Enable known_covariates in TimeSeriesCloudPredictor.predict_real_time #181
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shchur
commented
Apr 28, 2025
| @@ -198,6 +204,7 @@ def predict_real_time( | |||
| target=self.target_column, | |||
| static_features=static_features, | |||
| accept=accept, | |||
| inference_kwargs=dict(known_covariates=known_covariates, **kwargs), | |||
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I validated that this works locally but couldn't update the unit tests because currently known_covariates can only work with real-time prediction (and not batch prediction). One possible solution would be to train two predictors: one that expects known_covariates and one that doesn't, but I think that would be too much of an overhead.
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We might want to revisit this PR #113 for batch prediction support.
suzhoum
approved these changes
Apr 28, 2025
| @@ -198,6 +204,7 @@ def predict_real_time( | |||
| target=self.target_column, | |||
| static_features=static_features, | |||
| accept=accept, | |||
| inference_kwargs=dict(known_covariates=known_covariates, **kwargs), | |||
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We might want to revisit this PR #113 for batch prediction support.
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Issue #, if available: Fixes #116, #70
Description of changes:
known_covariateskwarg toTimeSeriesCloudPredictor.predict_real_timevia theinference_kwargsto theTimeSeriesSagemakerBackend.predict_real_timemethod.known_covariatescannot be used during batch prediction (only real-time inference supported)By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.