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Bug#966971: tpot: FTBFS: dh_auto_test: error: pybuild --test --test-nose -i python{version} -p 3.8 returned exit code 13

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  Bug#966971: tpot: FTBFS: dh_auto_test: error: pybuild --test --test-nose -i python{version} -p 3.8 returned exit code 13 Lucas Nussbaum <lucas@debian.org> - 2020-08-03 11:10 +0200
    Bug#966971: marked as done (tpot: FTBFS: dh_auto_test: error:  pybuild --test --test-nose -i python{version} -p 3.8 returned exit code  13) "Debian Bug Tracking System" <owner@bugs.debian.org> - 2020-08-15 22:30 +0200

#283716 — Bug#966971: tpot: FTBFS: dh_auto_test: error: pybuild --test --test-nose -i python{version} -p 3.8 returned exit code 13

Source: tpot
Version: 0.11.1+dfsg2-3
Severity: serious
Justification: FTBFS on amd64
Tags: bullseye sid ftbfs
Usertags: ftbfs-20200802 ftbfs-bullseye

Hi,

During a rebuild of all packages in sid, your package failed to build
on amd64.

Relevant part (hopefully):
> make[1]: Entering directory '/<<PKGBUILDDIR>>'
> dh_auto_build
> I: pybuild base:217: /usr/bin/python3 setup.py build 
> running build
> running build_py
> creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot
> copying tpot/gp_types.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot
> copying tpot/driver.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot
> copying tpot/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot
> copying tpot/decorators.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot
> copying tpot/export_utils.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot
> copying tpot/operator_utils.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot
> copying tpot/gp_deap.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot
> copying tpot/metrics.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot
> copying tpot/_version.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot
> copying tpot/base.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot
> copying tpot/tpot.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot
> creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/config
> copying tpot/config/classifier_mdr.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/config
> copying tpot/config/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/config
> copying tpot/config/regressor_sparse.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/config
> copying tpot/config/classifier_light.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/config
> copying tpot/config/regressor_mdr.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/config
> copying tpot/config/regressor_light.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/config
> copying tpot/config/classifier.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/config
> copying tpot/config/regressor.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/config
> copying tpot/config/classifier_sparse.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/config
> creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/builtins
> copying tpot/builtins/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/builtins
> copying tpot/builtins/one_hot_encoder.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/builtins
> copying tpot/builtins/zero_count.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/builtins
> copying tpot/builtins/stacking_estimator.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/builtins
> copying tpot/builtins/feature_set_selector.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/builtins
> copying tpot/builtins/combine_dfs.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/builtins
> copying tpot/builtins/feature_transformers.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tpot/builtins
> mkdocs build --clean --theme readthedocs
> WARNING -  Config value: 'pages'. Warning: The 'pages' configuration option has been deprecated and will be removed in a future release of MkDocs. Use 'nav' instead. 
> INFO    -  Cleaning site directory 
> INFO    -  Building documentation to directory: /<<PKGBUILDDIR>>/docs 
> rm -f docs/sitemap.xml.gz
> cp -r images docs/
> sed -i -e 's,https://raw.githubusercontent.com/EpistasisLab/tpot/master/,,' docs/index.html
> make[1]: Leaving directory '/<<PKGBUILDDIR>>'
>    dh_auto_test -O--buildsystem=pybuild
> I: pybuild pybuild:284: cp -r /<<PKGBUILDDIR>>/tests /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build; sed -i -e 's/python -m/python3.8 -m/' /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tests/driver_tests.py
> I: pybuild base:217: cd /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build; python3.8 -m nose -v tests
> Assert that the TPOT driver stores correct default values for all parameters. ... ok
> Assert that _print_args prints correct values for all parameters in default settings. ... ok
> Assert that _print_args prints correct values for all parameters in regression mode. ... ok
> driver_tests.test_scoring_function_argument ... ok
> Assert that the TPOT driver outputs normal result in mode mode. ... /usr/lib/python3.8/runpy.py:127: RuntimeWarning: 'tpot.driver' found in sys.modules after import of package 'tpot', but prior to execution of 'tpot.driver'; this may result in unpredictable behaviour
>   warn(RuntimeWarning(msg))
> ok
> Assert that the tpot_driver() in TPOT driver outputs normal result with verbosity = 1. ... ok
> Assert that the tpot_driver() in TPOT driver outputs normal result with verbosity = 2. ... ok
> Assert that the tpot_driver() in TPOT driver outputs normal result with verbosity = 3. ... ok
> Assert that the tpot_driver() in TPOT driver outputs normal result with exported python file and verbosity = 0. ... ok
> Assert that _read_data_file raises ValueError when the targe column is missing. ... ok
> Assert that the TPOT CLI interface's integer parsing throws an exception when n < 0. ... ok
> Assert that the TPOT CLI interface's integer parsing returns the integer value of a string encoded integer when n > 0. ... ok
> Assert that the TPOT CLI interface's integer parsing throws an exception when n is not an integer. ... ok
> Assert that the TPOT CLI interface's positive_integer_or_none parsing throws an exception when n < 0. ... ok
> Assert that the TPOT CLI interface's positive_integer_or_none parsing returns the integer value of a string encoded integer when n > 0. ... ok
> Assert that the TPOT CLI interface's positive_integer_or_none parsing throws an exception when n is not an integer and not None. ... ok
> Assert that the TPOT CLI interface's positive_integer_or_none parsing return None when value is string 'None' or 'none'. ... ok
> Assert that the TPOT CLI interface's float range returns a float with input is in 0. - 1.0. ... ok
> Assert that the TPOT CLI interface's float range throws an exception when input it out of range. ... ok
> Assert that the TPOT CLI interface's float range throws an exception when input is not a float. ... ok
> Assert that the TPOTClassifier can generate the same pipeline export with random seed of 39. ... ok
> Assert that TPOT's export function throws a RuntimeError when no optimized pipeline exists. ... ok
> Assert that TPOT's export function returns the expected pipeline text as a string. ... ok
> Assert that generate_pipeline_code() returns the correct code given a specific pipeline. ... ok
> Assert that generate_pipeline_code() returns the correct code given a specific pipeline with two CombineDFs. ... ok
> Assert that generate_import_code() returns the correct set of dependancies for a given pipeline. ... ok
> Assert that generate_import_code() returns the correct set of dependancies and dependancies are importable. ... ok
> Assert that the TPOT FeatureAgglomeration operator exports as expected ... ok
> Assert that the TPOT FastICA operator exports as expected ... ok
> Assert that the TPOT PCA operator exports as expected ... ok
> Assert that the TPOT ExtraTreesClassifier operator exports as expected ... ok
> Assert that the TPOT GradientBoostingClassifier operator exports as expected ... ok
> Assert that the TPOT RandomForestClassifier operator exports as expected ... ok
> Assert that the TPOT RFE operator exports as expected ... ok
> Assert that the TPOT SelectFromModel operator exports as expected ... ok
> Assert that the TPOT SelectFwe operator exports as expected ... ok
> Assert that the TPOT SelectPercentile operator exports as expected ... ok
> Assert that the TPOT VarianceThreshold operator exports as expected ... ok
> Assert that the TPOT Nystroem operator exports as expected ... ok
> Assert that the TPOT RBFSampler operator exports as expected ... ok
> Assert that the TPOT LogisticRegression operator exports as expected ... ok
> Assert that the TPOT SGDClassifier operator exports as expected ... ok
> Assert that the TPOT BernoulliNB operator exports as expected ... ok
> Assert that the TPOT GaussianNB operator exports as expected ... ok
> Assert that the TPOT MultinomialNB operator exports as expected ... ok
> Assert that the TPOT KNeighborsClassifier operator exports as expected ... ok
> Assert that the TPOT Binarizer operator exports as expected ... ok
> Assert that the TPOT MaxAbsScaler operator exports as expected ... ok
> Assert that the TPOT MinMaxScaler operator exports as expected ... ok
> Assert that the TPOT Normalizer operator exports as expected ... ok
> Assert that the TPOT PolynomialFeatures operator exports as expected ... ok
> Assert that the TPOT RobustScaler operator exports as expected ... ok
> Assert that the TPOT StandardScaler operator exports as expected ... ok
> Assert that the TPOT LinearSVC operator exports as expected ... ok
> Assert that the TPOT DecisionTreeClassifier operator exports as expected ... ok
> Assert that the TPOT OneHotEncoder operator exports as expected ... ok
> Assert that the TPOT ZeroCount operator exports as expected ... ok
> Assert that exported_pipeline() generated a compile source file as expected given a fixed pipeline. ... ok
> Assert that exported_pipeline() generated a compile source file as expected given a fixed simple pipeline (only one classifier). ... ok
> Assert that exported_pipeline() generated a compile source file as expected given a fixed simple pipeline with a preprocessor. ... ok
> Assert that exported_pipeline() generated a compile source file as expected given a fixed simple pipeline with input_matrix in CombineDFs. ... ok
> Assert that exported_pipeline() generated a compile source file as expected given a fixed simple pipeline with SelectFromModel. ... ok
> Assert that exported_pipeline() generated a compile source file with random_state and data_file_path. ... ok
> Assert that a TPOT operator can export properly with a callable function as a parameter. ... ok
> Assert that a TPOT operator can export properly with a BaseEstimator as a parameter. ... ok
> Assert that the Operator class returns operators by name appropriately. ... ok
> Assert that get_by_name raises TypeError with a incorrect operator name. ... ok
> Assert that get_by_name raises ValueError with duplicate operators in operator dictionary. ... ok
> Assert that indenting a multiline string by 4 spaces prepends 4 spaces before each new line. ... ok
> Assert that the TPOTClassifier can generate a scored pipeline export correctly. ... ok
> Assert that TPOT exports a pipeline with an imputation step if imputation was used in fit(). ... ok
> export_tests.test_set_param_recursive ... ok
> Assert that set_param_recursive sets "random_state" to 42 in nested estimator in SelectFromModel. ... ok
> Assert that set_param_recursive sets "random_state" to 42 in nested estimator in StackingEstimator in a complex pipeline. ... ok
> Assert that the StackingEstimator returns transformed X based on test feature list 1. ... ok
> Assert that the StackingEstimator returns transformed X based on test feature list 2. ... ok
> Assert that the StackingEstimator returns transformed X based on 2 subsets' names ... ok
> Assert that the StackingEstimator returns transformed X based on 2 subsets' indexs ... ok
> Assert that the StackingEstimator returns transformed X seleced based on test feature list 1's index. ... ok
> Assert that the _get_support_mask function returns correct mask. ... ok
> Assert that the StackingEstimator works as expected when input X is np.array. ... ok
> Assert that the StackingEstimator rasies ValueError when features are not available. ... ok
> Assert that the StackingEstimator __name__ returns correct class name. ... ok
> Assert that CategoricalSelector works as expected. ... ok
> Assert that CategoricalSelector works as expected with threshold=5. ... ok
> Assert that CategoricalSelector works as expected with threshold=20. ... ok
> Assert that CategoricalSelector rasies ValueError without categorical features. ... ok
> Assert that fit() in CategoricalSelector does nothing. ... ok
> Assert that ContinuousSelector works as expected. ... ok
> Assert that ContinuousSelector works as expected with threshold=5. ... ok
> Assert that ContinuousSelector works as expected with svd_solver='full' ... ok
> Assert that ContinuousSelector rasies ValueError without categorical features. ... ok
> Assert that fit() in ContinuousSelector does nothing. ... ok
> /usr/lib/python3/dist-packages/sklearn/utils/deprecation.py:143: FutureWarning: The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.
>   warnings.warn(message, FutureWarning)
> Assert that automatic selection of categorical features works as expected with a threshold of 10. ... ok
> Test fit_transform a dense matrix. ... ok
> Test fit_transform a dense matrix with minimum_fraction=0.5. ... ok
> Test fit_transform a dense matrix including NaNs. ... ok
> Test fit_transform a dense matrix including NaNs with minimum_fraction=0.5 ... ok
> Test fit_transform a dense matrix including NaNs with specifying categorical_features. ... ok
> Test fit_transform a dense matrix with minimum_fraction as sparse ... ok
> Test fit_transform a dense matrix including all NaN slice. ... ok
> Test fit_transform a sparse matrix. ... ok
> Test fit_transform a sparse matrix with minimum_fraction=0.5. ... ok
> Test fit_transform a sparse matrix with specifying categorical_features. ... ok
> Test fit_transform a sparse matrix including all zeros slice. ... ok
> Test fit_transform a sparse matrix including all zeros slice with minimum_fraction=0.5. ... ok
> Test fit_transform another sparse matrix including all zeros slice. ... ok
> Test OneHotEncoder with both dense and sparse matrixes. ... ok
> Assert _transform_selected return original X when selected is empty list ... ok
> Assert _transform_selected return original X when selected is a list of False values ... ok
> Test OneHotEncoder with categorical_features='auto'. ... ok
> Assert that the StackingEstimator returns transformed X with synthetic features in classification. ... ok
> Assert that the StackingEstimator returns transformed X with a synthetic feature in regression. ... ok
> Assert that the StackingEstimator worked as expected in scikit-learn pipeline in classification. ... ok
> Assert that the StackingEstimator worked as expected in scikit-learn pipeline in regression. ... FAIL
> Asserts that gp_deap.initialize_stats_dict initializes individual statistics correctly ... ok
> Assert that self._mate_operator updates stats as expected. ... ok
> Asserts that self._random_mutation_operator updates stats as expected. ... ok
> Failure: SkipTest () ... SKIP
> Assert that the TPOT instantiator stores the TPOT variables properly. ... ok
> Assert that TPOT intitializes with the correct default scoring function. ... ok
> Assert that TPOT rasies ValueError with a invalid sklearn metric function. ... ok
> Assert that TPOT intitializes with a valid _BaseScorer. ... ok
> Assert that TPOT intitializes with a valid scorer. ... ok
> Assert that TPOT rasies ValueError with a invalid sklearn metric function roc_auc_score. ... ok
> Assert that TPOT rasies ValueError with a invalid sklearn metric function from __main__. ... ok
> Assert that TPOT rasies ValueError with a valid sklearn metric function from __main__. ... ok
> Assert that the TPOT intitializes raises a ValueError when the scoring metrics is not available in SCORERS. ... ok
> Assert that the TPOT fit function raises a ValueError when dataset is not in right format. ... ok
> Assert that the TPOT intitializes raises a ValueError when subsample ratio is not in the range (0.0, 1.0]. ... ok
> Assert that the TPOT intitializes raises a ValueError when the sum of crossover and mutation probabilities is large than 1. ... ok
> Assert that the TPOT init stores max run time and sets generations to 1000000. ... ok
> Assert that the TPOT init stores max run time but keeps the generations at the user-supplied value. ... ok
> Assert that the TPOT init stores current number of processes. ... ok
> Assert that the TPOT init assign right ... ok
> Assert that the TPOT init rasies ValueError if n_jobs=0. ... ok
> Assert that _wrapped_cross_val_score return Timeout in a time limit. ... ok
> Assert that _wrapped_cross_val_score return -float('inf') with a invalid_pipeline ... ok
> Assert that the balanced_accuracy in TPOT returns correct accuracy. ... ok
> Assert that get_params returns the exact dictionary of parameters used by TPOT. ... ok
> Assert that set_params returns a reference to the TPOT instance. ... ok
> Assert that set_params updates TPOT's instance variables. ... ok
> Assert that TPOTBase class raises RuntimeError when using it directly. ... ok
> Assert that TPOT uses the pre-configured dictionary of operators when config_dict is 'TPOT light' or 'TPOT MDR'. ... ok
> Assert that TPOT uses a custom dictionary of operators when config_dict is Python dictionary. ... ok
> Assert that TPOT uses a custom dictionary of operators when config_dict is the path of Python dictionary. ... ok
> Assert that _read_config_file rasies FileNotFoundError with a wrong path. ... ok
> Assert that _read_config_file rasies ValueError with wrong dictionary format ... ok
> Assert that _read_config_file rasies ValueError without a dictionary named 'tpot_config'. ... ok
> Assert that the TPOTClassifier can generate the same pipeline with same random seed. ... ok
> Assert that the TPOTRegressor can generate the same pipeline with same random seed. ... ok
> Assert that the TPOT score function raises a RuntimeError when no optimized pipeline exists. ... ok
> Assert that the TPOTClassifier score function outputs a known score for a fixed pipeline. ... ok
> Assert that the TPOTRegressor score function outputs a known score for a fixed pipeline. ... ok
> Assert that the TPOTRegressor score function outputs a known score for a fixed pipeline with sample weights. ... FAIL
> Assert that TPOT template option generates pipeline when each step is a type of operator. ... ok
> Assert that TPOT template option generates pipeline when each step is operator type with a duplicate main type. ... ok
> Assert that TPOT template option generates pipeline when one of steps is a specific operator. ... ok
> Assert that TPOT template option generates pipeline when one of steps is a specific operator. ... ok
> Assert that TPOT rasie ValueError when template parameter is invalid. ... ok
> Assert that TPOT properly handles the group parameter when using GroupKFold. ... ok
> Assert that the TPOT predict function raises a RuntimeError when no optimized pipeline exists. ... ok
> Assert that the TPOT predict function returns a numpy matrix of shape (num_testing_rows,). ... ok
> Assert that the TPOT predict function works on dataset with nan ... ok
> Assert that the TPOT predict_proba function returns a numpy matrix of shape (num_testing_rows, num_testing_target). ... ok
> Assert that the TPOT predict_proba function returns a numpy matrix filled with probabilities (float). ... ok
> Assert that the TPOT predict_proba function raises a RuntimeError when no optimized pipeline exists. ... ok
> Assert that the TPOT predict_proba function raises a RuntimeError when the optimized pipeline do not have the predict_proba() function ... ok
> Assert that the TPOT predict_proba function works on dataset with nan. ... ok
> Assert that the TPOT warm_start flag stores the pop and pareto_front from the first run. ... ok
> Assert that the TPOT fit function provides an optimized pipeline. ... ok
> Assert that the TPOT fit function provides an optimized pipeline when config_dict is 'TPOT light'. ... ok
> Assert that the TPOT fit function provides an optimized pipeline with subsample of 0.8. ... ok
> Assert that the TPOT fit function provides an optimized pipeline with max_time_mins of 2 second. ... ok
> Assert that the TPOT fit function provides an optimized pipeline with max_time_mins of 2 second with warm_start=True. ... ok
> Assert that the TPOT fit function provides an optimized pipeline with pandas DataFrame ... ok
> Assert that the TPOT fit function runs normally with memory='auto'. ... ok
> Assert that the TPOT _setup_memory function runs normally with a valid path. ... ok
> Assert that the TPOT fit function does not clean up caching directory when memory is a valid path. ... ok
> Assert that the TPOT _setup_memory function create a directory which does not exist. ... ok
> Assert that the TPOT _setup_memory function runs normally with a Memory object. ... ok
> Assert that the TPOT _setup_memory function rasies ValueError with a invalid object. ... ok
> Assert that the _check_periodic_pipeline exports periodic pipeline. ... ok
> Assert that the _check_periodic_pipeline rasie StopIteration if self._last_optimized_pareto_front_n_gens >= self.early_stop. ... ok
> Assert that the _save_periodic_pipeline does not export periodic pipeline if exception happened ... ok
> Assert that _save_periodic_pipeline creates the checkpoint folder and exports to it if it didn't exist ... ok
> Assert that the _save_periodic_pipeline does not export periodic pipeline if the pipeline has been saved before. ... ok
> Assert that the TPOT fit_predict function provides an optimized pipeline and correct output. ... ok
> Assert that the TPOT _update_top_pipeline updated an optimized pipeline. ... ok
> Assert that the TPOT _update_top_pipeline raises RuntimeError when self._pareto_front is empty. ... ok
> Assert that the TPOT _update_top_pipeline raises RuntimeError when self._optimized_pipeline is not updated. ... ok
> Assert that the TPOT _update_top_pipeline raises RuntimeError when self._optimized_pipeline is not updated. ... ok
> Assert that evaluated_individuals_ stores current pipelines and their CV scores. ... ok
> Assert that _stop_by_max_time_mins raises KeyboardInterrupt when maximum minutes have elapsed. ... ok
> Assert that _update_evaluated_individuals_ raises ValueError when scoring function does not return a float. ... ok
> Assert that _evaluate_individuals returns operator_counts and CV scores in correct order. ... ok
> Assert that _evaluate_individuals returns operator_counts and CV scores in correct order with n_jobs=2 ... ok
> Assert that _update_pbar updates self._pbar with printing correct warning message. ... ok
> Assert _update_val updates result score in list and prints timeout message. ... ok
> Assert _preprocess_individuals preprocess DEAP individuals including one evaluated individual ... ok
> Assert _preprocess_individuals preprocess DEAP individuals with one invalid pipeline ... ok
> Assert _preprocess_individuals updatas self._pbar.total when max_time_mins is not None ... ok
> Assert that the check_dataset function returns feature and target as expected. ... ok
> Assert that the check_dataset function raise ValueError when sample_weight can not be converted to float array ... ok
> Assert that the check_dataset function raise ValueError when sample_weight has NaN ... ok
> Assert that the check_dataset function raise ValueError when sample_weight has a length different length ... ok
> Assert that the check_dataset function returns feature and target as expected. ... ok
> Assert that the TPOT fit function will not raise a ValueError in a dataset where NaNs are present. ... ok
> Assert that the TPOT predict function will not raise a ValueError in a dataset where NaNs are present. ... ok
> Assert that the TPOT _impute_values function returns a feature matrix with imputed NaN values. ... ok
> Assert that the TPOT score function will not raise a ValueError in a dataset where NaNs are present. ... ok
> Assert that the TPOT fit function will raise a ValueError in a sparse matrix with config_dict='TPOT light'. ... ok
> Assert that the TPOT fit function will raise a ValueError in a sparse matrix with config_dict=None. ... ok
> Assert that the TPOT fit function will raise a ValueError in a sparse matrix with config_dict='TPOT MDR'. ... ok
> Assert that the TPOT fit function will not raise a ValueError in a sparse matrix with config_dict='TPOT sparse'. ... ok
> Assert that the TPOT fit function will not raise a ValueError in a sparse matrix with a customized config dictionary. ... ok
> Assert that the source_decode can decode operator source and import operator class. ... ok
> Assert that the source_decode return None when sourcecode is not available. ... ok
> Assert that the source_decode raise ImportError when sourcecode is not available and verbose=3. ... ok
> Assert that the TPOT operators class factory. ... ok
> Assert that TPOT allows only one PolynomialFeatures operator in a pipeline. ... ok
> Assert that pick_two_individuals_eligible_for_crossover() picks the correct pair of nodes to perform crossover with ... ok
> Assert that pick_two_individuals_eligible_for_crossover() returns the right output when no pair is eligible ... ok
> Assert that self._mate_operator returns offsprings as expected. ... ok
> Assert that cxOnePoint() returns the correct type of node between two fixed pipelines. ... ok
> Assert that mutNodeReplacement() returns the correct type of mutation node in a fixed pipeline. ... ok
> Assert that mutNodeReplacement() returns the correct type of mutation node in a complex pipeline. ... ok
> Assert that varOr() applys crossover only and removes CV scores in offsprings. ... ok
> Assert that varOr() applys mutation only and removes CV scores in offsprings. ... ok
> Assert that varOr() applys reproduction only and does NOT remove CV scores in offsprings. ... ok
> Assert that TPOT operators return their type, e.g. 'Classifier', 'Preprocessor'. ... ok
> Assert that TPOT's gen_grow_safe function returns a pipeline of expected structure. ... ok
> Assert that clean_pipeline_string correctly returns a string without parameter prefixes ... ok
> Assert that ZeroCount operator returns correct transformed X. ... ok
> Assert that fit() in ZeroCount does nothing. ... ok
> 
> ======================================================================
> FAIL: Assert that the StackingEstimator worked as expected in scikit-learn pipeline in regression.
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File "/usr/lib/python3/dist-packages/nose/case.py", line 197, in runTest
>     self.test(*self.arg)
>   File "/<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tests/stacking_estimator_tests.py", line 114, in test_StackingEstimator_4
>     assert np.allclose(known_cv_score, cv_score)
> AssertionError
> 
> ======================================================================
> FAIL: Assert that the TPOTRegressor score function outputs a known score for a fixed pipeline with sample weights.
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File "/usr/lib/python3/dist-packages/nose/case.py", line 197, in runTest
>     self.test(*self.arg)
>   File "/<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build/tests/tpot_tests.py", line 633, in test_sample_weight_func
>     assert np.allclose(known_score, score, rtol=0.01)
> AssertionError: 
> -------------------- >> begin captured stdout << ---------------------
> Warning: xgboost.XGBRegressor is not available and will not be used by TPOT.
> 
> --------------------- >> end captured stdout << ----------------------
> 
> ----------------------------------------------------------------------
> Ran 235 tests in 28.971s
> 
> FAILED (SKIP=1, failures=2)
> E: pybuild pybuild:352: test: plugin distutils failed with: exit code=1: cd /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_tpot/build; python3.8 -m nose -v tests
> dh_auto_test: error: pybuild --test --test-nose -i python{version} -p 3.8 returned exit code 13

The full build log is available from:
   http://qa-logs.debian.net/2020/08/02/tpot_0.11.1+dfsg2-3_unstable.log

A list of current common problems and possible solutions is available at
http://wiki.debian.org/qa.debian.org/FTBFS . You're welcome to contribute!

About the archive rebuild: The rebuild was done on EC2 VM instances from
Amazon Web Services, using a clean, minimal and up-to-date chroot. Every
failed build was retried once to eliminate random failures.

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#284799 — Bug#966971: marked as done (tpot: FTBFS: dh_auto_test: error: pybuild --test --test-nose -i python{version} -p 3.8 returned exit code 13)

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Your message dated Sat, 15 Aug 2020 20:25:45 +0000
with message-id <E1k72kL-000657-R2@fasolo.debian.org>
and subject line Bug#966971: fixed in tpot 0.11.5+dfsg-1
has caused the Debian Bug report #966971,
regarding tpot: FTBFS: dh_auto_test: error: pybuild --test --test-nose -i python{version} -p 3.8 returned exit code 13
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
message is talking about, this may indicate a serious mail system
misconfiguration somewhere. Please contact owner@bugs.debian.org
immediately.)


-- 
966971: https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=966971
Debian Bug Tracking System
Contact owner@bugs.debian.org with problems

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