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[DOC] Add example notebook for using aeon distances with sklearn clusterers #2511
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[DOC] Add example notebook for using aeon distances with sklearn clusterers #2511
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Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
Thank you for contributing to
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Dear maintainers, Clustering Overview |
hi, thanks for this, we will take a look |
Please refer to the links in my comment of the corresponding issue. |
- Removed "metric=..." details from TOC and introduction. - Renamed "Loading Data" to "Example Dataset." - Deleted redundant Introduction section.
"For a comprehensive overview of all available distance metrics in aeon, see the aeon distances API reference."
"AgglomerativeClustering is, as the name suggests, an agglomerative approach that works by merging clusters bottom-up." Clarified Supported Linkage Methods: Included the supported linkage methods (single, complete, average, weighted) for precomputed distance matrices.
…subtract from 1, ensuring proper preservation of distance distribution.
…istances.ipynb) in the Clustering Overview under Clustering Notebooks.
Thank you for the guidance! I have added a reference in the Please let me know if there are additional updates or adjustments required! |
this is failing the tests currently, please click on details to see why and resolve. |
Could you add a link to your new notebook from the Clustering-with-sklearn.cluster section of the |
…version consistency.
… and avoid confusion.
…cluster section of sklearn_distances.ipynb.
Inserted the required reference in the Clustering-with-sklearn.cluster section of sklearn_distances.ipynb. Please advise if any further refinements are necessary. |
I think, we also miss a link to this notebook from https://aeon-toolkit--2511.org.readthedocs.build/en/2511/examples.html#clustering |
Added the missing link to the new notebook in the Clustering section of examples.md. Please let me know if any further changes are needed! |
Hello, |
Hi @SalmanDeveloperz, |
…c' to fix CI Job issue.
…almanDeveloperz/aeon into add-sklearn-clustering-example
Hi @SebastianSchmidl, |
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Nice work 👍
The example plots (matplotlib) are not included in the notebook output, yet. I do not know if this should happen automatically or not.
@MatthewMiddlehurst Are the notebooks built and committed back by our nightly CI builds?
No this is not done automatically, but may be a good idea for those with no output. It would be good to run the notebook so the outputs are displayed here i agree. |
Hi, |
I see no changes since my comment. You should run the notebook so it has output visible on the website. Make sure you check the readthedocs output under checks. |
- Added outputs for Spectral and Hierarchical Clustering plots. - Fixed oversight per @MatthewMiddlehurst's feedback. - Refined code for more accurate clustering results.
…almanDeveloperz/aeon into add-sklearn-clustering-example
Dear @MatthewMiddlehurst, I sincerely apologize for the oversight in my previous updates. I have now resolved the issues by fixing the merge conflict and correcting the indentation in the notebook. All GitHub Actions checks have passed successfully, and the notebook outputs for both Spectral and Hierarchical Clustering are now visible as requested. I’ve also verified the readthedocs output under checks. Kindly review the changes, and please let me know if any further adjustments are needed. Thank you for your guidance and patience throughout this process :) |
Reference Issues/PRs
Fixes #1241
What does this implement/fix? Explain your changes.
This pull request introduces a new Jupyter Notebook: sklearn_clustering_with_aeon_distances.ipynb. The notebook demonstrates how to integrate aeon's distance metrics with scikit-learn clustering algorithms. It includes:
Hierarchical Clustering: Using AgglomerativeClustering with metric="precomputed".
Density-Based Clustering: Using DBSCAN and OPTICS with metric="precomputed".
Spectral Clustering: Using SpectralClustering with affinity="precomputed" and the inverse of the distance matrix as the similarity matrix.
This addition enhances the clustering documentation, showing how to combine aeon’s distance metrics with widely-used scikit-learn clusterers.
Does your contribution introduce a new dependency? If yes, which one?
No new dependencies introduced.
Any other comments?
The notebook has been tested locally, and all cells execute without errors.
A reference to this notebook has been added to the clustering section of the documentation.
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