Detecting and Reacting to Data-Drift in Streaming Optimisation Domains - Code and Data
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| Natura: | Recurso digital |
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Zenodo
2024
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| contents | <p>This repository contains the code and data for reproducibility of the paper 'Detecting and Reacting to Data-Drift in Streaming Optimisation Domains'. </p> <p>The following files are included:</p> <ul> <li>algo_data.zip : contains algorithm raw performance data;</li> <li>Art_gallery_features_500_samples_full.csv : ELA feature data on the Art Gallery problem;</li> <li>average_loss_accuracy_chunk.csv : results of the encoding switching when drift is detected;</li> <li>drift_detection_zenodo.ipynb : jupyter notebook with the code do to generate the incremental drift scenario and detect drift with NannyML (other scenearios can be generated using this base);</li> <li>plot_features.zip : additional plots not available in the paper;</li> <li>Objective_function.zip : singularity containers and binaries of the code used for the objective function.</li> </ul> <p>How to launch the objective functions:</p> <ul> <li>singularity run drift_num.sif instance_size nb_cameras nb_constraints x0 y0 x1 y1 ... (x,y being the coordinates of the cameras)</li> <li>singularity run drift_bit.sif instance_size nb_cameras nb_constraints bistring (bistring of size instance_size or nb_constraints with the position of the cameras)</li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_10581012 |
| institution | Zenodo |
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| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Detecting and Reacting to Data-Drift in Streaming Optimisation Domains - Code and Data Anonymous <p>This repository contains the code and data for reproducibility of the paper 'Detecting and Reacting to Data-Drift in Streaming Optimisation Domains'. </p> <p>The following files are included:</p> <ul> <li>algo_data.zip : contains algorithm raw performance data;</li> <li>Art_gallery_features_500_samples_full.csv : ELA feature data on the Art Gallery problem;</li> <li>average_loss_accuracy_chunk.csv : results of the encoding switching when drift is detected;</li> <li>drift_detection_zenodo.ipynb : jupyter notebook with the code do to generate the incremental drift scenario and detect drift with NannyML (other scenearios can be generated using this base);</li> <li>plot_features.zip : additional plots not available in the paper;</li> <li>Objective_function.zip : singularity containers and binaries of the code used for the objective function.</li> </ul> <p>How to launch the objective functions:</p> <ul> <li>singularity run drift_num.sif instance_size nb_cameras nb_constraints x0 y0 x1 y1 ... (x,y being the coordinates of the cameras)</li> <li>singularity run drift_bit.sif instance_size nb_cameras nb_constraints bistring (bistring of size instance_size or nb_constraints with the position of the cameras)</li> </ul> |
| title | Detecting and Reacting to Data-Drift in Streaming Optimisation Domains - Code and Data |
| url | https://doi.org/10.5281/zenodo.10581012 |