[refactor] Separate all_nan_columns detection #401
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…oml#334) * [feat] Support statistics print by adding results manager object * [refactor] Make SearchResults extract run_history at __init__ Since the search results should not be kept in eternally, I made this class to take run_history in __init__ so that we can implicitly call extraction inside. From this change, the call of extraction from outside is not recommended. However, you can still call it from outside and to prevent mixup of the environment, self.clear() will be called. * [fix] Separate those changes into PR#336 * [fix] Fix so that test_loss includes all the metrics * [enhance] Strengthen the test for sprint and SearchResults * [fix] Fix an issue in documentation * [enhance] Increase the coverage * [refactor] Separate the test for results_manager to organize the structure * [test] Add the test for get_incumbent_Result * [test] Remove the previous test_get_incumbent and see the coverage * [fix] [test] Fix reversion of metric and strengthen the test cases * [fix] Fix flake8 issues and increase coverage * [fix] Address Ravin's comments * [enhance] Increase the coverage * [fix] Fix a flake8 issu
* [doc] Add workflow of the AutoPytorch * [doc] Address Ravin's comment
* [feat] Add an object that realizes the perf over time viz * [fix] Modify TODOs and add comments to avoid complications * [refactor] [feat] Format visualizer API and integrate this feature into BaseTask * [refactor] Separate a shared raise error process as a function * [refactor] Gather params in Dataclass to look smarter * [refactor] Merge extraction from history to the result manager Since this feature was added in a previous PR, we now rely on this feature to extract the history. To handle the order by the start time issue, I added the sort by endtime feature. * [feat] Merge the viz in the latest version * [fix] Fix nan --> worst val so that we can always handle by number * [fix] Fix mypy issues * [test] Add test for get_start_time * [test] Add test for order by end time * [test] Add tests for ensemble results * [test] Add tests for merging ensemble results and run history * [test] Add the tests in the case of ensemble_results is None * [fix] Alternate datetime to timestamp in tests to pass universally Since the mapping of timestamp to datetime variates on machine, the tests failed in the previous version. In this version, we changed the datetime in the tests to the fixed timestamp so that the tests will pass universally. * [fix] Fix status_msg --> status_type because it does not need to be str * [fix] Change the name for the homogeniety * [fix] Fix based on the file name change * [test] Add tests for set_plot_args * [test] Add tests for plot_perf_over_time in BaseTask * [refactor] Replace redundant lines by pytest parametrization * [test] Add tests for _get_perf_and_time * [fix] Remove viz attribute based on Ravin's comment * [fix] Fix doc-string based on Ravin's comments * [refactor] Hide color label settings extraction in dataclass Since this process makes the method in BaseTask redundant and this was pointed out by Ravin, I made this process a method of dataclass so that we can easily fetch this information. Note that since the color and label information always depend on the optimization results, we always need to pass metric results to ensure we only get related keys. * [test] Add tests for color label dicts extraction * [test] Add tests for checking if plt.show is called or not * [refactor] Address Ravin's comments and add TODO for the refactoring * [refactor] Change KeyError in EnsembleResults to empty Since it is not convenient to not be able to instantiate EnsembleResults in the case when we do not have any histories, I changed the functionality so that we can still instantiate even when the results are empty. In this case, we have empty arrays and it also matches the developers intuition. * [refactor] Prohibit external updates to make objects more robust * [fix] Remove a member variable _opt_scores since it is confusing Since opt_scores are taken from cost in run_history and metric_dict takes from additional_info, it was confusing for me where I should refer to what. By removing this, we can always refer to additional_info when fetching information and metrics are always available as a raw value. Although I changed a lot, the functionality did not change and it is easier to add any other functionalities now. * [example] Add an example how to plot performance over time * [fix] Fix unexpected train loss when using cross validation * [fix] Remove __main__ from example based on the Ravin's comment * [fix] Move results_xxx to utils from API * [enhance] Change example for the plot over time to save fig Since the plt.show() does not work on some environments, I changed the example so that everyone can run at least this example.
* cleanup of simple_imputer * Fixed doc and typo * Fixed docs * Made changes, added test * Fixed init statement * Fixed docs * Flake'd
…#351) * [feat] Add the option to save a figure in plot setting params Since non-GUI based environments would like to avoid the usage of show method in the matplotlib, I added the option to savefig and thus users can complete the operations inside AutoPytorch. * [doc] Add a comment for non-GUI based computer in plot_perf_over_time method * [test] Add a test to check the priority of show and savefig Since plt.savefig and plt.show do not work at the same time due to the matplotlib design, we need to check whether show will not be called when a figname is specified. We can actually raise an error, but plot will be basically called in the end of an optimization, so I wanted to avoid raising an error and just sticked to a check by tests.
* update workflow files * Remove double quotes * Exclude python 3.10 * Fix mypy compliance check * Added PEP 561 compliance * Add py.typed to MANIFEST for dist * Update .github/workflows/dist.yml Co-authored-by: Ravin Kohli <[email protected]> Co-authored-by: Ravin Kohli <[email protected]>
* Add fit pipeline with tests * Add documentation for get dataset * update documentation * fix tests * remove permutation importance from visualisation example * change disable_file_output * add * fix flake * fix test and examples * change type of disable_file_output * Address comments from eddie * fix docstring in api * fix tests for base api * fix tests for base api * fix tests after rebase * reduce dataset size in example * remove optional from doc string * Handle unsuccessful fitting of pipeline better * fix flake in tests * change to default configuration for documentation * add warning for no ensemble created when y_optimization in disable_file_output * reduce budget for single configuration * address comments from eddie * address comments from shuhei * Add autoPyTorchEnum * fix flake in tests * address comments from shuhei * Apply suggestions from code review Co-authored-by: nabenabe0928 <[email protected]> * fix flake * use **dataset_kwargs * fix flake * change to enforce keyword args Co-authored-by: nabenabe0928 <[email protected]>
* Add workflow for publishing docker image to github packages and dockerhub * add docker installation to docs * add workflow dispatch
* check if N==0, and handle this case * change position of comment * Address comments from shuhei
* add test evaluator * add no resampling and other changes for test evaluator * finalise changes for test_evaluator, TODO: tests * add tests for new functionality * fix flake and mypy * add documentation for the evaluator * add NoResampling to fit_pipeline * raise error when trying to construct ensemble with noresampling * fix tests * reduce fit_pipeline accuracy check * Apply suggestions from code review Co-authored-by: nabenabe0928 <[email protected]> * address comments from shuhei * fix bug in base data loader * fix bug in data loader for val set * fix bugs introduced in suggestions * fix flake * fix bug in test preprocessing * fix bug in test data loader * merge tests for evaluators and change listcomp in get_best_epoch * rename resampling strategies * add test for get dataset Co-authored-by: nabenabe0928 <[email protected]>
* [fix] Fix the no-training-issue when using simple intensifier * [test] Add a test for the modification * [fix] Modify the default budget so that the budget is compatible Since the previous version does not consider the provided budget_type when determining the default budget, I modified this part so that the default budget does not mix up the default budget for epochs and runtime. Note that since the default pipeline config defines epochs as the default budget, I also followed this rule when taking the default value. * [fix] Fix a mypy error * [fix] Change the total runtime for single config in the example Since the training sometimes does not finish in time, I increased the total runtime for the training so that we can accomodate the training in the given amount of time. * [fix] [refactor] Fix the SMAC requirement and refactor some conditions
* add variance thresholding * fix flake and mypy * Apply suggestions from code review Co-authored-by: nabenabe0928 <[email protected]> Co-authored-by: nabenabe0928 <[email protected]>
* Add new scalers * fix flake and mypy * Apply suggestions from code review Co-authored-by: nabenabe0928 <[email protected]> * add robust scaler * fix documentation * remove power transformer from feature preprocessing * fix tests * check for default in include and exclude * Apply suggestions from code review Co-authored-by: nabenabe0928 <[email protected]> Co-authored-by: nabenabe0928 <[email protected]>
* remove categorical strategy from simple imputer * fix tests * address comments from eddie * fix flake and mypy error * fix test cases for imputation
* [fix] Add check dataset in transform as well for test dataset, which does not require fit * [test] Migrate tests from the francisco's PR without modifications * [fix] Modify so that tests pass * [test] Increase the coverage
* Fix: keyword arguments to submit * Fix: Missing param for implementing AbstractTA * Fix: Typing of multi_objectives * Add: mutli_objectives to each ExecuteTaFucnWithQueue
* remove datamanager instances from evaluation and smbo * fix flake * Apply suggestions from code review Co-authored-by: nabenabe0928 <[email protected]> * fix flake Co-authored-by: nabenabe0928 <[email protected]>
* [fix] Fix the task inference issue mentioned in automl#352 Since sklearn task inference regards targets with integers as a classification task, I modified target_validator so that we always cast targets for regression to float. This workaround is mentioned in the reference below: scikit-learn/scikit-learn#8952 * [fix] [test] Add a small number to label for regression and add tests Since target labels are required to be float and sklearn requires numbers after a decimal point, I added a workaround to add the almost possible minimum fraction to array so that we can avoid a mis-inference of task type from sklearn. Plus, I added tests to check if we get the expected results for extreme cases. * [fix] [test] Adapt the modification of targets to scipy.sparse.xxx_matrix * [fix] Address Ravin's comments and loosen the small number choice
* Initial implementation without tests * add tests and make necessary changes * improve documentation * fix tests * Apply suggestions from code review Co-authored-by: nabenabe0928 <[email protected]> * undo change in as it causes tests to fail * change name from InputValidator to input_validator * extract statements to methods * refactor code * check if mapping is the same as expected * update precision reduction for dataframes and tests * fix flake Co-authored-by: nabenabe0928 <[email protected]>
* in progress * add remaining preprocessors * fix flake and mypy after rebase * Fix tests and add documentation * fix tests bug * fix bug in tests * fix bug where search space updates were not honoured * handle check for score func in feature preprocessors * address comments from shuhei * apply suggestions from code review * add documentation for feature preprocessors with percent to int value range * fix tests * fix tests * address comments from shuhei * fix tests which fail due to scaler
nabenabe0928
changed the title
Codecov Report
@@ Coverage Diff @@
## development #401 +/- ##
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- Coverage 84.15% 83.96% -0.20%
===============================================
Files 175 175
Lines 10169 10177 +8
Branches 1747 1744 -3
===============================================
- Hits 8558 8545 -13
- Misses 1112 1133 +21
Partials 499 499
Continue to review full report at Codecov.
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The previous version provided non-sparse data at the training and sparse data at the test time, which is invalid. That is why I fixed this error.
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Check
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ravinkohli
requested changes
Jun 14, 2022
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Refactoring