Your message dated Sun, 19 Oct 2025 02:34:05 +0000
with message-id <[email protected]>
and subject line Bug#1117992: fixed in scikit-optimize 0.10.2-5
has caused the Debian Bug report #1117992,
regarding scikit-optimize: autopkgtest regression with scikit-learn 1.7.2
to be marked as done.
This means that you claim that the problem has been dealt with.
If this is not the case it is now your responsibility to reopen the
Bug report if necessary, and/or fix the problem forthwith.
(NB: If you are a system administrator and have no idea what this
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--
1117992: https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=1117992
Debian Bug Tracking System
Contact [email protected] with problems
--- Begin Message ---
Source: scikit-optimize
Version: 0.10.2-4
Severity: important
User: [email protected]
Usertags: scikit-learn-1.7
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Hash: SHA512
Dear maintainer,
your package has an autopkgtest regression with scikit-learn 1.7.2.
Relevant excerpt from
https://ci.debian.net/packages/s/scikit-optimize/unstable/amd64/65190549/
follows:
483s =================================== FAILURES
===================================
483s _______________ test_minimizer_api[minimizer7-call_single-True]
________________
483s
483s verbose = True, call = <function call_single at 0x7f8fed034220>
483s minimizer = functools.partial(<function gbrt_minimize at 0x7f8fecff1580>,
acq_func='LCB')
483s
483s @pytest.mark.slow_test
483s @pytest.mark.parametrize("verbose", [True, False])
483s @pytest.mark.parametrize("call", [call_single, [call_single,
check_result_callable]])
483s @pytest.mark.parametrize("minimizer", MINIMIZERS)
483s def test_minimizer_api(verbose, call, minimizer):
483s n_calls = 7
483s n_initial_points = 3
483s n_models = n_calls - n_initial_points + 1
483s
483s > result = minimizer(
483s branin,
483s [(-5.0, 10.0), (0.0, 15.0)],
483s n_initial_points=n_initial_points,
483s n_calls=n_calls,
483s random_state=1,
483s verbose=verbose,
483s callback=call,
483s )
483s
483s tests/test_common.py:112:
483s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _
483s /usr/lib/python3/dist-packages/skopt/optimizer/gbrt.py:197: in
gbrt_minimize
483s return base_minimize(
483s /usr/lib/python3/dist-packages/skopt/optimizer/base.py:276: in
base_minimize
483s optimizer = Optimizer(
483s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _
483s
483s self = <skopt.optimizer.optimizer.Optimizer object at 0x7f8feb1c2d50>
483s dimensions = [(-5.0, 10.0), (0.0, 15.0)]
483s base_estimator =
GradientBoostingQuantileRegressor(base_estimator=GradientBoostingRegressor(loss='quantile',
483s ... quantiles=[0.16, 0.5, 0.84],
483s random_state=RandomState(MT19937) at
0x7F8FF0D98440)
483s n_random_starts = None, n_initial_points = 3, initial_point_generator =
'random'
483s n_jobs = 1, acq_func = 'LCB', acq_optimizer = 'sampling', random_state = 1
483s model_queue_size = None, space_constraint = None
483s acq_func_kwargs = {'kappa': 1.96, 'xi': 0.01}
483s acq_optimizer_kwargs = {'n_jobs': 1, 'n_points': 10000,
'n_restarts_optimizer': 5}
483s avoid_duplicates = True
483s
483s def __init__(
483s self,
483s dimensions,
483s base_estimator="gp",
483s n_random_starts=None,
483s n_initial_points=10,
483s initial_point_generator="random",
483s n_jobs=1,
483s acq_func="gp_hedge",
483s acq_optimizer="auto",
483s random_state=None,
483s model_queue_size=None,
483s space_constraint=None,
483s acq_func_kwargs=None,
483s acq_optimizer_kwargs=None,
483s avoid_duplicates=True,
483s ):
483s args = locals().copy()
483s del args['self']
483s self.specs = {"args": args, "function": "Optimizer"}
483s self.rng = check_random_state(random_state)
483s
483s # Configure acquisition function
483s
483s # Store and creat acquisition function set
483s self.acq_func = acq_func
483s self.acq_func_kwargs = acq_func_kwargs
483s self.avoid_duplicates = avoid_duplicates
483s
483s allowed_acq_funcs = [
483s "gp_hedge",
483s "EI",
483s "LCB",
483s "MES",
483s "PVRS",
483s "PI",
483s "EIps",
483s "PIps",
483s ]
483s if self.acq_func not in allowed_acq_funcs:
483s raise ValueError(
483s "expected acq_func to be in %s, got %s"
483s % (",".join(allowed_acq_funcs), self.acq_func)
483s )
483s
483s # treat hedging method separately
483s if self.acq_func == "gp_hedge":
483s self.cand_acq_funcs_ = ["EI", "LCB", "PI"]
483s self.gains_ = np.zeros(3)
483s else:
483s self.cand_acq_funcs_ = [self.acq_func]
483s
483s if acq_func_kwargs is None:
483s acq_func_kwargs = dict()
483s self.eta = acq_func_kwargs.get("eta", 1.0)
483s
483s # Configure counters of points
483s
483s # Check `n_random_starts` deprecation first
483s if n_random_starts is not None:
483s warnings.warn(
483s ("n_random_starts will be removed in favour of "
"n_initial_points."),
483s DeprecationWarning,
483s )
483s n_initial_points = n_random_starts
483s
483s if n_initial_points < 0:
483s raise ValueError(
483s "Expected `n_initial_points` >= 0, got %d" %
n_initial_points
483s )
483s self._n_initial_points = n_initial_points
483s self.n_initial_points_ = n_initial_points
483s
483s # Configure estimator
483s
483s # build base_estimator if doesn't exist
483s if isinstance(base_estimator, str):
483s base_estimator = cook_estimator(
483s base_estimator,
483s space=dimensions,
483s random_state=self.rng.randint(0, np.iinfo(np.int32).max),
483s n_jobs=n_jobs,
483s )
483s 483s # check if regressor
483s # check if regressor
483s if not is_regressor(base_estimator) and base_estimator is not None:
483s > raise ValueError("%s has to be a regressor." % base_estimator)
483s E ValueError:
GradientBoostingQuantileRegressor(base_estimator=GradientBoostingRegressor(loss='quantile',
483s E
n_estimators=30),
483s E quantiles=[0.16, 0.5, 0.84],
483s E
random_state=RandomState(MT19937) at 0x7F8FF0D98440) has to be a regressor.
483s
483s /usr/lib/python3/dist-packages/skopt/optimizer/optimizer.py:257: ValueError
Cheers
Timo
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--- End Message ---
--- Begin Message ---
Source: scikit-optimize
Source-Version: 0.10.2-5
Done: Colin Watson <[email protected]>
We believe that the bug you reported is fixed in the latest version of
scikit-optimize, which is due to be installed in the Debian FTP archive.
A summary of the changes between this version and the previous one is
attached.
Thank you for reporting the bug, which will now be closed. If you
have further comments please address them to [email protected],
and the maintainer will reopen the bug report if appropriate.
Debian distribution maintenance software
pp.
Colin Watson <[email protected]> (supplier of updated scikit-optimize package)
(This message was generated automatically at their request; if you
believe that there is a problem with it please contact the archive
administrators by mailing [email protected])
-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA512
Format: 1.8
Date: Sun, 19 Oct 2025 03:12:24 +0100
Source: scikit-optimize
Architecture: source
Version: 0.10.2-5
Distribution: unstable
Urgency: medium
Maintainer: Debian Python Team <[email protected]>
Changed-By: Colin Watson <[email protected]>
Closes: 1117992
Changes:
scikit-optimize (0.10.2-5) unstable; urgency=medium
.
* Team upload.
* Fix mixin order for scikit-learn >= 1.6 (closes: #1117992).
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Files:
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Git-Tag-Info: tag=de35958886a5ae506bfeae7f898ec98ace52db49
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Git-Tag-Tagger: Colin Watson <[email protected]>
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