Added anomaly display cell and comments #1493
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dennisbader
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May 10, 2024
* Small fix in utils.py * Factorize tests * Correct format * Dataset taxiNY * jupyter notebook addition, XX-anomaly-detection.ipynb * relocated XX-anomaly-detection.ipynb * Fix NormScorer proba input, and show_anomaly function * Fix NormScorer proba input, and show_anomaly function * Refactor window of Wasserstein, Kmeans and PyOD Scorers * Refactor test * Added anomaly display cell and comments (#1493) * Added anomaly display cell and comments * Added samuele comments Co-authored-by: julien12234 <julien.adda@gmail.com> * Added images, and Julien's recommendation * Added parameter window_transform, git statusChange the default windowing methodgit status * fix: solve error due to merge conflict and apply linting * round of Julien_H's comment * with images * states to values * Committing old local changes * Small fix * fix: reduced code redundancy between the two detectors, renamed the method eval_accuracy to eval_metric * refactor: simplified class hierarchy, added a bit of type hinting, fixed bug in predict * feat: migrated tests from unittest to pytest framework for the aggregators * feat: parametrized tests to reduce code repetition * fix: added docstring, increased test granularity * fix: bug in fittableaggreg predict sanity check * refactor: renamed eval_accuracy to eval_metric, removed NonFittableScorer class * fix: changed tests after eval_accuracy function name change * refactor: changed scorers tests from unittest to pytest framework * fix: all non fittable anomaly scorer are tested * refactor: renamed eval_accuracy eval_metric * refactor: changed framework from unittest to pytest * fix: typo * refactor: changed test framework from unittest to pytest * refactor: reduced code redundancy by using pytest.mark.parametrize * refactor: reduced redundant code in kmeans, pyod and wasserstein scorers * fix: logging * fix: ad module use series2seq instead of its own util method * refactor: single show_anomalies method across anomaly model classes * fix: modularized scorer training, fixed logging * fix: indentation error * feat: parallelize training of scorers * feat: parallelize scorer score method for component-wise multivariate * feat: parallelize and/or aggregators predict_core method * feat: simplified aggregation of anomaly scorer, added corresponding tests * Apply suggestions from code review Co-authored-by: Samuele Giuliano Piazzetta <samuele.piazzetta@gmail.com> * fix lint * update docs init and utils * refactor ad utils * refactor aggregators * refactor aggregators * refactor aggregators * refactor detectors * refactor module docs * refactor anomaly models * refactor anomaly models * refactor scorers * update diff_fn for scorers * refactor WindowAnomalyScorer * refactor tabularization for scorers * improve scorer docs * refactor score_from_prediction * use slice_intersect_values in ad evaluation * further code clean up * further code clean up * make API consistent * refactor show anomalies api * refactor eval_metric api for anomaly models * refactor eval_metric api for anomaly scorers * refactor eval_metric api for anomaly detectors * refactor eval_metric api for anomaly aggregator * enfore GlobalForecastingModel for AnomalyModel * update changelog * remove prefix in AD API to keep unified and covariates parameter names * apply suggestions from PR review * final updates * improve docs * revert changes * prepare example notebook * update changelog * add taxi dataset test --------- Co-authored-by: Samuele Giuliano Piazzetta <samuele.piazzetta@gmail.com> Co-authored-by: madtoinou <32447896+madtoinou@users.noreply.github.com> Co-authored-by: Antoine Madrona <antoine.madrona@epfl.ch> Co-authored-by: Julien Sven Adda <julien.adda@epfl.ch> Co-authored-by: madtoinou <antoine.madrona@unit8.co> Co-authored-by: dennisbader <dennis.bader@gmx.ch>
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Summary
Review of the AD example notebook.
Details
In this way, we can help the user understand the anomalies in the dataset we are showcasing.
Added some comments in the cells (in red). In general, I would suggest to: