Package: src:mdp Version: 3.6-10 Severity: serious Tags: ftbfs forky sid Dear maintainer:
During a rebuild of all packages in unstable, this package failed to build. Below you will find the last part of the build log (probably the most relevant part, but not necessarily). If required, the full build log is available here: https://people.debian.org/~sanvila/build-logs/202510/ About the archive rebuild: The build was made on virtual machines from AWS, using sbuild and a reduced chroot with only build-essential packages. If you cannot reproduce the bug please contact me privately, as I am willing to provide ssh access to a virtual machine where the bug is fully reproducible. If this is really a bug in one of the build-depends, please use reassign and add an affects on src:mdp, so that this is still visible in the BTS web page for this package. Thanks. -------------------------------------------------------------------------------- [...] debian/rules clean dh clean --buildsystem=pybuild dh_auto_clean -O--buildsystem=pybuild I: pybuild base:311: python3.13 setup.py clean /<<PKGBUILDDIR>>/setup.py:2: SetuptoolsDeprecationWarning: The test command is disabled and references to it are deprecated. !! ******************************************************************************** Please remove any references to `setuptools.command.test` in all supported versions of the affected package. This deprecation is overdue, please update your project and remove deprecated calls to avoid build errors in the future. ******************************************************************************** !! [... snipped ...] ??? mdp/signal_node.py:631: in stop_training self._train_seq[self._train_phase][1](*args, **kwargs) mdp/nodes/scikits_nodes.py:252: in _stop_training return self.scikits_alg.fit(self.data, self.labels, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3/dist-packages/sklearn/base.py:1365: in wrapper return fit_method(estimator, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3/dist-packages/sklearn/semi_supervised/_self_training.py:265: in fit self.estimator_ = self._get_estimator() ^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = SelfTrainingClassifier() def _get_estimator(self): """Get the estimator. Returns ------- estimator_ : estimator object The cloned estimator object. """ # TODO(1.8): remove and only keep clone(self.estimator) if self.estimator is None and self.base_estimator != "deprecated": estimator_ = clone(self.base_estimator) warn( ( "`base_estimator` has been deprecated in 1.6 and will be removed" " in 1.8. Please use `estimator` instead." ), FutureWarning, ) # TODO(1.8) remove elif self.estimator is None and self.base_estimator == "deprecated": > raise ValueError( "You must pass an estimator to SelfTrainingClassifier. Use `estimator`." E ValueError: You must pass an estimator to SelfTrainingClassifier. Use `estimator`. /usr/lib/python3/dist-packages/sklearn/semi_supervised/_self_training.py:219: ValueError __________ test_dimdtypeset[FixedThresholdClassifierScikitsLearnNode] __________ klass = <class 'mdp.nodes.wrap_scikits_classifier.<locals>.ScikitsNode'> init_args = [] inp_arg_gen = <function generic_test_factory.<locals>.<lambda> at 0x7f86f5a539c0> sup_arg_gen = <function _rand_labels at 0x7f86f5ad5620>, execute_arg_gen = None def test_dimdtypeset(klass, init_args, inp_arg_gen, sup_arg_gen, execute_arg_gen): init_args = call_init_args(init_args) inp = inp_arg_gen() # See https://github.com/mdp-toolkit/mdp-toolkit/pull/47 # and https://github.com/mdp-toolkit/mdp-toolkit/issues/62. if klass.__name__ in ABSINPUT_NODES: inp = numx.absolute(inp) > node = klass(*init_args) ^^^^^^^^^^^^^^^^^ mdp/test/test_nodes_generic.py:305: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = FixedThresholdClassifierScikitsLearnNode(input_dim=None, output_dim=None, dtype=None) input_dim = None, output_dim = None, dtype = None, kwargs = {} def __init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs): """ Initializes an object of type 'ScikitsNode'. :param input_dim: Dimensionality of the input. Default is None. :type input_dim: int :param output_dim: Dimensionality of the output. Default is None. :type output_dim: int :param dtype: Datatype of the input. Default is None. :type dtype: numpy.dtype or str """ if output_dim is not None: # output_dim and n_components cannot be defined at the same time if 'n_components' in kwargs: msg = ("Dimensionality set both by " "output_dim=%d and n_components=%d""") raise ScikitsException(msg % (output_dim, kwargs['n_components'])) super(ScikitsNode, self).__init__(input_dim=input_dim, output_dim=output_dim, dtype=dtype) > self.scikits_alg = scikits_class(**kwargs) ^^^^^^^^^^^^^^^^^^^^^^^ E TypeError: FixedThresholdClassifier.__init__() missing 1 required positional argument: 'estimator' mdp/nodes/scikits_nodes.py:246: TypeError _________ test_dimdtypeset[TunedThresholdClassifierCVScikitsLearnNode] _________ klass = <class 'mdp.nodes.wrap_scikits_classifier.<locals>.ScikitsNode'> init_args = [] inp_arg_gen = <function generic_test_factory.<locals>.<lambda> at 0x7f86f5a53a60> sup_arg_gen = <function _rand_labels at 0x7f86f5ad5620>, execute_arg_gen = None def test_dimdtypeset(klass, init_args, inp_arg_gen, sup_arg_gen, execute_arg_gen): init_args = call_init_args(init_args) inp = inp_arg_gen() # See https://github.com/mdp-toolkit/mdp-toolkit/pull/47 # and https://github.com/mdp-toolkit/mdp-toolkit/issues/62. if klass.__name__ in ABSINPUT_NODES: inp = numx.absolute(inp) > node = klass(*init_args) ^^^^^^^^^^^^^^^^^ mdp/test/test_nodes_generic.py:305: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = TunedThresholdClassifierCVScikitsLearnNode(input_dim=None, output_dim=None, dtype=None) input_dim = None, output_dim = None, dtype = None, kwargs = {} def __init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs): """ Initializes an object of type 'ScikitsNode'. :param input_dim: Dimensionality of the input. Default is None. :type input_dim: int :param output_dim: Dimensionality of the output. Default is None. :type output_dim: int :param dtype: Datatype of the input. Default is None. :type dtype: numpy.dtype or str """ if output_dim is not None: # output_dim and n_components cannot be defined at the same time if 'n_components' in kwargs: msg = ("Dimensionality set both by " "output_dim=%d and n_components=%d""") raise ScikitsException(msg % (output_dim, kwargs['n_components'])) super(ScikitsNode, self).__init__(input_dim=input_dim, output_dim=output_dim, dtype=dtype) > self.scikits_alg = scikits_class(**kwargs) ^^^^^^^^^^^^^^^^^^^^^^^ E TypeError: TunedThresholdClassifierCV.__init__() missing 1 required positional argument: 'estimator' mdp/nodes/scikits_nodes.py:246: TypeError ___________ test_dimdtypeset[SelfTrainingClassifierScikitsLearnNode] ___________ klass = <class 'mdp.nodes.wrap_scikits_classifier.<locals>.ScikitsNode'> init_args = [] inp_arg_gen = <function generic_test_factory.<locals>.<lambda> at 0x7f86f5a64fe0> sup_arg_gen = <function _rand_labels at 0x7f86f5ad5620>, execute_arg_gen = None def test_dimdtypeset(klass, init_args, inp_arg_gen, sup_arg_gen, execute_arg_gen): init_args = call_init_args(init_args) inp = inp_arg_gen() # See https://github.com/mdp-toolkit/mdp-toolkit/pull/47 # and https://github.com/mdp-toolkit/mdp-toolkit/issues/62. if klass.__name__ in ABSINPUT_NODES: inp = numx.absolute(inp) node = klass(*init_args) _train_if_necessary(inp, node, sup_arg_gen) > _stop_training_or_execute(node, inp) mdp/test/test_nodes_generic.py:307: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ mdp/test/test_nodes_generic.py:93: in _stop_training_or_execute node.stop_training() <string>:1: in <lambda> ??? mdp/signal_node.py:631: in stop_training self._train_seq[self._train_phase][1](*args, **kwargs) mdp/nodes/scikits_nodes.py:252: in _stop_training return self.scikits_alg.fit(self.data, self.labels, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3/dist-packages/sklearn/base.py:1365: in wrapper return fit_method(estimator, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3/dist-packages/sklearn/semi_supervised/_self_training.py:265: in fit self.estimator_ = self._get_estimator() ^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = SelfTrainingClassifier() def _get_estimator(self): """Get the estimator. Returns ------- estimator_ : estimator object The cloned estimator object. """ # TODO(1.8): remove and only keep clone(self.estimator) if self.estimator is None and self.base_estimator != "deprecated": estimator_ = clone(self.base_estimator) warn( ( "`base_estimator` has been deprecated in 1.6 and will be removed" " in 1.8. Please use `estimator` instead." ), FutureWarning, ) # TODO(1.8) remove elif self.estimator is None and self.base_estimator == "deprecated": > raise ValueError( "You must pass an estimator to SelfTrainingClassifier. Use `estimator`." E ValueError: You must pass an estimator to SelfTrainingClassifier. Use `estimator`. /usr/lib/python3/dist-packages/sklearn/semi_supervised/_self_training.py:219: ValueError =============================== warnings summary =============================== mdp/linear_flows_online.py:381 /<<PKGBUILDDIR>>/mdp/linear_flows_online.py:381: SyntaxWarning: invalid escape sequence '\ ' \ / mdp/nodes/isfa_nodes.py:109 /<<PKGBUILDDIR>>/mdp/nodes/isfa_nodes.py:109: SyntaxWarning: invalid escape sequence '\k' \kappa_{ICA}^{\tau} in the paper). Default is 1. ../../../usr/lib/python3/dist-packages/zombie_imp/__init__.py:1 /usr/lib/python3/dist-packages/zombie_imp/__init__.py:1: DeprecationWarning: the imp module was removed in favour of importlib. Someone brought it back, but it's not a good idea to use it. from .imp_3_11 import * mdp/test/test_IncSFANode.py: 74995 warnings mdp/test/test_MCANode.py: 80880 warnings mdp/test/test_nodes_generic.py: 20000 warnings /<<PKGBUILDDIR>>/mdp/nodes/mca_nodes_online.py:132: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) self.d[j] = mdp.numx.sqrt(l) -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html ===================================== NOTE ===================================== python: 3.13.9.final.0 mdp: 3.6 parallel python: NOT AVAILABLE: No module named 'pp' shogun: NOT AVAILABLE: No module named 'shogun' libsvm: NOT AVAILABLE: No module named 'libsvm' joblib: 1.4.2 sklearn: 1.7.2 numx: numpy 2.3.4 symeig: symeig_fake Random Seed: 725021957 IMPORTANT: some tests use random numbers. This could occasionally lead to failures due to numerical degeneracies. To rule this out, please run the tests more than once. If you get reproducible failures please report a bug! =========================== short test summary info ============================ FAILED mdp/test/test_nodes_generic.py::test_dtype_consistency[FixedThresholdClassifierScikitsLearnNode] FAILED mdp/test/test_nodes_generic.py::test_dtype_consistency[TunedThresholdClassifierCVScikitsLearnNode] FAILED mdp/test/test_nodes_generic.py::test_dtype_consistency[SelfTrainingClassifierScikitsLearnNode] FAILED mdp/test/test_nodes_generic.py::test_outputdim_consistency[FixedThresholdClassifierScikitsLearnNode] FAILED mdp/test/test_nodes_generic.py::test_outputdim_consistency[TunedThresholdClassifierCVScikitsLearnNode] FAILED mdp/test/test_nodes_generic.py::test_outputdim_consistency[SelfTrainingClassifierScikitsLearnNode] FAILED mdp/test/test_nodes_generic.py::test_dimdtypeset[FixedThresholdClassifierScikitsLearnNode] FAILED mdp/test/test_nodes_generic.py::test_dimdtypeset[TunedThresholdClassifierCVScikitsLearnNode] FAILED mdp/test/test_nodes_generic.py::test_dimdtypeset[SelfTrainingClassifierScikitsLearnNode] ==== 9 failed, 843 passed, 16 skipped, 175878 warnings in 72.17s (0:01:12) ===== E: pybuild pybuild:389: test: plugin custom failed with: exit code=1: python3.13 -m pytest --seed=725021957 mdp && python3.13 -m pytest --seed=725021957 bimdp dh_auto_test: error: pybuild --test --test-pytest -i python{version} -p 3.13 returned exit code 13 make[1]: *** [debian/rules:10: override_dh_auto_test] Error 25 make[1]: Leaving directory '/<<PKGBUILDDIR>>' make: *** [debian/rules:7: binary] Error 2 dpkg-buildpackage: error: debian/rules binary subprocess returned exit status 2 --------------------------------------------------------------------------------
