[FIX] results management and visualisation with missing test data #465
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ravinkohli
changed the title
Codecov Report
@@ Coverage Diff @@
## development #465 +/- ##
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+ Coverage 64.65% 85.48% +20.82%
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Files 231 231
Lines 16304 16351 +47
Branches 3009 3028 +19
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+ Hits 10542 13977 +3435
+ Misses 4714 1535 -3179
+ Partials 1048 839 -209
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theodorju
reviewed
Aug 10, 2022
autoPyTorch/utils/results_manager.py
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| OPTIONAL_INFERENCE_CHOICES = ('test',) | |||
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Would it make sense to transfer this to constants.py?
autoPyTorch/utils/results_manager.py
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| Checks if the data is missing for each optional inference choice and | ||
| sets the scores for that inference choice to all None. |
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It would be nice to also add the case of score == metric._worst_possible_result in the docstring:
Checks if the data is missing or if all scores are equal to the worst possible result for each optional inference choice and sets the scores for that inference choice to all None.
theodorju
approved these changes
Aug 12, 2022
ravinkohli
added a commit
that referenced
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Aug 23, 2022
* [FIX] Documentation and docker workflow file (#449) * fixes to documentation and docker * fix to docker * Apply suggestions from code review * add change log for release (#450) * [FIX] release docs (#452) * Release 0.2 * Release 0.2.0 * fix docs new line * [FIX] ADD forecasting init design to pip data files (#459) * add forecasting_init.json to data files under setup * avoid undefined reference in scale_value * checks for time series dataset split (#464) * checks for time series dataset split * maint * Update autoPyTorch/datasets/time_series_dataset.py Co-authored-by: Ravin Kohli <13005107+ravinkohli@users.noreply.github.com> Co-authored-by: Ravin Kohli <13005107+ravinkohli@users.noreply.github.com> * [FIX] Numerical stability scaling for timeseries forecasting tasks (#467) * resolve rebase conflict * add checks for scaling factors * flake8 fix * resolve conflict * [FIX] pipeline options in `fit_pipeline` (#466) * fix update of pipeline config options in fit pipeline * fix flake and test * suggestions from review * [FIX] results management and visualisation with missing test data (#465) * add flexibility to avoid checking for test scores * fix flake and test * fix bug in tests * suggestions from review * [ADD] Robustly refit models in final ensemble in parallel (#471) * add parallel model runner and update running traditional classifiers * update pipeline config to pipeline options * working refit function * fix mypy and flake * suggestions from review * fix mypy and flake * suggestions from review * finish documentation * fix tests * add test for parallel model runner * fix flake * fix tests * fix traditional prediction for refit * suggestions from review * add warning for failed processing of results * remove unnecessary change * update autopytorch version number * update autopytorch version number and the example file * [DOCS] Release notes v0.2.1 (#476) * Release 0.2.1 * add release docs * Update docs/releases.rst Co-authored-by: Difan Deng <33290713+dengdifan@users.noreply.github.com>
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Fixes issue #455
Types of changes
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Description
Currently, the results_manager.py and results_visualiser.py assumes that we always have the test data. However, in the API class, we allow the users to run the optimisation without giving test data and I think it is a common use case, especially in AutoML. This PR makes the necessary changes to allow visualising and storing results when no test data is passed.
Motivation and Context
Fixes #455
How has this been tested?
I have added tests where the run history does not contain the test scores and the tests ensure that the sprint_statistics and the plot_perf_over_time functions work with this run history