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.