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Codecov ReportBase: 85.49% // Head: 84.50% // Decreases project coverage by
Additional details and impacted files@@ Coverage Diff @@
## development #485 +/- ##
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- Coverage 85.49% 84.50% -0.99%
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Files 231 232 +1
Lines 16351 16475 +124
Branches 3028 2734 -294
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- Hits 13979 13923 -56
- Misses 1533 1675 +142
- Partials 839 877 +38
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autoPyTorch/api/base_task.py
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| y_test (np.ndarray): | ||
| The test ground truth labels. | ||
| score_kwargs: Any | ||
| additional arguments for computing the scores. Some metrics might require special arguments |
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could you add a note saying something like: Currently, needed for time series forecasting tasks. See autoPyTorch/api/time_series_forecasting.py
| if isinstance(seasonality, list): | ||
| seasonality = min(seasonality) # Use to calculate MASE | ||
| self.seasonality = int(seasonality) # type: ignore[call-overload] | ||
| self.seasonality = self.datamanager.seasonality |
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In the AbstractEvaluator class, we use a _init_datamanager_info (see here). At the end of this method we delete the datamaneger object to reduce the memory usage. Could you also update the time series forecasting evaluator, maybe overriding the parent class function?
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you can also do it as a separate PR
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data manager is required somewhere else by the time series evaluator because I defined time series dataset's getitem differently. Maybe in the future, we could fix that. But currently, I would prefer to keep self.datamanager in the evaluator
ravinkohli
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ravinkohli
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Thanks for your changes. I think the tests are failing because we need to update the requirements. Particularly, gluonts version 0.11.4 does not have 'DayOfMonth' from 'gluonts.time_feature'. Could you take a look?
This is fixed. However, I am considering removing all these dependencies (gluonts, pytorch-forecasting) in the future (which might take some time, though). |
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Description
Fix #484 to allow the forecasting-related arguments to be passed to compute
task.scoreMotivation and Context
How has this been tested?