], [-2.246], [-1.315], [4.982], [1.554], [-4.762], [-3.480], [0.282], [3.729], [-0.052], [-0.141], [3.661], [0.234], [-3.588], [-4.811], [1.446], [4.894], [-1.383], [-2.332], [3.968], [2.415], [-3.904], [-2.619], [1.140], [2.712], [-1.067], [-0.728], [2.989], [0.811], [-2.925], [-4.145], [2.041], [4.238], [-1.968], [-1.662], [4.557], [1.755], [-4.484], [-3.212], [0.472], [3.294], [-0.408], [-0.576], [3.159], [0.337], [-3.379], [-4.628], [1.577], [4.379], [-1.806], [-2.145], [4.093], [1.896], [-4.323], [-3.060], [0.643], [2.821], [-0.863], [-1.249], [2.572], [1.000], [-2.802], [-4.034], [2.242], [3.795], [-2.462], [-1.555], [4.764], [1.316], [-4.983], [-3.728], [0.051], [3.479], [-0.281]] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.230] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.137] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.657] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.897] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.450] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.414] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.966] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.713] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.068] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.141] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.814] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.993] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.234] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.037] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.756] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.558] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.293] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.471] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.417] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.163] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.376] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.573] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.897] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.095] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.820] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.213] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.393] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.997] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.568] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.799] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.246] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.315] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.762] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.480] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.282] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.141] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.588] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.811] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.383] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.332] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.904] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.619] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.140] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.067] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.728] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.173] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.925] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.145] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.968] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.662] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.484] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.212] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.282] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.576] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.379] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.628] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.806] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.145] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.323] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.060] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.536] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.863] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.249] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.802] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.034] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.462] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.555] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.316] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.983] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.728] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.051] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [5.000] DEBUG bo_w_gp:bo_w_gp.py:488 overall time consumed to acquire: 2.5455 sec. DEBUG bo_w_gp:wrappers_bo_function.py:97 next_point: [5.000] DEBUG bo_w_gp:wrappers_bo_function.py:113 time consumed to evaluate: 0.0000 sec. INFO bo_w_gp:wrappers_bo_function.py:85 Iteration 4 DEBUG bo_w_gp:bo_w_gp.py:375 Responses are normalized. DEBUG gp_kernel:gp_kernel.py:79 str_optimizer_method: BFGS DEBUG gp_kernel:gp_kernel.py:80 str_modelselection_method: ml DEBUG gp_kernel:gp_kernel.py:81 use_gradient: True DEBUG gp_kernel:gp_kernel.py:114 negative log marginal likelihood: 1.170193 DEBUG gp_kernel:gp_kernel.py:115 scipy message: Desired error not necessarily achieved due to precision loss. DEBUG gp_kernel:gp_kernel.py:154 hyps optimized: {'noise': 0.010, 'signal': 17.463, 'lengthscales': [7.424]} DEBUG gp_kernel:gp_kernel.py:155 time consumed to construct gpr: 0.0572 sec. DEBUG bo_w_gp:base_bo.py:310 samples: [[0.163], [-4.201], [-1.861], [2.501], [3.937], [-0.447], [-3.097], [1.285], [2.236], [-3.383], [-0.730], [4.886], [3.510], [-2.129], [-4.466], [1.170], [0.831], [-4.802], [-2.452], [3.179], [4.597], [-1.017], [-3.677], [1.934], [1.577], [-2.812], [-0.150], [4.236], [2.841], [-1.528], [-3.875], [0.492], [0.387], [-3.997], [-1.344], [3.038], [4.104], [-0.260], [-2.598], [1.763], [2.070], [-3.569], [-1.229], [4.407], [3.286], [-2.333], [-4.983], [0.633], [0.959], [-4.655], [-1.993], [3.619], [4.704], [-0.930], [-3.277], [2.354], [1.469], [-2.900], [-0.549], [3.817], [2.714], [-1.674], [-4.335], [0.052], [0.137], [-4.224], [-1.564], [2.800], [3.882], [-0.499], [-2.849], [1.535], [2.445], [-3.171], [-0.824], [4.794], [3.689], [-1.947], [-4.609], [1.030], [0.738], [-4.893], [-2.243], [3.390], [4.454], [-1.158], [-3.497], [2.116], [1.873], [-2.513], [-0.175], [4.213], [3.089], [-1.277], [-3.930], [0.439], [0.557], [-3.824], [-1.477], [2.907], [4.322], [-0.039], [-2.702], [1.662], [2.005], [-3.631], [-0.971], [4.667], [3.270], [-2.346], [-4.696], [0.922], [1.216], [-4.395], [-2.057], [3.557], [4.991], [-0.640], [-3.293], [2.340], [1.337], [-3.030], [-0.380], [3.989], [2.610], [-1.776], [-4.116], [0.273]] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.406] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.201] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.861] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.501] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.937] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.431] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.097] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.433] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.236] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.383] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.730] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.886] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.675] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.129] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.466] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.172] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.226] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.802] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.452] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.017] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.597] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.017] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.677] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.397] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.593] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.812] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.620] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.236] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.451] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.528] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.875] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.492] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.435] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.997] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.344] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.624] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.104] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.260] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.598] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.400] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.374] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.569] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.229] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.407] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.428] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.333] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.983] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.433] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.297] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.655] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.993] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.434] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.704] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.930] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.277] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.407] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.676] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.900] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.549] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.813] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.714] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.674] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.335] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.215] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.137] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.224] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.564] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.432] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.882] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.276] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.849] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.596] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.444] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.171] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.824] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.794] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.596] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.947] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.609] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.416] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.232] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.893] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.243] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.367] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.454] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.158] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.497] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.420] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.596] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.513] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.272] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.213] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.583] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.277] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.930] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.429] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.327] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.824] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.477] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.373] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.322] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.422] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.702] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.594] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.492] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.631] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.971] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.667] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.993] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.346] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.696] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.088] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.594] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.395] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.057] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.999] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.991] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.440] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.293] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.867] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.355] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.030] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.380] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.989] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.339] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.776] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.116] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.365] DEBUG bo_w_gp:bo_w_gp.py:488 overall time consumed to acquire: 8.6048 sec. DEBUG bo_w_gp:wrappers_bo_function.py:97 next_point: [0.431] DEBUG bo_w_gp:wrappers_bo_function.py:113 time consumed to evaluate: 0.0000 sec. INFO bo_w_gp:wrappers_bo_function.py:85 Iteration 5 DEBUG bo_w_gp:bo_w_gp.py:375 Responses are normalized. DEBUG gp_kernel:gp_kernel.py:79 str_optimizer_method: BFGS DEBUG gp_kernel:gp_kernel.py:80 str_modelselection_method: ml DEBUG gp_kernel:gp_kernel.py:81 use_gradient: True DEBUG gp_kernel:gp_kernel.py:114 negative log marginal likelihood: 0.971741 DEBUG gp_kernel:gp_kernel.py:115 scipy message: Desired error not necessarily achieved due to precision loss. DEBUG gp_kernel:gp_kernel.py:154 hyps optimized: {'noise': 0.010, 'signal': 28.685, 'lengthscales': [11.466]} DEBUG gp_kernel:gp_kernel.py:155 time consumed to construct gpr: 0.0538 sec. DEBUG bo_w_gp:base_bo.py:310 samples: [[0.498], [-0.650], [-3.394], [2.911], [3.905], [-4.983], [-2.259], [1.473], [2.065], [-1.591], [-4.335], [4.477], [3.591], [-2.795], [-0.070], [1.159], [0.768], [-0.618], [-2.872], [3.357], [4.868], [-3.788], [-1.513], [2.299], [1.707], [-2.181], [-4.436], [4.295], [2.677], [-3.472], [-1.197], [0.107], [0.226], [-1.003], [-3.747], [2.639], [4.179], [-4.633], [-1.909], [1.747], [2.415], [-1.317], [-4.061], [4.827], [3.238], [-3.067], [-0.343], [0.806], [1.040], [-0.265], [-2.520], [3.629], [4.593], [-4.138], [-1.864], [2.024], [1.356], [-2.456], [-4.711], [3.945], [3.030], [-3.200], [-0.925], [0.460], [0.322], [-0.795], [-3.208], [3.066], [4.025], [-4.821], [-2.389], [1.301], [1.881], [-1.729], [-4.141], [4.625], [3.714], [-2.635], [-0.203], [0.990], [0.912], [-0.437], [-3.026], [3.167], [4.702], [-3.907], [-1.338], [2.428], [1.848], [-1.999], [-4.587], [4.103], [2.518], [-3.599], [-1.029], [0.244], [0.047], [-1.145], [-3.558], [2.791], [4.297], [-4.468], [-2.037], [1.573], [2.233], [-1.457], [-3.869], [4.977], [3.363], [-2.910], [-0.478], [0.639], [1.186], [-0.087], [-2.676], [3.441], [4.430], [-4.260], [-1.691], [2.156], [1.495], [-2.271], [-4.859], [3.750], [2.869], [-3.324], [-0.754], [0.594]] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.498] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.650] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.394] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.911] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.905] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.983] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.259] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.386] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.391] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.591] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.335] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.477] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.591] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.795] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.070] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.159] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.075] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.618] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.872] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.357] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.868] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.788] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.513] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.617] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.118] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.181] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.436] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.295] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.677] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.472] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.197] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.107] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.226] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.003] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.747] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.639] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.179] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.633] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.909] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.747] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.415] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.317] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.061] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.827] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.238] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.067] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.343] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.806] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.040] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.265] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.520] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.629] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.593] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.138] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.864] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [2.024] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.356] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.456] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.711] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.945] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.027] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.200] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.925] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.460] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.322] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.795] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-3.208] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.055] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.025] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.821] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.389] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.130] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [1.116] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-1.729] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-4.141] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [4.625] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [3.714] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-2.635] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [-0.203] DEBUG bo_w_gp:bo_w_gp.py:156 acquired sample: [0.990] =============================== warnings summary =============================== tests/common/test_bo_bo_w_tp.py: 4 warnings tests/common/test_bo_bo_w_trees.py: 4 warnings tests/common/test_bo_bo_w_gp.py: 4 warnings tests/common/test_wrappers_bo_class.py: 3 warnings tests/common/test_wrappers_bo_function.py: 1 warning /home/buildozer/aports/community/py3-bayeso/src/bayeso-0.6.0/bayeso/bo/base_bo.py:233: UserWarning: The balance properties of Sobol' points require n to be a power of 2. samples = sampler.random(num_samples) tests/common/test_version.py::test_version_setup /home/buildozer/aports/community/py3-bayeso/src/bayeso-0.6.0/tests/common/test_version.py:15: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources tests/common/test_trees_trees_common.py: 32 warnings /usr/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=14521) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() tests/common/test_wrappers_bo_class.py: 2688 warnings tests/common/test_bo_bo_w_gp.py: 1607 warnings tests/common/test_wrappers_bo_function.py: 3153 warnings /home/buildozer/aports/community/py3-bayeso/src/bayeso-0.6.0/bayeso/bo/bo_w_gp.py:146: DeprecationWarning: scipy.optimize: The `disp` and `iprint` options of the L-BFGS-B solver are deprecated and will be removed in SciPy 1.18.0. next_point = minimize( tests/common/test_bo_bo_w_trees.py: 32 warnings /usr/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=14840) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() tests/common/test_gp_kernel.py::test_get_optimized_kernel tests/common/test_gp_kernel.py::test_get_optimized_kernel /home/buildozer/aports/community/py3-bayeso/src/bayeso-0.6.0/bayeso/gp/gp_kernel.py:125: DeprecationWarning: scipy.optimize: The `disp` and `iprint` options of the L-BFGS-B solver are deprecated and will be removed in SciPy 1.18.0. result_optimized = scipy.optimize.minimize(neg_log_ml_, hyps_converted, tests/common/test_tp_kernel.py::test_get_optimized_kernel tests/common/test_tp_kernel.py::test_get_optimized_kernel tests/common/test_tp_kernel.py::test_get_optimized_kernel /home/buildozer/aports/community/py3-bayeso/src/bayeso-0.6.0/bayeso/tp/tp_kernel.py:99: DeprecationWarning: scipy.optimize: The `disp` and `iprint` options of the L-BFGS-B solver are deprecated and will be removed in SciPy 1.18.0. result_optimized = scipy.optimize.minimize(neg_log_ml_, hyps_converted, tests/common/test_bo_bo_w_tp.py: 899 warnings tests/common/test_wrappers_bo_class.py: 640 warnings /home/buildozer/aports/community/py3-bayeso/src/bayeso-0.6.0/bayeso/bo/bo_w_tp.py:137: DeprecationWarning: scipy.optimize: The `disp` and `iprint` options of the L-BFGS-B solver are deprecated and will be removed in SciPy 1.18.0. next_point = minimize( tests/common/test_wrappers_bo_class.py: 160 warnings /usr/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=14876) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html =========================== short test summary info ============================ FAILED tests/common/test_bo_bo_w_tp.py::test_optimize_str_acq - Failed: Timeo... FAILED tests/common/test_bo_bo_w_gp.py::test_optimize_str_acq - Failed: Timeo... FAILED tests/common/test_bo_bo_w_tp.py::test_optimize_use_ard - Failed: Timeo... FAILED tests/common/test_bo_bo_w_gp.py::test_optimize_use_ard - Failed: Timeo... FAILED tests/common/test_wrappers_bo_function.py::test_run_single_round - Fai... ================ 5 failed, 247 passed, 9233 warnings in 56.59s ================= >>> ERROR: py3-bayeso: check failed >>> py3-bayeso: Uninstalling dependencies... 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