99 update plot precision recall curve with thresholds annotations to match sklearns plot chance level behavior#125
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- Updated `plot_roc_curve_with_thresholds_annotations` to include customizable styling for the random classifier line via `random_classifier_line_kw`. - Introduced `plot_precision_recall_curve_with_thresholds_annotations` to support plotting a chance level prevalence line with customizable styling through `chance_level_kw`. - Added handling for average precision score calculation in the precision-recall curve function. - Updated documentation to reflect new parameters and their usage. - Modified tests to cover new functionalities and ensure robust validation. These enhancements improve the flexibility and usability of the plotting functions, allowing for better customization in visualizations.
…Handling and Annotations - Updated `plot_roc_curve_with_thresholds_annotations` and `plot_precision_recall_curve_with_thresholds_annotations` to include hover information for better interactivity in visualizations. - Improved error handling for average precision score calculations, providing clearer error messages for users. - Enhanced documentation to clarify parameter usage and updated descriptions for better understanding. - Modified tests to ensure robust validation of new functionalities and error handling scenarios. These enhancements improve the usability and clarity of the plotting functions, making them more informative and user-friendly.
- Updated `plot_precision_recall_curve_with_thresholds_annotations` to clarify the inference of the positive class when `positive_label` is None, aligning with scikit-learn's behavior. - Refactored the handling of the `positive_label` parameter to use `effective_positive_label` for improved clarity and consistency. - Modified tests for `plot_precision_recall_curve_with_thresholds_annotations` to ensure robust validation, including a new test for non-binary `y_true` inputs. These changes improve the usability and accuracy of the precision-recall curve plotting function, enhancing its documentation and test coverage.
- Updated the `plot_precision_recall_curve_with_thresholds_annotations` function to clarify the resolution of the positive label when `plot_chance_level=True` and `positive_label=None`, ensuring compliance with scikit-learn conventions. - Modified tests to validate the behavior of the function when handling non-binary `y_true` inputs, including assertions to confirm that the precision-recall curve function is not called in such cases. These changes improve the robustness and clarity of the precision-recall curve plotting functionality.
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- Increased the tolerance from 15 to 17 in the `test_plot_precision_recall_curve_with_thresholds_annotations_exists_figure` test to accommodate minor variations in generated plots. This change ensures that the test remains robust against slight differences in rendering while maintaining the integrity of the precision-recall curve visualizations.
idanmoradarthas
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99-update-plot_precision_recall_curve_with_thresholds_annotations-to-match-sklearns-plot_chance_level-behavior
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April 27, 2026 18:20
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Summary
This PR refactors and enhances the evaluation curve plotting functions in
ds_utils.metrics.curvesto align more closely with scikit-learn conventions and provide better visual diagnostics. The primary focus was on correcting the "chance level" representation in Precision-Recall curves and improving the overall usability and styling of both ROC and PR plots.Key Changes
Precision-Recall Curves (
plot_precision_recall_curve_with_thresholds_annotations)sklearn.metrics.PrecisionRecallDisplay.average_precision_scorecalculation. Classifier legends now displayClassifier (AP = 0.XX), and the chance level legend displaysChance level (AP = prevalence).plot_chance_levelandchance_level_kwto control the display and styling of the prevalence line.sample_weight.positive_labelparameter was being mutated internally.positive_label.ROC Curves (
plot_roc_curve_with_thresholds_annotations)random_classifier_line_kwto allow customization of the diagonal random classifier line."Random Classifier (AUC = 0.50)"to match scikit-learn conventions.General Improvements
hoverinfo="text"for classifier curves andhoverinfo="name"for reference lines to reduce Plotly's default hover clutter.try/exceptblocks for curve generation vs. metric calculation to provide more precise error messages.README.md,skills/metrics/SKILL.md, and all function docstrings to reflect API changes and new parameters.Testing & Verification
test_metrics/test_curves.pyto usepytest.mark.parametrizefor better coverage with less duplication.roc_auc_score,average_precision_score) across the test suite.pytest-mplimage comparisons pass with the new styling and legend updates.ruff check.Breaking Changes
add_random_classifier_lineparameter fromplot_precision_recall_curve_with_thresholds_annotations(as the diagonal line was incorrect for PR curves) and replaced it withplot_chance_level.