Your message dated Sat, 15 Aug 2020 20:25:45 +0000
with message-id <[email protected]>
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 [email protected]
immediately.)
--
966971: https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=966971
Debian Bug Tracking System
Contact [email protected] with problems
--- Begin Message ---
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.
--- End Message ---
--- Begin Message ---
Source: tpot
Source-Version: 0.11.5+dfsg-1
Done: Christian Kastner <[email protected]>
We believe that the bug you reported is fixed in the latest version of
tpot, 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.
Christian Kastner <[email protected]> (supplier of updated tpot 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: Sat, 15 Aug 2020 21:45:37 +0200
Source: tpot
Architecture: source
Version: 0.11.5+dfsg-1
Distribution: unstable
Urgency: medium
Maintainer: Debian Science Maintainers
<[email protected]>
Changed-By: Christian Kastner <[email protected]>
Closes: 966971
Changes:
tpot (0.11.5+dfsg-1) unstable; urgency=medium
.
* New upstream release.
- Drop obsolete patches
- Refresh patches
- Add Unconditionally-increase-test-tolerances.patch
* Fix dversionmangle in d/watch
* Tweak Build-Depends versions
* Exclude some tests until pytorch, xgboost are available
* Skip a flaky test. (Closes: #966971)
Checksums-Sha1:
b82b4e9aea0008134c639bcf93f112d8bb934f33 2372 tpot_0.11.5+dfsg-1.dsc
a866cd8bf075a112a398770971dd4c02319224db 725884 tpot_0.11.5+dfsg.orig.tar.xz
fad22a8fe55c71941a18d307a9113a44e689beed 8624 tpot_0.11.5+dfsg-1.debian.tar.xz
5882ff4c3434bcb0fb16305edda279e395857938 7308
tpot_0.11.5+dfsg-1_source.buildinfo
Checksums-Sha256:
65a96a8b44bc9cab551c5ebea084a82ab3790fb28a35e93f3af72bfc2d515163 2372
tpot_0.11.5+dfsg-1.dsc
09a512f327c3414ae2c62154609bbf0a5e77f9e26810e5ea5be62054acf70719 725884
tpot_0.11.5+dfsg.orig.tar.xz
3800362aa092514c03ee0a48d692c7d26e11395b702766d87947fe0ab941f211 8624
tpot_0.11.5+dfsg-1.debian.tar.xz
9c0f4d8fe6aee301033cc7d754dd57a1f39243bf0d0dfb52db23ba1407e63a91 7308
tpot_0.11.5+dfsg-1_source.buildinfo
Files:
ac998d1da53e1104654888237ea07f12 2372 python optional tpot_0.11.5+dfsg-1.dsc
5121ef505224d04c1e5bdc16b3538b9c 725884 python optional
tpot_0.11.5+dfsg.orig.tar.xz
4bf3ce5a7a20df7f8cb82a7bd0c51e2d 8624 python optional
tpot_0.11.5+dfsg-1.debian.tar.xz
821d7f98adfac8a8e46c4aefba9a98d4 7308 python optional
tpot_0.11.5+dfsg-1_source.buildinfo
-----BEGIN PGP SIGNATURE-----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=0DQd
-----END PGP SIGNATURE-----
--- End Message ---
