feature: add ModelPerformanceCallback that raises ModelUnderperfomingError#875
Merged
Merged
Conversation
…Error if model underperforms
|
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
This pull request introduces a new callback for evaluating model performance in forecasting workflows, ensuring that models meet defined performance criteria before proceeding. It also adds a custom exception for underperforming models and comprehensive unit tests for the new logic.
Model performance evaluation and error handling:
ModelPerformanceCallbackclass inmodel_performance_callback.pythat checks model performance against a specified metric and threshold at the end of fitting. If the model does not meet the criteria, aModelUnderperfomingErroris raised, allowing workflows to stop early or use fallback models.ModelUnderperfomingErrorexception inexceptions.py, providing a descriptive error message when a model fails to meet performance requirements.Testing:
test_model_performance_callback.pyto verify correct behavior of the callback, including scenarios where metrics are missing, metric values are above/below thresholds, and both "higher_is_better" and "lower_is_better" directions are handled.