Improve Test Data for Precision-Recall Curve to Show Realistic Shape#124
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idanmoradarthas merged 2 commits intoApr 24, 2026
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- Added a new fixture `plotly_models_pr_curve_dict` to load precision-recall curve data from a JSON file. - Updated tests for plotting precision-recall curves to use the new fixture, ensuring consistency in data handling. - Modified baseline images for precision-recall curve tests to reflect recent changes in plotting behavior and annotations. These changes enhance the robustness of the precision-recall curve testing framework and ensure accurate visual comparisons.
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- Updated the `plot_precision_recall_curve_with_thresholds_annotations` function to handle NaN values in thresholds, ensuring that the displayed probability annotations are clear and informative. - Modified the text formatting for annotations to indicate 'N/A' for NaN probabilities, improving the clarity of the visual output. These changes enhance the usability of the precision-recall curve visualization by providing more accurate and user-friendly annotations.
idanmoradarthas
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April 24, 2026 03:27
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Resolves
Resolves #100
Description
This pull request addresses the need for realistic Precision-Recall curve test visualizations. The previous mock data generated arbitrary, near-perfect (right-angle) PR curves that did not accurately reflect real-world, descending trade-off curve shapes.
To solve this, a new dataset simulating a challenging classification task (with increased label noise and reduced class separation) was generated. The resulting realistic model predictions were saved to a new, dedicated JSON data file for the PR curve tests, ensuring the visual baseline tests correctly represent expected outputs.
Key Changes
plotly_models_pr_curve.jsonwhich contains simulated data with realistic PR threshold trade-offs for Decision Tree, Random Forest, and Gradient Boosting models.plotly_models_pr_curve_dictfixture intest_curves.pyto load the new dataset exclusively for Precision-Recall tests. The existingplotly_models.jsonis preserved to avoid breaking current ROC curve baseline tests.test_plotly_graph_method_shape_mismatchusingpytest.mark.parametrizeto dynamically handle validation against both data files without code duplication.ruffto comply with the project standards.Verification
test_curves.pyunit tests pass successfully.ruff checkandruff format.