Add SUDE dimension reduction method and update related documentation #729
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Pull Request Overview
This pull request adds SUDE (Scalable Unsupervised Deep Embedding) as a new dimension reduction method to the Dynamo package. The implementation includes a complete SUDE algorithm with supporting mathematical functions and integrates it into the existing dimension reduction pipeline.
- Added complete SUDE algorithm implementation with supporting functions for landmark selection, embedding computation, and large dataset handling
- Integrated SUDE into the main dimension reduction API and exposed it at the package level
- Added comprehensive test coverage for SUDE functionality including edge cases and parameter validation
Reviewed Changes
Copilot reviewed 15 out of 15 changed files in this pull request and generated 1 comment.
Show a summary per file
| File | Description |
|---|---|
| tests/test_sude.py | Comprehensive test suite covering SUDE functionality, parameter validation, and integration |
| dynamo/tools/utils_reduceDimension.py | Updated to include SUDE as a supported reduction method in the main pipeline |
| dynamo/tools/dimension_reduction.py | Added dedicated run_sude function and updated documentation to mention SUDE support |
| dynamo/tools/init.py | Exposed run_sude function at the package level |
| dynamo/external/sude_py/*.py | Complete SUDE algorithm implementation with supporting mathematical functions |
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Codecov Report❌ Patch coverage is Additional details and impacted files@@ Coverage Diff @@
## master #729 +/- ##
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+ Coverage 36.56% 37.27% +0.71%
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Files 183 195 +12
Lines 35352 35813 +461
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+ Hits 12925 13349 +424
- Misses 22427 22464 +37 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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1 participant
This pull request adds a new dimension reduction method, SUDE, to the codebase and integrates it into the existing pipeline for dimensionality reduction. The main changes include the addition of the full SUDE algorithm implementation and supporting functions, updating the API to expose SUDE, and modifying documentation to reflect the new option.
SUDE algorithm and supporting functions:
sude.py, including landmark selection, embedding computation, and handling of large datasets.init_pca(PCA initialization),pca(PCA embedding),mds(MDS embedding),pps(landmark sampling),learning_sandlearning_l(manifold learning for landmarks),opt_scale(optimal landmark scaling),clle(constrained locally linear embedding), andplotcluster2(embedding visualization).API and integration updates:
sudefunction at the package level by updating the__init__.pyfile to import it.run_sudeas an available dimension reduction method.reduceDimensionfunction to mention SUDE as a supported reduction method.